diff --git a/.ai/system-prompt.md b/.ai/system-prompt.md
index 95c266dae5..783309814b 100644
--- a/.ai/system-prompt.md
+++ b/.ai/system-prompt.md
@@ -21,7 +21,7 @@ As a W&B docs team member, you:
## Essential Guidelines
### Always:
-- Follow the [W&B docs style guide](.ai/style-guide.md)
+- Follow the [W&B docs style guide](./style-guide.md)
- Use context managers for `wandb.init()` in code examples
- Reference only public APIs documented in the [API reference](https://docs.wandb.ai/ref/python/)
- Test code examples when possible to ensure they work
diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
deleted file mode 100644
index 07d829aad1..0000000000
--- a/.github/workflows/build_docs.yml
+++ /dev/null
@@ -1,25 +0,0 @@
-name: Hugo build test
-
-on:
- pull_request:
- merge_group:
- workflow_call:
-
-defaults:
- run:
- shell: bash
-
-jobs:
- build:
- runs-on: ubuntu-latest
- steps:
- - uses: actions/checkout@v5
- - name: Setup environment
- run: |
- wget "https://github.com/gohugoio/hugo/releases/download/v$(grep 'HUGO_VERSION' wrangler.toml | awk -F '\"' {'print $2'})/hugo_extended_$(grep 'HUGO_VERSION' wrangler.toml | awk -F '\"' {'print $2'})_linux-amd64.deb" -O hugo.deb
- sudo apt install ./hugo.deb
- npm install
- go mod download
- hugo mod get -u
- - name: Run Hugo build
- run: hugo
diff --git a/.github/workflows/generate-reference-docs.yml b/.github/workflows/generate-reference-docs.yml
new file mode 100644
index 0000000000..98fcb0c8ee
--- /dev/null
+++ b/.github/workflows/generate-reference-docs.yml
@@ -0,0 +1,136 @@
+name: Generate Weave Reference Documentation
+
+on:
+ workflow_dispatch:
+ inputs:
+ weave_version:
+ description: 'Weave version (e.g., "latest", "0.51.34", "v0.51.34", commit SHA, or branch name)'
+ required: false
+ default: 'latest'
+ type: string
+
+jobs:
+ generate-docs:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+
+ - name: Set up Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: '3.11'
+
+ - name: Set up Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: '18'
+
+ - name: Cache Python dependencies
+ uses: actions/cache@v4
+ with:
+ path: ~/.cache/pip
+ key: ${{ runner.os }}-pip-${{ hashFiles('scripts/reference-generation/requirements.txt') }}
+ restore-keys: |
+ ${{ runner.os }}-pip-
+
+ - name: Install requests for Service API generation
+ run: |
+ pip install requests
+
+ - name: Set version variable
+ id: version
+ run: |
+ VERSION="${{ github.event.inputs.weave_version }}"
+ echo "version=${VERSION}" >> $GITHUB_OUTPUT
+ echo "Using Weave version: ${VERSION}"
+
+ # Generate timestamp
+ TIMESTAMP=$(date -u +"%Y-%m-%d %H:%M:%S UTC")
+ echo "timestamp=${TIMESTAMP}" >> $GITHUB_OUTPUT
+ echo "Generated at: ${TIMESTAMP}"
+
+ - name: Generate Service API Documentation
+ run: |
+ echo "Generating Service API documentation..."
+ python scripts/reference-generation/generate_service_api_spec.py
+
+ - name: Generate Python SDK Documentation
+ run: |
+ echo "Generating Python SDK documentation..."
+ VERSION="${{ steps.version.outputs.version }}"
+ echo "Using Weave version: ${VERSION}"
+
+ # Create isolated environment for Python SDK generation
+ python -m venv venv_python
+ source venv_python/bin/activate
+ pip install requests lazydocs
+ python scripts/reference-generation/generate_python_sdk_docs.py "${VERSION:-latest}"
+ deactivate
+ rm -rf venv_python
+
+ - name: Generate TypeScript SDK Documentation
+ run: |
+ echo "Generating TypeScript SDK documentation..."
+ VERSION="${{ steps.version.outputs.version }}"
+ echo "Using Weave version: ${VERSION}"
+
+ # Create isolated environment for TypeScript SDK generation
+ python -m venv venv_typescript
+ source venv_typescript/bin/activate
+ python scripts/reference-generation/generate_typescript_sdk_docs.py "${VERSION:-latest}"
+ deactivate
+ rm -rf venv_typescript
+
+ - name: Fix file casing
+ run: |
+ echo "Fixing file casing for SDK documentation..."
+ python scripts/reference-generation/fix_casing.py
+
+ - name: Fix broken links
+ run: |
+ echo "Fixing broken links in documentation..."
+ python scripts/reference-generation/fix_broken_links.py
+
+ - name: Create Pull Request
+ id: create-pr
+ uses: peter-evans/create-pull-request@v6
+ with:
+ token: ${{ secrets.GITHUB_TOKEN }}
+ commit-message: "chore: Update reference documentation (Weave ${{ steps.version.outputs.version }})"
+ title: "Update Weave reference documentation (Weave ${{ steps.version.outputs.version }})"
+ draft: true
+ body: |
+ This PR updates the reference documentation for Weave.
+
+ **Version**: ${{ steps.version.outputs.version }}
+ **Generated on**: ${{ steps.version.outputs.timestamp }}
+
+ ### Changes
+ - Updated Service API documentation from OpenAPI spec
+ - Regenerated Python SDK documentation using lazydocs
+ - Regenerated TypeScript SDK documentation using typedoc
+ - Fixed internal cross-reference links
+
+ ### Notes
+ - Documentation generation runs in isolated environments
+ - All dependencies are development-only and not included in the repository
+ - Please review the changes before merging
+ branch: update-reference-docs-${{ github.run_number }}
+ delete-branch: true
+ labels: |
+ documentation
+ automated
+ weave
+
+ - name: Display PR Link
+ if: steps.create-pr.outputs.pull-request-number
+ run: |
+ PR_URL="https://github.com/${{ github.repository }}/pull/${{ steps.create-pr.outputs.pull-request-number }}"
+ echo "📝 Draft PR created successfully!"
+ echo "🔗 Review the changes at: $PR_URL"
+ echo ""
+ echo "::notice title=Pull Request Created::A draft PR has been created for review: $PR_URL"
diff --git a/.github/workflows/mintlify-deployment-preview.yml b/.github/workflows/mintlify-deployment-preview.yml
new file mode 100644
index 0000000000..bb16376fa0
--- /dev/null
+++ b/.github/workflows/mintlify-deployment-preview.yml
@@ -0,0 +1,483 @@
+name: Mintlify Deployment Preview Links
+
+on:
+ deployment_status:
+
+permissions:
+ contents: read
+ pull-requests: write
+ issues: write
+ deployments: read
+
+jobs:
+ update-preview-links:
+ # Only run when Mintlify deployment succeeds
+ if: |
+ github.event.deployment_status.state == 'success' &&
+ github.event.deployment.environment == 'staging' &&
+ contains(github.event.deployment_status.creator.login, 'mintlify')
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v5
+ with:
+ ref: ${{ github.event.deployment.ref }}
+
+ - name: Get PR number from deployment ref
+ id: get-pr
+ uses: actions/github-script@v8
+ with:
+ script: |
+ // The deployment ref is the branch name
+ const branch = context.payload.deployment.ref;
+ const deploymentSha = context.payload.deployment.sha;
+
+ // Find PRs for this branch
+ const prs = await github.rest.pulls.list({
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ head: `${context.repo.owner}:${branch}`,
+ state: 'open'
+ });
+
+ if (prs.data.length > 0) {
+ const pr = prs.data[0];
+
+ // Check if this deployment is for the latest commit in the PR
+ const prHeadSha = pr.head.sha;
+ if (deploymentSha !== prHeadSha) {
+ core.warning(`Deployment SHA (${deploymentSha}) doesn't match PR head SHA (${prHeadSha})`);
+ core.warning(`This appears to be a deployment for an older commit. Skipping update.`);
+ core.setOutput('skip_update', 'true');
+ return null;
+ }
+
+ core.setOutput('pr_number', pr.number);
+ core.setOutput('base_ref', pr.base.ref);
+ core.setOutput('base_sha', pr.base.sha);
+ core.setOutput('skip_update', 'false');
+ core.info(`Found PR #${pr.number} for branch ${branch}`);
+ core.info(`Base branch: ${pr.base.ref} (${pr.base.sha})`);
+ core.info(`Deployment SHA matches PR head: ${deploymentSha}`);
+ return pr.number;
+ } else {
+ core.warning(`No open PR found for branch ${branch}`);
+ core.setOutput('skip_update', 'true');
+ return null;
+ }
+
+ - name: Get all PR changed files
+ if: steps.get-pr.outputs.pr_number && steps.get-pr.outputs.skip_update != 'true'
+ id: changed
+ uses: actions/github-script@v8
+ with:
+ script: |
+ const prNumber = parseInt('${{ steps.get-pr.outputs.pr_number }}');
+
+ // Get all files changed in the PR
+ const files = await github.paginate(github.rest.pulls.listFiles, {
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ pull_number: prNumber,
+ per_page: 100
+ });
+
+ // Filter for relevant file types
+ const relevantFiles = files.filter(file => {
+ const filename = file.filename;
+ return filename.endsWith('.mdx') ||
+ filename.endsWith('.md') ||
+ filename === 'docs.json' ||
+ filename === 'mint.json' ||
+ filename.startsWith('snippets/') ||
+ filename.startsWith('images/') ||
+ filename.startsWith('icons/') ||
+ filename.startsWith('logo/') ||
+ filename.startsWith('static/') ||
+ filename === 'style.css';
+ });
+
+ // Group files by status - include previous_filename for renamed files
+ const added = relevantFiles.filter(f => f.status === 'added').map(f => f.filename);
+ const modified = relevantFiles.filter(f => f.status === 'modified').map(f => f.filename);
+ const removed = relevantFiles.filter(f => f.status === 'removed').map(f => f.filename);
+ const renamed = relevantFiles.filter(f => f.status === 'renamed').map(f => ({
+ old: f.previous_filename,
+ new: f.filename
+ }));
+
+ // Store all categorized files for the comment builder
+ core.setOutput('added_files', JSON.stringify(added));
+ core.setOutput('modified_files', JSON.stringify(modified));
+ core.setOutput('removed_files', JSON.stringify(removed));
+ core.setOutput('renamed_files', JSON.stringify(renamed));
+ core.setOutput('any_changed', relevantFiles.length > 0 ? 'true' : 'false');
+
+ core.info(`Found ${relevantFiles.length} changed files in PR #${prNumber}`);
+ core.info(`Added: ${added.length}, Modified: ${modified.length}, Renamed: ${renamed.length}, Removed: ${removed.length}`);
+
+ - name: Update PR comment with preview links
+ if: steps.get-pr.outputs.pr_number && steps.get-pr.outputs.skip_update != 'true' && steps.changed.outputs.any_changed == 'true'
+ uses: actions/github-script@v8
+ env:
+ PR_NUMBER: ${{ steps.get-pr.outputs.pr_number }}
+ PREVIEW_URL: ${{ github.event.deployment_status.environment_url }}
+ ADDED_FILES: ${{ steps.changed.outputs.added_files }}
+ MODIFIED_FILES: ${{ steps.changed.outputs.modified_files }}
+ REMOVED_FILES: ${{ steps.changed.outputs.removed_files }}
+ RENAMED_FILES: ${{ steps.changed.outputs.renamed_files }}
+ with:
+ script: |
+ const fs = require('fs');
+ const path = require('path');
+
+ const prNumber = parseInt(process.env.PR_NUMBER);
+ const previewUrl = process.env.PREVIEW_URL;
+
+ // Parse the categorized files
+ const added = JSON.parse(process.env.ADDED_FILES || '[]');
+ const modified = JSON.parse(process.env.MODIFIED_FILES || '[]');
+ const removed = JSON.parse(process.env.REMOVED_FILES || '[]');
+ const renamed = JSON.parse(process.env.RENAMED_FILES || '[]');
+
+ if (!previewUrl) {
+ core.warning('No preview URL found in deployment status');
+ return;
+ }
+
+ const totalFiles = added.length + modified.length + removed.length + renamed.length;
+ core.info(`Mintlify Preview URL: ${previewUrl}`);
+ core.info(`Total changed files: ${totalFiles}`);
+ core.info(`Added: ${added.length}, Modified: ${modified.length}, Renamed: ${renamed.length}, Removed: ${removed.length}`);
+
+ // Helper function to detect if a file is an image
+ function isImageFile(filepath) {
+ const imageExtensions = ['.png', '.jpg', '.jpeg', '.gif', '.svg', '.webp', '.ico', '.bmp'];
+ const ext = path.extname(filepath).toLowerCase();
+ if (imageExtensions.includes(ext)) return true;
+
+ // Also check if it's in an image-related directory
+ return filepath.startsWith('images/') ||
+ filepath.startsWith('icons/') ||
+ filepath.startsWith('logo/') ||
+ filepath.startsWith('static/');
+ }
+
+ // Function to get title from file path
+ function titleFromPath(p) {
+ let base = path.basename(p, path.extname(p));
+
+ // Handle special icon naming pattern
+ if (base.includes('Name=') || base.includes('Mode=')) {
+ const nameMatch = base.match(/Name=([^,\s]+)/);
+ const modeMatch = base.match(/Mode=([^,\s]+)/);
+
+ if (nameMatch || modeMatch) {
+ const parts = [];
+ if (nameMatch) parts.push(nameMatch[1]);
+ if (modeMatch) parts.push(modeMatch[1]);
+ base = parts.join(' ');
+ }
+ }
+
+ if (base === 'index') {
+ const dir = path.dirname(p);
+ const parent = path.basename(dir);
+ return parent.replace(/[-_]+/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
+ }
+ return base.replace(/[-_]+/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
+ }
+
+ // Function to get URL from file path
+ function urlFromPath(p) {
+ // Remove file extension
+ let urlPath = p.replace(/\.(mdx?|md)$/, '');
+
+ // Remove 'index' from the end if present
+ if (urlPath.endsWith('/index')) {
+ urlPath = urlPath.slice(0, -6);
+ }
+
+ return `${previewUrl}/${urlPath}`;
+ }
+
+ // Helper function to create file table rows
+ function createFileTable(files, maxShow = 10, showPreview = true, isImage = false) {
+ let table = showPreview ?
+ `| File | Preview |\n|------|----------|\n` :
+ `| File |\n|------|\n`;
+
+ const toShow = files.slice(0, maxShow);
+ toShow.forEach(file => {
+ if (showPreview) {
+ if (file.endsWith('.mdx') || file.endsWith('.md')) {
+ const title = titleFromPath(file);
+ const url = urlFromPath(file);
+ table += `| \`${file}\` | [${title}](${url}) |\n`;
+ } else if (isImage) {
+ // Images can also be previewed directly
+ const title = titleFromPath(file);
+ const url = `${previewUrl}/${file}`;
+ table += `| \`${file}\` | [View ${title}](${url}) |\n`;
+ } else {
+ table += `| \`${file}\` | - |\n`;
+ }
+ } else {
+ table += `| \`${file}\` |\n`;
+ }
+ });
+
+ if (files.length > maxShow) {
+ table += showPreview ?
+ `| *... and ${files.length - maxShow} more files* | |\n` :
+ `| *... and ${files.length - maxShow} more files* |\n`;
+ }
+
+ return table;
+ }
+
+ // Build comment body
+ let body = `\n`;
+ body += `## 📚 Mintlify Preview Links\n\n`;
+ body += `🔗 **[View Full Preview](${previewUrl})**\n\n`;
+
+ const maxPerSubcategory = 10;
+ let hasChanges = false;
+
+ // Renamed/Moved files
+ if (renamed.length > 0) {
+ hasChanges = true;
+ body += `### 🔄 Renamed/Moved (${renamed.length} total)\n\n`;
+
+ const pagesRenamed = renamed.filter(r => r.new.endsWith('.mdx') || r.new.endsWith('.md'));
+ const imagesRenamed = renamed.filter(r => !pagesRenamed.some(p => p.new === r.new) && isImageFile(r.new));
+ const otherRenamed = renamed.filter(r => !pagesRenamed.some(p => p.new === r.new) && !imagesRenamed.some(i => i.new === r.new));
+
+ // Pages (with preview links)
+ if (pagesRenamed.length > 0) {
+ body += `#### 📄 Pages (${pagesRenamed.length})\n\n`;
+ body += `| Old Path → New Path | Preview |\n|---------------------|----------|\n`;
+ const toShow = pagesRenamed.slice(0, maxPerSubcategory);
+ toShow.forEach(({old: oldPath, new: newPath}) => {
+ const title = titleFromPath(newPath);
+ const url = urlFromPath(newPath);
+ body += `| \`${oldPath}\` → \`${newPath}\` | [${title}](${url}) |\n`;
+ });
+ if (pagesRenamed.length > maxPerSubcategory) {
+ body += `| *... and ${pagesRenamed.length - maxPerSubcategory} more pages* | |\n`;
+ }
+ body += `\n`;
+ }
+
+ // Images (with preview links)
+ if (imagesRenamed.length > 0) {
+ if (imagesRenamed.length > 5) {
+ body += `\n🖼️ Images (${imagesRenamed.length})\n\n`;
+ } else {
+ body += `#### 🖼️ Images (${imagesRenamed.length})\n\n`;
+ }
+ body += `| Old Path → New Path | Preview |\n|---------------------|----------|\n`;
+ const toShow = imagesRenamed.slice(0, maxPerSubcategory);
+ toShow.forEach(({old: oldPath, new: newPath}) => {
+ const title = titleFromPath(newPath);
+ const url = `${previewUrl}/${newPath}`;
+ body += `| \`${oldPath}\` → \`${newPath}\` | [View ${title}](${url}) |\n`;
+ });
+ if (imagesRenamed.length > maxPerSubcategory) {
+ body += `| *... and ${imagesRenamed.length - maxPerSubcategory} more images* | |\n`;
+ }
+ if (imagesRenamed.length > 5) {
+ body += `\n\n`;
+ }
+ body += `\n`;
+ }
+
+ // Other files (no preview)
+ if (otherRenamed.length > 0) {
+ body += `\n⚙️ Other (${otherRenamed.length})\n\n`;
+ body += `| Old Path → New Path |\n|---------------------|\n`;
+ const toShow = otherRenamed.slice(0, maxPerSubcategory);
+ toShow.forEach(({old: oldPath, new: newPath}) => {
+ body += `| \`${oldPath}\` → \`${newPath}\` |\n`;
+ });
+ if (otherRenamed.length > maxPerSubcategory) {
+ body += `| *... and ${otherRenamed.length - maxPerSubcategory} more files* |\n`;
+ }
+ body += `\n\n\n`;
+ }
+ }
+
+ // Added files
+ if (added.length > 0) {
+ hasChanges = true;
+ body += `### ✨ Added (${added.length} total)\n\n`;
+
+ const pagesAdded = added.filter(f => f.endsWith('.mdx') || f.endsWith('.md'));
+ const imagesAdded = added.filter(f => !pagesAdded.includes(f) && isImageFile(f));
+ const otherAdded = added.filter(f => !pagesAdded.includes(f) && !imagesAdded.includes(f));
+
+ // Pages (with preview links)
+ if (pagesAdded.length > 0) {
+ body += `#### 📄 Pages (${pagesAdded.length})\n\n`;
+ body += createFileTable(pagesAdded, maxPerSubcategory, true, false);
+ body += `\n`;
+ }
+
+ // Images (with preview links)
+ if (imagesAdded.length > 0) {
+ if (imagesAdded.length > 5) {
+ body += `\n🖼️ Images (${imagesAdded.length})\n\n`;
+ } else {
+ body += `#### 🖼️ Images (${imagesAdded.length})\n\n`;
+ }
+ body += createFileTable(imagesAdded, maxPerSubcategory, true, true);
+ if (imagesAdded.length > 5) {
+ body += `\n`;
+ }
+ body += `\n`;
+ }
+
+ // Other files (no preview)
+ if (otherAdded.length > 0) {
+ body += `\n⚙️ Other (${otherAdded.length})\n\n`;
+ body += createFileTable(otherAdded, maxPerSubcategory, false);
+ body += `\n\n\n`;
+ }
+ }
+
+ // Changed files (Modified)
+ if (modified.length > 0) {
+ hasChanges = true;
+ body += `### 📝 Changed (${modified.length} total)\n\n`;
+
+ const pagesModified = modified.filter(f => f.endsWith('.mdx') || f.endsWith('.md'));
+ const imagesModified = modified.filter(f => !pagesModified.includes(f) && isImageFile(f));
+ const otherModified = modified.filter(f => !pagesModified.includes(f) && !imagesModified.includes(f));
+
+ // Pages (with preview links)
+ if (pagesModified.length > 0) {
+ body += `#### 📄 Pages (${pagesModified.length})\n\n`;
+ body += createFileTable(pagesModified, maxPerSubcategory, true, false);
+ body += `\n`;
+ }
+
+ // Images (with preview links)
+ if (imagesModified.length > 0) {
+ if (imagesModified.length > 5) {
+ body += `\n🖼️ Images (${imagesModified.length})\n\n`;
+ } else {
+ body += `#### 🖼️ Images (${imagesModified.length})\n\n`;
+ }
+ body += createFileTable(imagesModified, maxPerSubcategory, true, true);
+ if (imagesModified.length > 5) {
+ body += `\n`;
+ }
+ body += `\n`;
+ }
+
+ // Other files (no preview)
+ if (otherModified.length > 0) {
+ body += `\n⚙️ Other (${otherModified.length})\n\n`;
+ body += createFileTable(otherModified, maxPerSubcategory, false);
+ body += `\n\n\n`;
+ }
+ }
+
+ // Deleted files (no preview links)
+ if (removed.length > 0) {
+ hasChanges = true;
+ body += `### 🗑️ Deleted (${removed.length} total)\n\n`;
+
+ const pagesRemoved = removed.filter(f => f.endsWith('.mdx') || f.endsWith('.md'));
+ const imagesRemoved = removed.filter(f => !pagesRemoved.includes(f) && isImageFile(f));
+ const otherRemoved = removed.filter(f => !pagesRemoved.includes(f) && !imagesRemoved.includes(f));
+
+ body += `\nView deleted files\n\n`;
+
+ // Pages (no preview)
+ if (pagesRemoved.length > 0) {
+ body += `**📄 Pages (${pagesRemoved.length})**\n\n`;
+ body += `| File |\n|------|\n`;
+ const toShow = pagesRemoved.slice(0, maxPerSubcategory);
+ toShow.forEach(file => {
+ body += `| \`${file}\` |\n`;
+ });
+ if (pagesRemoved.length > maxPerSubcategory) {
+ body += `| *... and ${pagesRemoved.length - maxPerSubcategory} more pages* |\n`;
+ }
+ body += `\n`;
+ }
+
+ // Images (no preview)
+ if (imagesRemoved.length > 0) {
+ body += `**🖼️ Images (${imagesRemoved.length})**\n\n`;
+ body += `| File |\n|------|\n`;
+ const toShow = imagesRemoved.slice(0, maxPerSubcategory);
+ toShow.forEach(file => {
+ body += `| \`${file}\` |\n`;
+ });
+ if (imagesRemoved.length > maxPerSubcategory) {
+ body += `| *... and ${imagesRemoved.length - maxPerSubcategory} more images* |\n`;
+ }
+ body += `\n`;
+ }
+
+ // Other files (no preview)
+ if (otherRemoved.length > 0) {
+ body += `**⚙️ Other (${otherRemoved.length})**\n\n`;
+ body += `| File |\n|------|\n`;
+ const toShow = otherRemoved.slice(0, maxPerSubcategory);
+ toShow.forEach(file => {
+ body += `| \`${file}\` |\n`;
+ });
+ if (otherRemoved.length > maxPerSubcategory) {
+ body += `| *... and ${otherRemoved.length - maxPerSubcategory} more files* |\n`;
+ }
+ body += `\n`;
+ }
+
+ body += `\n\n`;
+ }
+
+ if (!hasChanges) {
+ body += `*No documentation changes detected in this PR.*\n\n`;
+ }
+
+ // Add deployment info and timestamp
+ const deploymentSha = context.payload.deployment.sha.substring(0, 7);
+ const timestamp = new Date().toISOString().replace('T', ' ').split('.')[0] + ' UTC';
+
+ body += `\n---\n`;
+ body += `🤖 *Generated automatically when Mintlify deployment succeeds*\n`;
+ body += `📍 *Deployment: \`${deploymentSha}\` at ${timestamp}*`;
+
+ // Find existing comment
+ const comments = await github.paginate(github.rest.issues.listComments, {
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ issue_number: prNumber,
+ per_page: 100,
+ });
+
+ let existingComment = comments.find(c =>
+ c.body && c.body.includes('')
+ );
+
+ // Create or update comment
+ if (existingComment) {
+ await github.rest.issues.updateComment({
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ comment_id: existingComment.id,
+ body
+ });
+ core.info(`Updated existing comment #${existingComment.id}`);
+ } else {
+ const newComment = await github.rest.issues.createComment({
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ issue_number: prNumber,
+ body
+ });
+ core.info(`Created new comment #${newComment.data.id}`);
+ }
diff --git a/.github/workflows/pr-preview-links-on-comment.yml b/.github/workflows/pr-preview-links-on-comment.yml
deleted file mode 100644
index 1d2dc5e973..0000000000
--- a/.github/workflows/pr-preview-links-on-comment.yml
+++ /dev/null
@@ -1,545 +0,0 @@
-name: PR Preview Links - Update on Cloudflare Comment
-
-on:
- issue_comment:
- types: [created, edited]
-
-permissions:
- contents: read
- pull-requests: write
- issues: write
-
-jobs:
- update-preview-links:
- # Only run on PR comments from Cloudflare bot
- if: |
- github.event.issue.pull_request &&
- contains(github.event.comment.user.login, 'cloudflare') &&
- contains(github.event.comment.body, 'Branch Preview URL')
-
- runs-on: ubuntu-latest
-
- steps:
- - name: Get PR details
- id: pr
- uses: actions/github-script@v8
- with:
- script: |
- const pr = await github.rest.pulls.get({
- owner: context.repo.owner,
- repo: context.repo.repo,
- pull_number: context.issue.number
- });
- core.setOutput('head_sha', pr.data.head.sha);
- core.setOutput('base_sha', pr.data.base.sha);
-
- - name: Checkout
- uses: actions/checkout@v5
- with:
- fetch-depth: 0
- ref: ${{ steps.pr.outputs.head_sha }}
-
- - name: Setup Node.js
- uses: actions/setup-node@v6
- with:
- node-version-file: .node-version
-
- - name: Get Hugo version and setup
- run: |
- set -euo pipefail
- ver=$(grep 'HUGO_VERSION' wrangler.toml | awk -F '"' '{print $2}')
- wget "https://github.com/gohugoio/hugo/releases/download/v${ver}/hugo_extended_${ver}_linux-amd64.deb" -O hugo.deb
- sudo apt-get update
- sudo apt-get install -y ./hugo.deb
-
- - name: Setup Go
- uses: actions/setup-go@v6
- with:
- go-version-file: go.mod
- check-latest: true
-
- - name: Setup deps for build
- run: |
- set -euo pipefail
- npm install
- go mod download
- hugo mod get -u
-
- - name: Get changed files
- id: changed
- uses: tj-actions/changed-files@v47
- with:
- base_sha: ${{ steps.pr.outputs.base_sha }}
- sha: ${{ steps.pr.outputs.head_sha }}
- files: |
- content/**
- static/**
- assets/**
- layouts/**
- i18n/**
- configs/**
- include_all_old_new_renamed_files: true
-
- - name: Build Hugo (generate pageurls)
- if: steps.changed.outputs.any_changed == 'true'
- run: |
- hugo --minify
- test -f public/pageurls.json && echo "Found pageurls.json" || (echo "Missing pageurls.json" && ls -la public || true)
-
-
- - name: Extract Branch Preview URL
- id: extract-url
- uses: actions/github-script@v8
- with:
- script: |
- function sanitizeUrl(u) {
- if (!u) return '';
- u = u.trim().replace(/^['"<]+/, '').replace(/[>'"]+$/, '');
- u = u.split(/[\s<>'"\]\)]/)[0];
- try { u = decodeURI(u); } catch (e) {}
- return u.replace(/\/$/, '');
- }
-
- function extractBranchPreviewUrl(body) {
- const patterns = [
- // Table format with anchor tag (what Cloudflare actually uses)
- /Branch\s+Preview\s+URL:<\/strong><\/td>
[\s\S]*?]+href=['"]([^'"<>]+pages\.dev[^'"<>]*)['"]/i,
- // Try anchor tag format on same line
- /Branch\s+Preview\s+URL:\s*]+href=['"]([^'"<>]+pages\.dev[^'"<>]*)['"]/i,
- // Try plain URL format
- /Branch\s+Preview\s+URL:\s*]+pages\.dev[^\s<>]*)/i
- ];
-
- for (const pattern of patterns) {
- const m = (body || '').match(pattern);
- if (m && m[1]) {
- const url = m[1];
- if (!url.startsWith('http')) {
- return sanitizeUrl('https://' + url);
- }
- return sanitizeUrl(url);
- }
- }
- return '';
- }
-
- const branchUrl = extractBranchPreviewUrl(context.payload.comment.body);
- core.info(`Extracted Branch Preview URL: ${branchUrl}`);
- core.setOutput('branch_url', branchUrl);
-
- - name: Update PR comment with preview links
- if: steps.changed.outputs.any_changed == 'true' && steps.extract-url.outputs.branch_url
- uses: actions/github-script@v8
- env:
- ADDED: ${{ steps.changed.outputs.added_files }}
- MODIFIED: ${{ steps.changed.outputs.modified_files }}
- DELETED: ${{ steps.changed.outputs.deleted_files }}
- RENAMED: ${{ steps.changed.outputs.renamed_files }}
- RENAMED_PAIRS: ${{ steps.changed.outputs.all_old_new_renamed_files }}
- PREVIEW_BASE: ${{ steps.extract-url.outputs.branch_url }}
- with:
- script: |
- const fs = require('fs');
- const path = require('path');
-
- function splitList(s) {
- if (!s) return [];
- return s
- .split(/\r?\n|,|\s+/)
- .map(x => x.trim())
- .filter(Boolean);
- }
-
- const added = splitList(process.env.ADDED);
- const modified = splitList(process.env.MODIFIED);
- const deleted = splitList(process.env.DELETED);
- const renamed = splitList(process.env.RENAMED);
- const renamedPairs = splitList(process.env.RENAMED_PAIRS);
-
- // Process renamed files - tj-actions outputs them as "old_path new_path" pairs
- const renamedFiles = [];
- const renamedMapping = new Map(); // old path -> new path
- for (let i = 0; i < renamedPairs.length; i += 2) {
- if (i + 1 < renamedPairs.length) {
- const oldPath = renamedPairs[i];
- const newPath = renamedPairs[i + 1];
- renamedMapping.set(oldPath, newPath);
- renamedFiles.push({ old: oldPath, new: newPath });
- }
- }
-
- // Filter out renamed files from added and deleted arrays to avoid duplication
- const addedFiltered = added.filter(f => !Array.from(renamedMapping.values()).includes(f));
- const deletedFiltered = deleted.filter(f => !Array.from(renamedMapping.keys()).includes(f));
-
- const allChanged = [...addedFiltered, ...modified, ...deletedFiltered, ...renamed];
- const anyEligible = allChanged.some(p => /^(content|static|assets)\//.test(p));
-
- // Handle case where there are no content changes
- if (!anyEligible) {
- core.info('No relevant content changes. Updating comment to reflect this.');
-
- const previewBase = (process.env.PREVIEW_BASE || '').replace(/\/$/, '');
- const header = '\n\n**PR Preview: Changed content**';
- const body = header + '\n\nNo documentation content changes in this PR.';
-
- // Find and update our existing comment
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
-
- const existing = comments.find(c => //.test(c.body || ''));
- if (existing) {
- await github.rest.issues.updateComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- comment_id: existing.id,
- body,
- });
- core.info('Updated existing preview links comment to show no content changes');
- }
-
- return;
- }
-
- let pageMap = [];
- // Build candidate pageurls.json paths by scanning public/
- const candidatePaths = new Set();
- try {
- if (fs.existsSync('public/pageurls.json')) candidatePaths.add('public/pageurls.json');
- } catch (e) {
- core.warning(`Root pageurls existence check failed: ${e.message}`);
- }
- try {
- if (fs.existsSync('public')) {
- const entries = fs.readdirSync('public', { withFileTypes: true });
- for (const ent of entries) {
- if (ent.isDirectory()) {
- candidatePaths.add(path.posix.join('public', ent.name, 'pageurls.json'));
- candidatePaths.add(path.posix.join('public', ent.name, 'index.pageurls.json'));
- }
- }
- }
- } catch (e) {
- core.warning(`Could not scan public/ for languages: ${e.message}`);
- }
-
- core.info(`Attempting to load page maps from: ${Array.from(candidatePaths).join(', ') || '(none)'}`);
- for (const pageurlsPath of candidatePaths) {
- try {
- if (!fs.existsSync(pageurlsPath)) continue;
- const raw = fs.readFileSync(pageurlsPath, 'utf8');
- const langPages = JSON.parse(raw);
- pageMap = pageMap.concat(langPages);
- core.info(`Loaded ${langPages.length} pages from ${pageurlsPath}`);
-
- } catch (e) {
- core.warning(`Could not read ${pageurlsPath}. ${e.message}`);
- }
- }
-
- // Build lookup: support keys with and without language prefix
- const byPath = new Map();
- for (const p of pageMap) {
- const lang = p.lang || 'en'; // Default to 'en' if no lang specified
- const rel = (p.path || '').replace(/^\/+/, '').replace(/\\/g, '/');
- const withLang = lang && !rel.startsWith(lang + '/') ? `${lang}/${rel}` : rel;
- const withoutLang = lang && rel.startsWith(lang + '/') ? rel.slice(lang.length + 1) : rel;
- const keys = new Set([
- rel,
- withLang,
- withoutLang,
- path.posix.join('content', withLang),
- path.posix.join('content', withoutLang),
- path.posix.join('content', rel),
- // Add explicit handling for language in path
- path.posix.join('content', lang, rel),
- path.posix.join('content', lang, withoutLang),
- // Add .md extension variations for content files
- rel.endsWith('.md') ? rel : rel + '.md',
- withLang.endsWith('.md') ? withLang : withLang + '.md',
- withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md',
- path.posix.join('content', lang, rel.endsWith('.md') ? rel : rel + '.md'),
- path.posix.join('content', lang, withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md'),
- path.posix.join('content', withLang.endsWith('.md') ? withLang : withLang + '.md'),
- path.posix.join('content', withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md'),
- // Special handling for index files
- path.posix.join('content', lang, '_index.md'),
- path.posix.join('content', lang, 'index.md'),
- ].filter(Boolean).map(k => k.replace(/\\/g, '/')));
-
- // Debug log for new pages
- if (p.path && p.path.includes('dspy')) {
- core.info(`Debug: Page ${p.path} (lang: ${lang}) has keys: ${Array.from(keys).join(', ')}`);
- }
-
- for (const k of keys) byPath.set(k, p);
- }
-
-
- const previewBase = (process.env.PREVIEW_BASE || '').replace(/\/$/, '');
- core.info(`Using Branch Preview URL: ${previewBase}`);
-
- function mapEntry(filePath) {
- const norm = filePath.replace(/\\/g, '/');
-
- // Try multiple variations to find the item
- let item = byPath.get(norm);
- if (!item) item = byPath.get(norm.replace(/^content\//, ''));
- if (!item) item = byPath.get(norm.replace(/\.md$/, ''));
- if (!item) item = byPath.get(norm.replace(/^content\//, '').replace(/\.md$/, ''));
-
- // Debug log for unmatched files
- if (!item) {
- core.info(`Debug: Could not find mapping for ${norm}`);
- // For new files, construct the URL based on the file path
- if (norm.startsWith('content/') && norm.endsWith('.md')) {
- // Extract language and path
- const pathParts = norm.replace(/^content\//, '').replace(/\.md$/, '').split('/');
- let lang = 'en';
- let urlPath = pathParts.join('/');
-
- // Check if first part is a language code
- if (['en', 'ja', 'ko'].includes(pathParts[0])) {
- lang = pathParts[0];
- urlPath = pathParts.slice(1).join('/');
- }
-
- // Handle _index.md files
- if (urlPath.endsWith('/_index') || urlPath === '_index') {
- urlPath = urlPath.replace(/\/_index$/, '').replace(/^_index$/, '');
- }
-
- // Construct the URL
- const rel = lang === 'en' ? `/${urlPath}/` : `/${lang}/${urlPath}/`;
- const href = previewBase ? (previewBase.replace(/\/$/, '') + rel) : '';
-
- core.info(`Debug: Constructed URL for new file ${norm}: ${href}`);
- return { title: titleFromPath(norm), rel, href, path: norm };
- }
- }
-
- if (!item) return { title: titleFromPath(norm), rel: '', href: '', path: norm };
- const rel = item.relPermalink || '';
- const href = previewBase ? (previewBase.replace(/\/$/, '') + rel) : '';
- const title = item.title || titleFromPath(norm);
- return { title, rel, href, path: norm };
- }
-
- function titleFromPath(p) {
- const stem = p.replace(/^content\//, '').replace(/\.(md|markdown)$/, '');
- const parts = stem.split('/');
- const last = parts[parts.length - 1];
- const base = last === 'index' ? (parts[parts.length - 2] || 'index') : last;
- return base.replace(/[\-_]+/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
- }
-
- // Function to check if a file is an include
- function isIncludeFile(filePath) {
- return /^content\/[^\/]+\/_includes\//.test(filePath);
- }
-
- // Function to find pages that use a specific include
- function findPagesUsingInclude(includePath) {
- const includeFileName = includePath.split('/').pop();
- const pagesUsingInclude = [];
-
- // Search through all pages in pageMap
- for (const page of pageMap) {
- if (page.path && page.path.endsWith('.md')) {
- try {
- // Construct the correct file path
- // page.path might already include content/ prefix, so handle both cases
- let contentPath = page.path;
- if (!contentPath.startsWith('content/')) {
- contentPath = path.join('content', page.lang || 'en', page.path);
- }
- if (fs.existsSync(contentPath)) {
- const content = fs.readFileSync(contentPath, 'utf8');
-
- // Check for usage of the include file with various path formats
- // The include might be referenced as:
- // - /_includes/filename.md
- // - /content/en/_includes/filename.md
- // - /content//_includes/filename.md
- const escapedFileName = includeFileName.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
-
- // Pattern to match any path ending with /_includes/filename
- const includePattern = `[^"']*\\/_includes\\/${escapedFileName}`;
-
- // Check for both readfile syntaxes with flexible path matching
- const pattern1 = new RegExp(`\\{\\{%\\s*readfile\\s+(?:file=)?["'](${includePattern})["']\\s*%\\}\\}`, 'i');
- const pattern2 = new RegExp(`\\{\\{<\\s*readfile\\s+file=["'](${includePattern})["']\\s*>\\}\\}`, 'i');
-
- if (pattern1.test(content) || pattern2.test(content)) {
- pagesUsingInclude.push(page);
- }
- }
- } catch (e) {
- // Skip if file can't be read
- }
- }
- }
-
- return pagesUsingInclude;
- }
-
- function buildRows(files, isDeleted = false) {
- const rows = [];
-
- for (const fp of files) {
- if (fp.startsWith('content/')) {
- // Check if this is an include file
- if (isIncludeFile(fp)) {
- // Use just the filename for includes
- const filename = fp.split('/').pop();
-
- // Find pages that use this include
- const dependentPages = findPagesUsingInclude(fp);
-
- // Build the title cell with filename and dependent pages
- let titleCell = `\`${filename}\``;
- if (dependentPages.length > 0) {
- // Add dependent pages as a nested list
- const pageLinks = dependentPages.map(page => {
- const href = previewBase ? (previewBase + page.relPermalink) : '';
- const linkText = page.title || titleFromPath(page.path);
- return href ? `[${linkText}](${href})` : linkText;
- });
-
- const dependentList = pageLinks.map(link => ` ↳ ${link}`).join('');
- titleCell += `:${dependentList}`;
- }
-
- rows.push(`| ${titleCell} | \`${fp}\` |`);
- } else {
- // Regular content file
- const e = mapEntry(fp);
- // Don't create links for deleted files
- const titleCell = (e.href && !isDeleted) ? `[${e.title}](${e.href})` : e.title;
- const pathCell = '`' + e.path + '`';
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- } else {
- // Static/assets: create direct preview links if base is known
- // Only link common web assets; skip JSON, map files, etc.
- const isLinkableAsset = /\.(png|jpe?g|gif|webp|svg|css|js|ico|txt|pdf|mp4|webm)$/i.test(fp);
- const rel = fp.replace(/^static\//, '/').replace(/^assets\//, '/assets/');
- const href = (previewBase && isLinkableAsset && !isDeleted) ? (previewBase + rel) : '';
- const title = titleFromPath(fp);
- const titleCell = href ? `[${title}](${href})` : title;
- const pathCell = '`' + fp + '`';
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- }
-
- return rows;
- }
-
- function buildRenamedRows(renamedFiles) {
- const rows = [];
-
- for (const rename of renamedFiles) {
- const oldPath = rename.old;
- const newPath = rename.new;
-
- if (newPath.startsWith('content/')) {
- // Map the new path to get the preview link
- const e = mapEntry(newPath);
- const titleCell = e.href ? `[${e.title}](${e.href})` : e.title;
- const pathCell = `\`${oldPath}\` → \`${newPath}\``;
- rows.push(`| ${titleCell} | ${pathCell} |`);
- } else {
- // Static/assets: create direct preview links if base is known
- const isLinkableAsset = /\.(png|jpe?g|gif|webp|svg|css|js|ico|txt|pdf|mp4|webm)$/i.test(newPath);
- const rel = newPath.replace(/^static\//, '/').replace(/^assets\//, '/assets/');
- const href = (previewBase && isLinkableAsset) ? (previewBase + rel) : '';
- const title = titleFromPath(newPath);
- const titleCell = href ? `[${title}](${href})` : title;
- const pathCell = `\`${oldPath}\` → \`${newPath}\``;
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- }
-
- return rows;
- }
-
- const addedRows = buildRows(addedFiltered);
- const modifiedRows = buildRows(modified);
- const deletedRows = buildRows(deletedFiltered, true); // Pass true for deleted files
- const renamedRows = buildRenamedRows(renamedFiles);
-
- const header = '\n\n**PR Preview: Changed content**\n\nBase preview: ' + previewBase;
-
- function section(title, rows) {
- if (rows.length === 0) return '';
- return `\n\n### ${title}\n\n| Title | Path |\n| --- | --- |\n${rows.join('\n')}`;
- }
-
- const body = [
- header,
- section('Renamed/Moved', renamedRows),
- section('Added', addedRows),
- section('Modified', modifiedRows),
- section('Deleted', deletedRows),
- ].join('');
-
- // Find and update our existing comment
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
-
- const existing = comments.find(c => //.test(c.body || ''));
- if (existing) {
- await github.rest.issues.updateComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- comment_id: existing.id,
- body,
- });
- core.info('Updated existing preview links comment');
-
- // Add link to PR description if we have preview links
- if (added.length + modified.length > 0) {
- try {
- const pr = await github.rest.pulls.get({
- owner: context.repo.owner,
- repo: context.repo.repo,
- pull_number: context.issue.number,
- });
-
- const currentBody = pr.data.body || '';
- const commentUrl = `https://github.com/${context.repo.owner}/${context.repo.repo}/pull/${context.issue.number}#issuecomment-${existing.id}`;
- const linkMarker = '';
- const linkText = `\n\n${linkMarker}\n\n📄 **[View preview links for changed content](${commentUrl})**`;
-
- // Only add if not already present
- if (!currentBody.includes(linkMarker)) {
- await github.rest.pulls.update({
- owner: context.repo.owner,
- repo: context.repo.repo,
- pull_number: context.issue.number,
- body: currentBody + linkText,
- });
- core.info('Added preview link to PR description');
- }
- } catch (e) {
- core.warning(`Could not update PR description: ${e.message}`);
- }
- }
- } else {
- // This shouldn't happen since main workflow creates comment first
- await github.rest.issues.createComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- body,
- });
- core.info('Created new preview links comment');
- }
diff --git a/.github/workflows/pr-preview-links.yml b/.github/workflows/pr-preview-links.yml
deleted file mode 100644
index 920f5312fe..0000000000
--- a/.github/workflows/pr-preview-links.yml
+++ /dev/null
@@ -1,717 +0,0 @@
-name: PR Preview Links
-
-on:
- pull_request:
- types: [opened, synchronize, reopened]
-
-permissions:
- contents: read
- pull-requests: write
- issues: write
-
-jobs:
- preview-links:
- runs-on: ubuntu-latest
- env:
- CF_PAGES_SUFFIX: docodile.pages.dev
- steps:
- - name: Checkout
- uses: actions/checkout@v5
- with:
- fetch-depth: 0
- ref: ${{ github.event.pull_request.head.sha }}
-
- - name: Setup Node.js
- uses: actions/setup-node@v6
- with:
- node-version-file: .node-version
-
- - name: Get Hugo version and setup
- run: |
- set -euo pipefail
- ver=$(grep 'HUGO_VERSION' wrangler.toml | awk -F '"' '{print $2}')
- wget "https://github.com/gohugoio/hugo/releases/download/v${ver}/hugo_extended_${ver}_linux-amd64.deb" -O hugo.deb
- sudo apt-get update
- sudo apt-get install -y ./hugo.deb
-
- - name: Setup Go
- uses: actions/setup-go@v6
- with:
- go-version-file: go.mod
- check-latest: true
-
- - name: Setup deps for build
- run: |
- set -euo pipefail
- npm install
- go mod download
- hugo mod get -u
-
- - name: Get changed files
- id: changed
- uses: tj-actions/changed-files@v47
- with:
- files: |
- content/**
- static/**
- assets/**
- layouts/**
- i18n/**
- configs/**
- include_all_old_new_renamed_files: true
-
- - name: Build Hugo (generate pageurls)
- if: steps.changed.outputs.any_changed == 'true'
- run: |
- # Debug: Check what ref we're building
- echo "Current branch: $(git branch --show-current)"
- echo "HEAD commit: $(git rev-parse HEAD)"
- echo "PR head SHA: ${{ github.event.pull_request.head.sha }}"
-
- hugo --minify
- test -f public/pageurls.json && echo "Found pageurls.json" || (echo "Missing pageurls.json" && ls -la public || true)
-
- # Debug: Check a specific file's title in the generated JSON
- if [ -f public/pageurls.json ]; then
- echo "Checking for public-api entries in pageurls.json:"
- jq '.[] | select(.path | contains("public-api"))' public/pageurls.json || true
- fi
-
- - name: Create or find preview comment
- if: steps.changed.outputs.any_changed == 'true'
- id: find-comment
- uses: actions/github-script@v8
- with:
- script: |
- // First, check if our comment already exists
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
-
- const existingComment = comments.find(c => //.test(c.body || ''));
- if (existingComment) {
- core.info(`Found existing preview comment: ${existingComment.id}`);
- core.setOutput('comment_id', existingComment.id);
- core.setOutput('comment_exists', 'true');
- } else {
- // Create placeholder comment immediately
- const placeholderBody = `\n\n**PR Preview: Changed content**\n\n⏳ *Generating preview links...*\n\nThis comment will be automatically updated when file changes are detected and Cloudflare Pages finishes deploying.`;
-
- const newComment = await github.rest.issues.createComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- body: placeholderBody,
- });
-
- core.info(`Created placeholder preview comment: ${newComment.data.id}`);
- core.setOutput('comment_id', newComment.data.id);
- core.setOutput('comment_exists', 'false');
- }
-
- - name: Find Cloudflare Branch Preview URL
- if: steps.changed.outputs.any_changed == 'true'
- id: find-preview
- uses: actions/github-script@v8
- with:
- script: |
- function htmlDecode(s) {
- return (s || '')
- .replace(/&/g, '&')
- .replace(/</g, '<')
- .replace(/>/g, '>')
- .replace(/"/g, '"')
- .replace(/'/g, "'");
- }
- function sanitizeUrl(u) {
- if (!u) return '';
- u = htmlDecode(u.trim());
- u = u.replace(/^['"<]+/, '').replace(/[>'"]+$/, '');
- u = u.split(/[\s<>'"\]\)]/)[0];
- try { u = decodeURI(u); } catch (e) {}
- return u.replace(/\/$/, '');
- }
- function extractBranchPreviewUrl(body) {
- // Look for "Branch Preview URL:" followed by a URL
- // In Cloudflare's table format, it's split across table cells
- const patterns = [
- // Table format with anchor tag (what Cloudflare actually uses)
- // Matches across multiple lines in a table cell
- /Branch\s+Preview\s+URL:<\/strong><\/td>
[\s\S]*?]+href=['"]([^'"<>]+pages\.dev[^'"<>]*)['"]/i,
- // Try anchor tag format on same line
- /Branch\s+Preview\s+URL:\s*]+href=['"]([^'"<>]+pages\.dev[^'"<>]*)['"]/i,
- // Try markdown link format
- /Branch\s+Preview\s+URL:\s*\[.*?\]\(([^)]+pages\.dev[^)]*)\)/i,
- // Try plain URL format (with or without angle brackets)
- /Branch\s+Preview\s+URL:\s*]+pages\.dev[^\s<>]*)/i
- ];
-
- for (const pattern of patterns) {
- const m = (body || '').match(pattern);
- if (m && m[1]) {
- const url = m[1];
- // Make sure we have the full URL including https://
- if (!url.startsWith('http')) {
- return sanitizeUrl('https://' + url);
- }
- return sanitizeUrl(url);
- }
- }
- return '';
- }
- async function fromOurExistingComment() {
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
- const ours = comments.find(c => //i.test(c.body || ''));
- if (!ours) return '';
- const body = ours.body || '';
- const hidden = body.match(//i);
- if (hidden && hidden[1]) return sanitizeUrl(hidden[1]);
- return '';
- }
- async function waitForCloudflareBranchUrl(maxWaitMs = 120000) { // Max 2 minutes
- const startTime = Date.now();
- let delayMs = 5000; // Start with 5 seconds
- let attempt = 0;
-
- while (Date.now() - startTime < maxWaitMs) {
- attempt++;
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
-
- for (const c of comments) {
- // Check if this is a Cloudflare comment
- if (c.user && c.user.login && c.user.login.includes('cloudflare') && c.body && c.body.includes('pages.dev')) {
- core.info(`Found Cloudflare comment from ${c.user.login}`);
- const url = extractBranchPreviewUrl(c.body || '');
- if (url) {
- core.info(`Successfully extracted Branch Preview URL: ${url}`);
- return url;
- } else if (c.body.includes('Branch Preview URL')) {
- core.info('Branch Preview URL field found but URL not yet populated');
- }
- }
- }
-
- const elapsed = Math.round((Date.now() - startTime) / 1000);
- const remaining = Math.round((maxWaitMs - (Date.now() - startTime)) / 1000);
-
- if (Date.now() - startTime + delayMs < maxWaitMs) {
- core.info(`Attempt ${attempt}: Branch Preview URL not found yet (${elapsed}s elapsed, ${remaining}s remaining)`);
- await new Promise(r => setTimeout(r, delayMs));
- // Exponential backoff: increase delay but cap at 20 seconds
- delayMs = Math.min(delayMs * 1.5, 20000);
- }
- }
-
- core.warning('Branch Preview URL not found within 2 minutes. Links will be added when Cloudflare comment triggers update workflow.');
- return '';
- }
- let base = await waitForCloudflareBranchUrl();
- if (!base) base = await fromOurExistingComment();
- core.info(`Branch Preview URL found: ${base || '(not yet)'}`);
- core.setOutput('base', base);
-
- - name: Update PR comment with preview links
- if: steps.changed.outputs.any_changed == 'true'
- uses: actions/github-script@v8
- env:
- ADDED: ${{ steps.changed.outputs.added_files }}
- MODIFIED: ${{ steps.changed.outputs.modified_files }}
- DELETED: ${{ steps.changed.outputs.deleted_files }}
- RENAMED: ${{ steps.changed.outputs.renamed_files }}
- RENAMED_PAIRS: ${{ steps.changed.outputs.all_old_new_renamed_files }}
- PREVIEW_BASE: ${{ steps.find-preview.outputs.base }}
- COMMENT_ID: ${{ steps.find-comment.outputs.comment_id }}
- with:
- script: |
- const fs = require('fs');
- const path = require('path');
-
- function splitList(s) {
- if (!s) return [];
- // Support newline, comma, or space separated outputs
- return s
- .split(/\r?\n|,|\s+/)
- .map(x => x.trim())
- .filter(Boolean);
- }
-
- const added = splitList(process.env.ADDED);
- const modified = splitList(process.env.MODIFIED);
- const deleted = splitList(process.env.DELETED);
- const renamed = splitList(process.env.RENAMED);
- const renamedPairs = splitList(process.env.RENAMED_PAIRS);
-
- // Process renamed files - tj-actions outputs them as "old_path new_path" pairs
- const renamedFiles = [];
- const renamedMapping = new Map(); // old path -> new path
- for (let i = 0; i < renamedPairs.length; i += 2) {
- if (i + 1 < renamedPairs.length) {
- const oldPath = renamedPairs[i];
- const newPath = renamedPairs[i + 1];
- renamedMapping.set(oldPath, newPath);
- renamedFiles.push({ old: oldPath, new: newPath });
- }
- }
-
- // Filter out renamed files from added and deleted arrays to avoid duplication
- const addedFiltered = added.filter(f => !Array.from(renamedMapping.values()).includes(f));
- const deletedFiltered = deleted.filter(f => !Array.from(renamedMapping.keys()).includes(f));
-
- const allChanged = [...addedFiltered, ...modified, ...deletedFiltered, ...renamed];
- const anyEligible = allChanged.some(p => /^(content|static|assets)\//.test(p));
- if (!anyEligible) {
- core.info('No relevant content changes. Updating comment to reflect this.');
-
- // Update the comment to show no content changes
- const commentId = process.env.COMMENT_ID;
- if (!commentId) {
- core.error('No comment ID found');
- return;
- }
-
- const previewBase = (process.env.PREVIEW_BASE || '').replace(/\/$/, '');
- const header = '\n\n**PR Preview: Changed content**';
- const body = header + '\n\nNo documentation content changes in this PR.';
-
- await github.rest.issues.updateComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- comment_id: parseInt(commentId),
- body,
- });
-
- core.info('Updated preview comment to show no content changes');
- return;
- }
-
- let pageMap = [];
-
- // Load pageurls.json for all languages
- const languages = ['', 'ja', 'ko']; // '' for English (root)
- for (const lang of languages) {
- const pageurlsPath = lang ? `public/${lang}/pageurls.json` : 'public/pageurls.json';
- try {
- const raw = fs.readFileSync(pageurlsPath, 'utf8');
- const langPages = JSON.parse(raw);
- pageMap = pageMap.concat(langPages);
- core.info(`Loaded ${langPages.length} pages from ${pageurlsPath}`);
- } catch (e) {
- core.warning(`Could not read ${pageurlsPath}. ${e.message}`);
- }
- }
-
- // Build lookup: support keys with and without language prefix
- const byPath = new Map();
- for (const p of pageMap) {
- const lang = p.lang || 'en'; // Default to 'en' if no lang specified
- const rel = (p.path || '').replace(/^\/+/, '').replace(/\\/g, '/');
- const withLang = lang && !rel.startsWith(lang + '/') ? `${lang}/${rel}` : rel;
- const withoutLang = lang && rel.startsWith(lang + '/') ? rel.slice(lang.length + 1) : rel;
-
- // Create all possible key variations
- const keys = new Set([
- rel,
- withLang,
- withoutLang,
- path.posix.join('content', withLang),
- path.posix.join('content', withoutLang),
- path.posix.join('content', rel),
- // Add explicit handling for language in path
- path.posix.join('content', lang, rel),
- path.posix.join('content', lang, withoutLang),
- // Add .md extension variations for content files
- rel.endsWith('.md') ? rel : rel + '.md',
- withLang.endsWith('.md') ? withLang : withLang + '.md',
- withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md',
- path.posix.join('content', lang, rel.endsWith('.md') ? rel : rel + '.md'),
- path.posix.join('content', lang, withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md'),
- path.posix.join('content', withLang.endsWith('.md') ? withLang : withLang + '.md'),
- path.posix.join('content', withoutLang.endsWith('.md') ? withoutLang : withoutLang + '.md'),
- // Special handling for index files
- path.posix.join('content', lang, '_index.md'),
- path.posix.join('content', lang, 'index.md'),
- ].filter(Boolean).map(k => k.replace(/\\/g, '/')));
-
- // Debug log for new pages
- if (p.path && p.path.includes('dspy')) {
- core.info(`Debug: Page ${p.path} (lang: ${lang}) has keys: ${Array.from(keys).join(', ')}`);
- }
-
- for (const k of keys) byPath.set(k, p);
- }
-
- // Find Cloudflare preview base URL from existing PR comments
- const comments = await github.paginate(github.rest.issues.listComments, {
- owner: context.repo.owner,
- repo: context.repo.repo,
- issue_number: context.issue.number,
- per_page: 100,
- });
- // Helpers to extract and sanitize pages.dev URLs
- function htmlDecode(s) {
- return s
- .replace(/&/g, '&')
- .replace(/</g, '<')
- .replace(/>/g, '>')
- .replace(/"/g, '"')
- .replace(/'/g, "'");
- }
- function sanitizeUrl(u) {
- if (!u) return '';
- u = htmlDecode(u.trim());
- // trim surrounding quotes or angle brackets
- u = u.replace(/^['"<]+/, '').replace(/[>'"]+$/, '');
- // cut at first disallowed delimiter if present
- u = u.split(/[\s<>'"\]\)]/)[0];
- try { u = decodeURI(u); } catch (e) {}
- // remove trailing slash
- return u.replace(/\/$/, '');
- }
- function extractUrls(body) {
- const urls = [];
- const regex = /https?:\/\/[^\s'"<>]+pages\.dev[^\s'"<>]*/ig;
- const matches = (body || '').match(regex) || [];
- for (let m of matches) {
- const clean = sanitizeUrl(m);
- if (clean && !urls.includes(clean)) urls.push(clean);
- }
- return urls;
- }
-
- // Use Branch Preview URL extracted from Cloudflare comment
- const previewBase = (process.env.PREVIEW_BASE || '').replace(/\/$/, '');
- if (!previewBase) {
- core.warning('No Branch Preview URL found yet; leaving links unlinked rather than using commit URL.');
- }
- core.info(`Preview base: ${previewBase || '(not found yet)'}`);
-
- function mapEntry(filePath) {
- const norm = filePath.replace(/\\/g, '/');
-
- // Try multiple variations to find the item
- let item = byPath.get(norm);
- if (!item) item = byPath.get(norm.replace(/^content\//, ''));
- if (!item) item = byPath.get(norm.replace(/\.md$/, ''));
- if (!item) item = byPath.get(norm.replace(/^content\//, '').replace(/\.md$/, ''));
-
- // Debug log for unmatched files
- if (!item) {
- core.info(`Debug: Could not find mapping for ${norm}`);
- // For new files, construct the URL based on the file path
- if (norm.startsWith('content/') && norm.endsWith('.md')) {
- // Extract language and path
- const pathParts = norm.replace(/^content\//, '').replace(/\.md$/, '').split('/');
- let lang = 'en';
- let urlPath = pathParts.join('/');
-
- // Check if first part is a language code
- if (['en', 'ja', 'ko'].includes(pathParts[0])) {
- lang = pathParts[0];
- urlPath = pathParts.slice(1).join('/');
- }
-
- // Handle _index.md files
- if (urlPath.endsWith('/_index') || urlPath === '_index') {
- urlPath = urlPath.replace(/\/_index$/, '').replace(/^_index$/, '');
- }
-
- // Construct the URL
- const rel = lang === 'en' ? `/${urlPath}/` : `/${lang}/${urlPath}/`;
- const href = previewBase ? (previewBase.replace(/\/$/, '') + rel) : '';
-
- core.info(`Debug: Constructed URL for new file ${norm}: ${href}`);
- return { title: titleFromPath(norm), rel, href, path: norm };
- }
- }
-
- if (!item) return { title: titleFromPath(norm), rel: '', href: '', path: norm };
- const rel = item.relPermalink || '';
- const href = previewBase ? (previewBase.replace(/\/$/, '') + rel) : '';
- const title = item.title || titleFromPath(norm);
- return { title, rel, href, path: norm };
- }
-
- function titleFromPath(p) {
- const stem = p.replace(/^content\//, '').replace(/\.(md|markdown)$/, '');
- const parts = stem.split('/');
- const last = parts[parts.length - 1];
- const base = last === 'index' ? (parts[parts.length - 2] || 'index') : last;
- return base.replace(/[\-_]+/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
- }
-
- // Function to check if a file is an include
- function isIncludeFile(filePath) {
- return /^content\/[^\/]+\/_includes\//.test(filePath);
- }
-
- // Function to find pages that use a specific include
- function findPagesUsingInclude(includePath) {
- const includeFileName = includePath.split('/').pop();
- const pagesUsingInclude = [];
-
- // Search through all pages in pageMap
- for (const page of pageMap) {
- if (page.path && page.path.endsWith('.md')) {
- try {
- // Construct the correct file path
- // page.path might already include content/ prefix, so handle both cases
- let contentPath = page.path;
- if (!contentPath.startsWith('content/')) {
- contentPath = path.join('content', page.lang || 'en', page.path);
- }
- if (fs.existsSync(contentPath)) {
- const content = fs.readFileSync(contentPath, 'utf8');
-
- // Check for usage of the include file with various path formats
- // The include might be referenced as:
- // - /_includes/filename.md
- // - /content/en/_includes/filename.md
- // - /content//_includes/filename.md
- const escapedFileName = includeFileName.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
-
- // Pattern to match any path ending with /_includes/filename
- const includePattern = `[^"']*\\/_includes\\/${escapedFileName}`;
-
- // Check for both readfile syntaxes with flexible path matching
- const pattern1 = new RegExp(`\\{\\{%\\s*readfile\\s+(?:file=)?["'](${includePattern})["']\\s*%\\}\\}`, 'i');
- const pattern2 = new RegExp(`\\{\\{<\\s*readfile\\s+file=["'](${includePattern})["']\\s*>\\}\\}`, 'i');
-
- if (pattern1.test(content) || pattern2.test(content)) {
- pagesUsingInclude.push(page);
- }
- }
- } catch (e) {
- // Skip if file can't be read
- }
- }
- }
-
- return pagesUsingInclude;
- }
-
- function buildRows(files, isDeleted = false) {
- const rows = [];
-
- for (const fp of files) {
- if (fp.startsWith('content/')) {
- // Check if this is an include file
- if (isIncludeFile(fp)) {
- // Use just the filename for includes
- const filename = fp.split('/').pop();
-
- // Find pages that use this include
- const dependentPages = findPagesUsingInclude(fp);
-
- // Build the title cell with filename and dependent pages
- let titleCell = `\`${filename}\``;
- if (dependentPages.length > 0) {
- // Add dependent pages as a nested list
- const pageLinks = dependentPages.map(page => {
- const href = previewBase ? (previewBase + page.relPermalink) : '';
- const linkText = page.title || titleFromPath(page.path);
- return href ? `[${linkText}](${href})` : linkText;
- });
-
- const dependentList = pageLinks.map(link => ` ↳ ${link}`).join('');
- titleCell += `:${dependentList}`;
- }
-
- rows.push(`| ${titleCell} | \`${fp}\` |`);
- } else {
- // Regular content file
- const e = mapEntry(fp);
- // Don't create links for deleted files
- const titleCell = (e.href && !isDeleted) ? `[${e.title}](${e.href})` : e.title;
- const pathCell = '`' + e.path + '`';
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- } else {
- // Static/assets: create direct preview links if base is known
- // Only link common web assets; skip JSON, map files, etc.
- const isLinkableAsset = /\.(png|jpe?g|gif|webp|svg|css|js|ico|txt|pdf|mp4|webm)$/i.test(fp);
- const rel = fp.replace(/^static\//, '/').replace(/^assets\//, '/assets/');
- const href = (previewBase && isLinkableAsset && !isDeleted) ? (previewBase + rel) : '';
- const title = titleFromPath(fp);
- const titleCell = href ? `[${title}](${href})` : title;
- const pathCell = '`' + fp + '`';
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- }
-
- return rows;
- }
-
- // Calculate file sizes/changes for sorting by delta
- function getFileStats(files) {
- const { execSync } = require('child_process');
- return files.map(fp => {
- let size = 0;
- try {
- // Use git to get the number of changed lines
- // For added files, count all lines; for modified, count insertions + deletions
- const diffStat = execSync(`git diff --numstat origin/${{ github.base_ref }}...HEAD -- "${fp}" 2>/dev/null || echo "0 0 ${fp}"`, { encoding: 'utf8' }).trim();
- const parts = diffStat.split(/\s+/);
- if (parts.length >= 2) {
- const insertions = parseInt(parts[0]) || 0;
- const deletions = parseInt(parts[1]) || 0;
- size = insertions + deletions;
- }
- } catch (e) {
- // If git diff fails, try file size as fallback
- try {
- if (fs.existsSync(fp)) {
- const stats = fs.statSync(fp);
- size = stats.size;
- }
- } catch (e2) {
- // Ignore errors
- }
- }
- return { path: fp, size };
- });
- }
-
- // Sort files by size (as a proxy for change delta) and limit to maxRows
- function limitAndSortFiles(files, maxRows = 20) {
- if (files.length <= maxRows) {
- return { files: files.sort(), truncated: false };
- }
-
- // Get file stats and sort by size descending
- const stats = getFileStats(files);
- stats.sort((a, b) => b.size - a.size);
-
- // Take top maxRows files
- const topFiles = stats.slice(0, maxRows).map(s => s.path);
- // Sort alphabetically for consistent display
- topFiles.sort();
-
- return { files: topFiles, truncated: true, total: files.length };
- }
-
- function buildRenamedRows(renamedFiles) {
- const rows = [];
-
- for (const rename of renamedFiles) {
- const oldPath = rename.old;
- const newPath = rename.new;
-
- if (newPath.startsWith('content/')) {
- // Map the new path to get the preview link
- const e = mapEntry(newPath);
- const titleCell = e.href ? `[${e.title}](${e.href})` : e.title;
- const pathCell = `\`${oldPath}\` → \`${newPath}\``;
- rows.push(`| ${titleCell} | ${pathCell} |`);
- } else {
- // Static/assets: create direct preview links if base is known
- const isLinkableAsset = /\.(png|jpe?g|gif|webp|svg|css|js|ico|txt|pdf|mp4|webm)$/i.test(newPath);
- const rel = newPath.replace(/^static\//, '/').replace(/^assets\//, '/assets/');
- const href = (previewBase && isLinkableAsset) ? (previewBase + rel) : '';
- const title = titleFromPath(newPath);
- const titleCell = href ? `[${title}](${href})` : title;
- const pathCell = `\`${oldPath}\` → \`${newPath}\``;
- rows.push(`| ${titleCell} | ${pathCell} |`);
- }
- }
-
- return rows;
- }
-
- const MAX_ROWS_PER_TABLE = 20;
- const addedInfo = limitAndSortFiles(addedFiltered, MAX_ROWS_PER_TABLE);
- const modifiedInfo = limitAndSortFiles(modified, MAX_ROWS_PER_TABLE);
- const deletedInfo = limitAndSortFiles(deletedFiltered, MAX_ROWS_PER_TABLE);
- const renamedInfo = { files: renamedFiles.slice(0, MAX_ROWS_PER_TABLE), truncated: renamedFiles.length > MAX_ROWS_PER_TABLE, total: renamedFiles.length };
-
- const addedRows = buildRows(addedInfo.files);
- const modifiedRows = buildRows(modifiedInfo.files);
- const deletedRows = buildRows(deletedInfo.files, true); // Pass true for deleted files
- const renamedRows = buildRenamedRows(renamedInfo.files);
-
- const header = '\n\n**PR Preview: Changed content**' + (previewBase ? `\n\nBase preview: ${previewBase}` : '\n\n⏳ *Links will be added automatically when Cloudflare Pages finishes deploying (typically 2-5 minutes)*');
- if (!previewBase) {
- core.info('Preview base URL not available yet. Comment will be automatically updated when Cloudflare posts the Branch Preview URL.');
- }
-
- function section(title, rows, info) {
- if (rows.length === 0 && !info.truncated) return '';
-
- let sectionTitle = title;
- if (info.truncated) {
- sectionTitle += ` (showing ${rows.length} of ${info.total} files with largest changes)`;
- }
-
- return `\n\n### ${sectionTitle}\n\n| Title | Path |\n| --- | --- |\n${rows.join('\n')}`;
- }
-
- const body = [
- header,
- section('Renamed/Moved', renamedRows, renamedInfo),
- section('Added', addedRows, addedInfo),
- section('Modified', modifiedRows, modifiedInfo),
- section('Deleted', deletedRows, deletedInfo),
- ].join('');
-
- // Check if body exceeds GitHub's limit
- const MAX_COMMENT_SIZE = 65536;
- if (body.length > MAX_COMMENT_SIZE) {
- core.warning(`Preview comment size (${body.length}) exceeds GitHub's limit (${MAX_COMMENT_SIZE}). This should not happen with row limits in place.`);
- }
-
- // Update the existing comment
- const commentId = process.env.COMMENT_ID;
- if (!commentId) {
- core.error('No comment ID found');
- return;
- }
-
- await github.rest.issues.updateComment({
- owner: context.repo.owner,
- repo: context.repo.repo,
- comment_id: parseInt(commentId),
- body,
- });
-
- core.info(`Updated preview comment ${commentId} with ${addedFiltered.length + modified.length + deletedFiltered.length + renamedFiles.length} changed files`);
-
- // Add link to PR description if we have preview links and this is the first time
- if (previewBase && (addedFiltered.length + modified.length + renamedFiles.length) > 0) {
- try {
- const pr = await github.rest.pulls.get({
- owner: context.repo.owner,
- repo: context.repo.repo,
- pull_number: context.issue.number,
- });
-
- const currentBody = pr.data.body || '';
- const commentUrl = `https://github.com/${context.repo.owner}/${context.repo.repo}/pull/${context.issue.number}#issuecomment-${commentId}`;
- const linkMarker = '';
- const linkText = `\n\n${linkMarker}\n📄 **[View preview links for changed pages](${commentUrl})**`;
-
- // Only add if not already present
- if (!currentBody.includes(linkMarker)) {
- await github.rest.pulls.update({
- owner: context.repo.owner,
- repo: context.repo.repo,
- pull_number: context.issue.number,
- body: currentBody + linkText,
- });
- core.info('Added preview link to PR description');
- }
- } catch (e) {
- core.warning(`Could not update PR description: ${e.message}`);
- }
- }
diff --git a/README.md b/README.md
index 0635aa3a79..6f245570e2 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
# Weights & Biases Documentation
-The Weights & Biases Docs ([https://docs.wandb.ai/](https://docs.wandb.ai/)) is built using Docsy, a technical documentation theme for Hugo, a static website generator. The high level overview of the doc writing process is:
+The Weights & Biases Docs ([https://docs.wandb.ai/](https://docs.wandb.ai/)) is built using Mintlify, a static website generator. The high level overview of the doc writing process is:
1. Edit the desired files
2. Create a pull request proposing your changes
@@ -30,10 +30,11 @@ This section shows how to edit a page or report a bug from within your browser w
If you work for Weights & Biases, file a doc JIRA, using this template: https://wandb.atlassian.net/secure/CreateIssueDetails!init.jspa?priority=3&pid=10026&issuetype=10047.
-
+{/*
To report a bug on a page you're reading on docs.wandb.com:
1. Scroll to the bottom of the page and click **Report issue**.
1. Provide a title and optionally edit the description, then click **Create**.
+*/}
To report a bug from https://github.com/wandb/docs:
1. Click the **Issues** tab.
@@ -42,227 +43,105 @@ To report a bug from https://github.com/wandb/docs:
## Prerequisites
-A current version of NodeJS is required; ideally, something newer than version 20. If you still need to use an old version of node for other projects, we suggest using `nvm` and setting up version 20 using that, which you can swap into with the `use` command:
+The `mint` CLI is required for local builds. See https://www.mintlify.com/docs/installation. Install it from `npx` or from Homebrew.
-```
-nvm install 20
-nvm use 20
-```
### macOS
-After cloning this repo, `cd` into your local clone directory and these commands:
-
-```
-brew install go
-brew install hugo
-brew install npm
-npm install
-hugo mod get -u
-```
-
-The last lines critical, as it downloads Hugo, the [Docsy](https://docsy.dev) module for Hugo, and the dependencies of each.
-
-## Running the website locally
-
-```bash
-hugo server
-```
-
-## Exiting `hugo server`
-
-Hit `CTRL+C` in the terminal that is showing `hugo` activity to interrupt the server and exit to the terminal prompt.
-
-## Hugo and Docsy shortcodes
-
-- We use Docsy's `alert` shortcode for admonitions. The alert `color` determines the admonition type. Refer to [alerts](https://www.docsy.dev/docs/adding-content/shortcodes/#alert) for details. Examples:
- ```markdown
- {{% alert %}}
- Only **public** reports are viewable when embedded.
- {{% /alert %}}
- ```
- ```markdown
- {{% alert title="Undo changes to your workspace" %}}
- Select the undo button (arrow that points left) to undo any unwanted changes.
- {{% /alert %}}
- ```
- ```markdown
- {{% alert title="Warning" color="warning" %}}
- This is a warning.
- {{% /alert %}}
- ```
-- We use Docsy's `tabpane` and `tab` shortcodes for tabbed content. Refer to [tabpane](https://www.docsy.dev/docs/adding-content/shortcodes/#tabpane) for details. Example:
- ```markdown
- {{< tabpane text=true >}}
- {{% tab header="GitHub repository dispatch" value="github" %}}
- ... Markdown contents ...
- {{% /tab %}}
-
- {{% tab header="Microsoft Teams notification" value="microsoft"%}}
- ... Markdown contents ...
- {{% /tab %}}
- {{< /tabpane >}}
- ```
-- We use a custom `img` shortcode for images. It is implemented in `layouts/shortcodes/img.html`. Examples:
- ```markdown
- {{< img src="/images/app_ui/automated_workspace.svg" >}}
- ```
- ```markdown
- {{< img src="/images/app_ui/automated_workspace.svg" alt="automated workspace icon" >}}
+After cloning this repo:
+1. `cd` into the clone.
+2. Create a working branch:
+ ```shell
+ git checkout -b my_working_branch origin/main
```
- ```markdown
- {{< img src="/images/app_ui/automated_workspace.svg" alt="automated workspace icon" width="32px" >}}
+3. Build and build and serve the docs locally:
+ ```shell
+ mint dev
```
- ```markdown
- {{< img src="/images/app_ui/demo_make_a_custom_chart_bar_chart.gif" alt="Creating a custom bar chart showing accuracy across runs in a project" max-width="90%" >}}
+ By default, content is served from `https://localhost:3000/`.
+4. Check for broken links and images:
+ ```shell
+ mint broken-links
+ ```
+5. Push your branch to `origin`:
+ ```shell
+ git push origin my_working_branch
```
-- We use a custom `ctabutton` shortcode to link to Colab notebooks. It is implemented in `layouts/shortcodes/cta-button.html`. Examples:
- ```markdown
- {{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Using_W&B_Sweeps_with_XGBoost.ipynb" >}}
- ```
-- We use a custom `prism` shortcode to load titled code examples from `static/` within the docs repo. Example for a file stored in `static/webhook_test.sh`:
- ```markdown
- {{< prism file="/webhook_test.sh" title="webhook_test.sh">}}{{< /prism >}}
- ```
-- We are _experimenting_ with using `readfile` for includes. If you run into issues, report it in a Github issue.
-
- Add a Markdown file with no front matter to `content/_includes/`. Subdirectories are supported. Include the file using the [`readfile`](https://www.docsy.dev/docs/adding-content/shortcodes/#reuse-documentation) shortcode. For example:
- ```markdown
- {{< readfile file="/_includes/enterprise-only.md" >}}
- ```
-
- - If you change an include, the `hugo serve` incremental build does not pick up the change. Stop and restart `hugo serve`.
- - Hugo and Docsy shortcodes are **not** supported inside the include file.
-## Editing style
-
-Style overrides are in `/assets/scss/_variables_project.scss`. Here we can override all the styles that ship with the Docsy theme. O
-
-## Troubleshooting
-
-As you run the website locally, you may run into the following error:
-
-```console
-$ hugo server
-WARN 2023/06/27 16:59:06 Module "project" is not compatible with this Hugo version; run "hugo mod graph" for more information.
-Start building sites …
-hugo v0.101.0-466fa43c16709b4483689930a4f9ac8add5c9f66+extended windows/amd64 BuildDate=2022-06-16T07:09:16Z VendorInfo=gohugoio
-Error: Error building site: "C:\Users\foo\path\to\docsy-example\content\en\_index.md:5:1": failed to extract shortcode: template for shortcode "blocks/cover" not found
-Built in 27 ms
-```
-
-This error occurs if you are running an outdated version of Hugo. As of docsy theme version `v0.7.0`, hugo version `0.110.0` or higher is required.
-See this [section](https://www.docsy.dev/docs/get-started/docsy-as-module/installation-prerequisites/#install-hugo) of the user guide for instructions on how to install Hugo.
-
-Or you may be confronted with the following error:
-
-```console
-$ hugo server
-
-INFO 2021/01/21 21:07:55 Using config file:
-Building sites … INFO 2021/01/21 21:07:55 syncing static files to /
-Built in 288 ms
-Error: Error building site: TOCSS: failed to transform "scss/main.scss" (text/x-scss): resource "scss/scss/main.scss_9fadf33d895a46083cdd64396b57ef68" not found in file cache
-```
-
-This error occurs if you have not installed the extended version of Hugo.
-See this [section](https://www.docsy.dev/docs/get-started/docsy-as-module/installation-prerequisites/#install-hugo) of the user guide for instructions on how to install Hugo.
+6. The first time you push a new branch, the terminal output includes a link to create a PR. Click the link and create a draft PR. CI tests will run.
+7. When you are satisfied with your PR and all tests pass, click **Ready for review** to convert the draft PR to a reviewable PR. A member of the W&B docs team will review your changes and give feedback.
+8. When all feedback is addressed and the PR is approved, the reviewer will merge the PR.
-Or you may encounter the following error:
-```console
-$ hugo server
+## Running the website locally
-Error: failed to download modules: binary with name "go" not found
-```
+Build and build and serve the docs locally:
+ ```shell
+ mint dev
+ ```
-This error occurs if you have not installed the `go` programming language on your system.
-See this [section](https://www.docsy.dev/docs/get-started/docsy-as-module/installation-prerequisites/#install-go-language) of the user guide for instructions on how to install `go`.
+## Exiting `mint dev`
+To interrupt the Mintlify server, type `CTRL+C` in the terminal where it is running.
-[alternate dashboard]: https://app.netlify.com/sites/goldydocs/deploys
-[deploys]: https://app.netlify.com/sites/docsy-example/deploys
-[Docsy user guide]: https://docsy.dev/docs
-[Docsy]: https://github.com/google/docsy
-[example.docsy.dev]: https://example.docsy.dev
-[Hugo theme module]: https://gohugo.io/hugo-modules/use-modules/#use-a-module-for-a-theme
-[Netlify]: https://netlify.com
-[Docker Compose documentation]: https://docs.docker.com/compose/gettingstarted/
+## Mintlify compatibility
+Mintlify processes Markdown files in `.mdx` format, which has stricter requirements than `.md`. If a file contains incompatible Markdown, `mint dev` will fail and `mint broken-links` will show errors. For example, HTML comments (``) are not supported. Instead, use this syntax for comments: `{/* like this */}`.
+For general guidelinest, refer to the **Create content** section of the [Mintlify documentation](https://www.mintlify.com/docs/). For example:
+- [Format text](https://www.mintlify.com/docs/create/text)
+- [Lists and tables](https://www.mintlify.com/docs/create/list-table)
+- [Images and embeds](https://www.mintlify.com/docs/create/image-embeds)
+- [Callouts](https://www.mintlify.com/docs/components/callouts)
+- [Cards](https://www.mintlify.com/docs/components/cards)
+- [Tabs](https://www.mintlify.com/docs/components/tabs)
+This list of links is not exhaustive. Check the Mintlify documentation for full details.
## How to edit the docs locally
1. Navigate to your local clone this repo and pull the latest changes from main:
-
-```bash
-git pull origin main
-```
-
+ ```bash
+ git pull origin main
+ ```
2. Create a feature branch off of `main`.
-
-```bash
-git checkout -b
-```
-
+ ```bash
+ git checkout -b
+ ```
3. After installing the prerequsites documented above, start a local preview of the docs.
-
-```bash
-hugo server
-```
-
-This will return the localhost URL and port number where you can preview your changes to the docs as you make them (e.g. `https://localhost:1313`).
-
+ ```bash
+ mint dev
+ ```
+ This will return the localhost URL and port number where you can preview your changes to the docs as you make them (e.g. `https://localhost:3000`).
4. Make your changes on the new branch.
-5. Check your changes are rendered correctly. Any time you save a file, the preview should automatically update.
+5. Check your changes are rendered correctly. Any time you save a file, the preview automatically updates.
7. Commit the changes to the branch.
-
-```bash
-git add .
-git commit -m 'Useful commit message.'
-```
-
+ ```bash
+ git add .
+ git commit -m 'Useful commit message.'
+ ```
8. Push the branch to GitHub.
-
-```bash
-git push origin
-```
-
+ ```bash
+ git push origin
+ ```
9. Open a pull request from the new branch to the original repo.
-
## What files do I edit?
+You can edit most `.mdx` files in the repo directly. Content in a few directories is generated from upstream code. Edit the code in the source repo, not in `wandb/docs`. After your upstream change is merged and released, when the relevant content is next generated, your change will be included.
-There are two types of docs in our Docs site: Developer Guide and the API Reference Guide.
-
-### Developer Guide
-
-All markdown files for the [W&B Developer Guide](https://docs.wandb.ai/) are stored in:
-
-```bash
-content/guides/
-```
-
-The PR you create will get reviewed and (if approved) merged by the Docs Team.
-
-### API Reference Guide
-
-All markdown files for the [W&B API Reference Guide](https://docs.wandb.ai/ref) are stored in:
-
-```bash
-content/ref
-```
-
-The markdown files are generated from docstrings in https://github.com/wandb/wandb. Modify the docstring from the appropriate Python Class, function, or CLI definition to update the public-facing documentation API.
-
-Once you are done, create a pull request from https://github.com/wandb/wandb. The PR you create will get reviewed and (if approved) merged by the SDK Team. The Docs are updated when the W&B SDK Team makes an W&BSDK Release. SDK Releases occur about every 2-4 weeks.
+| Directory | Source repo |
+|-----------|-------------|
+| `/ref/python/`| [`wandb/wandb`](https://github.com/wandb/wandb) |
+| `/models/ref/cli/` | [`wandb/wandb`](https://github.com/wandb/wandb)|
+| `/weave/api-reference/` | [`wandb/weave`](https://github.com/wandb/weave) |
+| `/weave/cookbooks/` | [`wandb/weave`](https://github.com/wandb/weave) |
+| `/weave/reference/` | [`wandb/weave`](https://github.com/wandb/weave) |
## AI resources
-The `.ai/` directory contains resources specifically designed for AI agents working with this repository. These include:
+The `.ai/` directory contains experimental resources specifically designed for AI agents working with this repository. These include:
- **[Runbooks](.ai/runbooks/)**: Step-by-step instructions for complex, recurring tasks (e.g., testing GitHub Actions changes)
- **[Style guide](.ai/style-guide.md)**: Quick reference for AI agents on wandb/docs style conventions
-If you're using an AI agent to help with documentation tasks, provide these resources as context to ensure consistent, high-quality contributions. See the [.ai/README.md](.ai/README.md) for more details.
+If you're using an AI agent to help with documentation tasks, provide these resources as context to ensure consistent, high-quality contributions. Agents do not discover or load the prompts in `.ai/` automatically. See the [.ai/README.md](.ai/README.md) for more details.
## License
@@ -278,7 +157,7 @@ The source for this documentation is offered under the Apache 2.0 license.
## Attributions
-- This project uses Docsy, a Hugo theme by Google. [License](https://github.com/google/docsy/blob/main/LICENSE)
+- This project uses Mintlify. [License](https://github.com/mintlify)
- A dependency of Docsy is the `caniuse-lite` package, offered under CC-BY-4.0. [License](https://github.com/browserslist/caniuse-lite/blob/main/LICENSE)
- Another dependency of Docsy is Font Awesome Free, offered under the CC-BY-4.0, SIL OFL 1.1, and MIT licenses. [License notice](https://github.com/FortAwesome/Font-Awesome/blob/master/LICENSE.txt)
- This site is built using Hugo, a static site generator. [License](https://github.com/gohugoio/hugo/blob/master/LICENSE)
diff --git a/archetypes/support.md b/archetypes/support.md
deleted file mode 100644
index 25c8ab376f..0000000000
--- a/archetypes/support.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Your Title Here
-toc_hide: true
-type: docs
-url: /support/:filename
-support:
- - your-term-here
----
-
-Your content here.
diff --git a/assets/images/app_ui/custom-charts-query-example.png b/assets/images/app_ui/custom-charts-query-example.png
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diff --git a/assets/images/app_ui/manual-line-plot.png b/assets/images/app_ui/manual-line-plot.png
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diff --git a/assets/images/app_ui/run-colors-colorblind-deuteranomaly.png b/assets/images/app_ui/run-colors-colorblind-deuteranomaly.png
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diff --git a/assets/images/app_ui/run-colors-key-based.png b/assets/images/app_ui/run-colors-key-based.png
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diff --git a/assets/images/general/architecture.png b/assets/images/general/architecture.png
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diff --git a/assets/images/hosting/saas_cloud_arch.png b/assets/images/hosting/saas_cloud_arch.png
deleted file mode 100644
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diff --git a/assets/scss/_variables_project.scss b/assets/scss/_variables_project.scss
deleted file mode 100644
index edaf1036fe..0000000000
--- a/assets/scss/_variables_project.scss
+++ /dev/null
@@ -1,1493 +0,0 @@
-/*
-
-Add styles or override variables from the theme here.
-
-*/
-
-@import url('https://fonts.googleapis.com/css2?family=Source+Serif+4:ital,opsz,wght@0,8..60,200..900;1,8..60,200..900&display=swap');
-
-// First define the base color tokens
-// Moon (Grayscale)
-$moon-white: #ffffff;
-$moon-50: #fafafa;
-$moon-100: #f5f6f7;
-$moon-200: #e4e6eb;
-$moon-250: #dfe0e2;
-$moon-300: #d3d7de;
-$moon-350: #c1c6cf;
-$moon-400: #aeb3bd;
-$moon-500: #79808a;
-$moon-600: #565c66;
-$moon-650: #4a4f58;
-$moon-700: #3f464f;
-$moon-750: #363c44;
-$moon-800: #2b3038;
-$moon-900: #1a1d24;
-$moon-950: #171a1f;
-
-// Teal colors
-$teal-300: #a9edf2;
-$teal-400: #58d3db;
-$teal-450: #10bfcc;
-$teal-500: #13a9ba;
-$teal-550: #0e97a7;
-$teal-600: #038194;
-
-// Border colors
-$border-default: $moon-200; // Using moon-200 instead of moon-250
-$border-hover: $moon-350; // color/moon/350
-$border-active: $teal-500; // color/teal/500
-$border-subtle: $moon-100; // color/moon/100
-$dark-border-default: $moon-750; // color/moon/750
-$dark-border-hover: $moon-650; // color/moon/650
-$dark-border-subtle: $moon-800; // color/moon/800
-
-// Border radius
-$border-radius-medium: 8px; // Used for most components
-$border-radius-large: 12px; // Used for modals and larger components
-
-// Then define the semantic color tokens
-// Text colors
-$text-primary: $moon-800; // color/moon/800
-$text-secondary: $moon-600; // color/moon/600
-$text-tertiary: $moon-500; // color/moon/500
-$text-disabled: $moon-350; // color/moon/350
-
-// Link colors
-$link-primary: $teal-600; // color/teal/600
-$link-hover: $teal-550; // color/teal/550
-$link-secondary: $moon-600; // color/moon/600
-$link-secondary-hover: $moon-500; // color/moon/500
-
-// Background colors
-$bg-primary: $moon-white; // White background
-$bg-secondary: $moon-50; // color/moon/50
-$bg-tertiary: $moon-100; // color/moon/100
-$bg-disabled: $moon-350; // color/moon/350
-
-// Dark mode colors
-$dark-text-primary: $moon-200; // color/moon/200
-$dark-text-secondary: $moon-400; // color/moon/400
-$dark-text-tertiary: $moon-500; // color/moon/500
-$dark-text-disabled: $moon-650; // color/moon/650
-
-$dark-link-primary: $teal-500; // color/teal/500
-$dark-link-hover: $teal-450; // color/teal/450
-$dark-link-secondary: $moon-600; // color/moon/600
-$dark-link-secondary-hover: $moon-500; // color/moon/500
-
-$dark-bg-primary: $moon-900; // color/moon/900
-$dark-bg-secondary: $moon-800; // color/moon/800
-$dark-bg-tertiary: $moon-700; // color/moon/700
-$dark-bg-disabled: $moon-650; // color/moon/650
-
-// Other brand colors
-$nav-bg: rgb(26, 28, 31);
-$wandb-gold: #FFCC33;
-$wandb-link-blue: rgb(7, 177, 196);
-$hover-bg: #A9EDF260;
-
-// Gradients
-$sunset-gradient: linear-gradient(98deg, #e180ff 0%, #ffcf4d 100%);
-$wandb-gradient: linear-gradient(180deg, #ffcc33 39%, #ffad33 72%);
-
-// Font weights
-$font-weight-light: 300;
-$font-weight-regular: 400;
-$font-weight-medium: 500;
-$font-weight-semibold: 600;
-$font-weight-bold: 700;
-
-// Bootstrap overrides
-:root {
- --bs-nav-link-font-size: 18px;
-}
-
-// Typography Component
-// Base body styles
-body {
- font-family: "Source Sans Pro", sans-serif;
- font-size: 16px;
- font-weight: $font-weight-regular;
- color: $text-primary;
- background-color: $bg-primary;
-
- [data-bs-theme=dark] & {
- color: $dark-text-primary;
- background-color: $dark-bg-primary;
- }
-}
-p, td, div
-{
- font-family: "Source Sans Pro", sans-serif;
-}
-.td-sidebar-nav__section-title, .td-sidebar-nav__section without-child
-{
- font-size: 16px;
- font-weight: $font-weight-bold;
-}
-
-// Headings
-h1, h2, h3, h4, h5, h6 {
- font-weight: $font-weight-regular !important;
- font-family: "Source Serif 4", sans-serif;
- color: $text-primary;
-
- [data-bs-theme=dark] & {
- color: $dark-text-primary;
- }
-}
-
-// Heading sizes
-h1, .td-content h1 { font-size: 32px !important; }
-h2, .td-content h2 { font-size: 24px !important; }
-h3, .td-content h3 { font-size: 20px !important; }
-h4, .td-content h4 { font-size: 18px !important; }
-h5, .td-content h5 { font-size: 17px !important; }
-h6, .td-content h6 { font-size: 16px !important; }
-
-// Lead paragraph
-.td-content .lead {
- font-size: 16px !important;
- font-weight: $font-weight-regular !important;
-}
-
-// Reading time
-.reading-time {
- font-size: 14px;
- color: $text-secondary;
-
- [data-bs-theme=dark] & {
- color: $dark-text-secondary;
- }
-}
-
-// Links
-a {
- color: $link-primary !important;
- text-decoration: none !important;
-
- &:hover {
- color: $link-hover !important;
- text-decoration: none !important;
- }
-
- [data-bs-theme=dark] & {
- color: $dark-link-primary !important;
-
- &:hover {
- color: $dark-link-hover !important;
- }
- }
-}
-
-// hide "create child" and "create project issue" links
-.td-page-meta__child, .td-page-meta__project-issue { display: none !important; }
-
-// shrink the right nav a bit
-.td-page-meta {
- display: none !important;
- font-size: 16px !important;
- a {
- padding-bottom: 15px;
- line-height: 22px;
- }
-}
-
-// hide left nav label
-.tree-root { display: none !important;}
-
-// First define breakpoints
-$breakpoint-sm: 576px; // Phone
-$breakpoint-md: 768px; // Tablet
-$breakpoint-lg: 992px; // Desktop
-$breakpoint-xl: 1200px; // Large desktop
-
-// Consolidated navbar styles with mobile-first approach
-.td-navbar {
- background-color: $bg-primary !important;
- padding: 0 !important;
- border-bottom: 1px solid $border-default !important;
-
- // Base styles (mobile first)
- .container-fluid {
- padding: 8px !important;
- gap: 16px !important;
-
- .td-navbar-nav-scroll {
- align-items: center !important;
- height: auto !important;
- margin: 0 !important;
- padding: 0 !important;
- gap: 16px !important;
-
- .navbar-nav {
- display: flex !important;
- align-items: center !important;
- padding-left: 0 !important;
- margin: 0 !important;
- gap: 8px !important;
-
- .nav-item {
- padding-inline-end: 0 !important;
- }
-
- &.main-nav {
- padding: 0 !important;
- margin: 0 !important;
-
- // Nav links base styles
- .nav-link {
- color: $text-secondary !important;
- font-weight: $font-weight-regular !important;
- transition: all 0.2s ease-in-out !important;
- border-bottom: none !important;
- border-radius: $border-radius-medium !important;
- padding: 8px 10px !important;
- font-size: 16px !important;
-
- &:hover {
- text-decoration: none !important;
- background-color: rgba($moon-300, 0.2) !important;
- }
-
- &.active {
- color: $link-primary !important;
- background-color: rgba($moon-300, 0.2) !important;
- font-weight: $font-weight-regular !important;
- }
- }
-
- }
-
- &.main-nav-right {
- padding: 0 !important;
- margin: 0 !important;
- align-items: center !important;
- gap: 0 !important;
-
- // Update mobile light/dark toggle styles
- .td-light-dark-menu {
- display: flex !important;
- align-items: center !important;
- margin: 0 !important;
- gap: 0 !important;
- min-width: 0 !important;
- }
-
- // Dropdown styles
- .nav-item.dropdown {
- margin: 0 !important;
-
- .nav-link {
- font-size: 16px !important;
- font-weight: $font-weight-regular !important;
- color: $text-secondary !important;
-
- &:hover {
- background-color: transparent !important;
- }
-
- &::after {
- display: none !important;
- }
-
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
- }
- }
- }
- }
- }
- }
- }
-
- // Brand/logo styles
- .navbar-brand {
- display: flex !important;
- align-items: center !important;
- padding: 0 !important;
- margin-right: 0 !important;
-
- .navbar-logo {
- display: block;
- width: auto;
- height: auto;
- margin: 0 !important;
-
- svg {
- margin: 0 !important;
- width: 100%;
- }
- }
- }
-
- // Hide the doc site name
- .navbar-brand__name {
- display: none !important;
- }
-
-
- // Tablet styles (577px and up)
- @media (min-width: $breakpoint-sm + 1) {
- .container-fluid {
- padding: 8px 12px !important;
- }
-
- .navbar-brand {
- margin-right: 1rem !important;
- }
- }
-
- // Desktop styles (992px and up)
- @media (min-width: $breakpoint-lg) {
- .container-fluid {
- padding: 8px 24px !important;
- flex-direction: row !important;
- align-items: center !important;
- justify-content: flex-start !important;
- gap: 32px !important;
-
- .td-navbar-nav-scroll {
- flex-grow: 1 !important;
-
- .navbar-nav {
-
- &.main-nav {
- flex-grow: 1 !important;
- gap: 16px !important;
-
- .nav-link {
- padding: 6px 12px !important;
- font-size: 18px !important;
- }
- }
-
- &.main-nav-right {
- gap: 16px !important;
-
- .nav-link{
- font-size: 16px !important;
- color: $text-secondary !important;
- }
- }
- }
- .main-nav-search {
- min-width: 300px !important;
-
- .DocSearch.DocSearch-Button {
- margin: 0 !important;
- min-width: 100% !important;
- }
- }
- }
- }
-
-
- .nav-item.dropdown {
- &.d-none.d-lg-block,
- &.td-light-dark-menu {
- margin: 0 4px !important;
-
- .nav-link {
- font-size: 16px !important;
- }
- }
- }
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- background-color: $dark-bg-primary !important;
- border-bottom: 1px solid $dark-border-default !important;
-
- &.js-navbar-scroll {
- background-color: $dark-bg-primary !important;
- }
-
- .td-navbar-nav-scroll {
-
- .navbar-nav {
-
- &.main-nav {
- .nav-link {
- color: $moon-white !important;
-
- &:hover {
- color: $teal-500 !important;
- }
-
- &.active {
- color: $teal-500 !important;
- }
- }
- }
-
- .nav-item.dropdown {
- &.d-none.d-lg-block,
- &.td-light-dark-menu {
- .nav-link {
- color: $dark-text-primary !important;
-
- &:hover {
- color: $dark-link-primary !important;
- }
- }
- }
- }
- }
- }
- }
-
- // Update the light/dark menu styles
- .td-light-dark-menu {
- &.nav-item.dropdown {
- margin: 0 2px !important;
- display: flex !important;
- align-items: center !important;
- }
-
- // Hide from navbar-right on mobile
- &.d-none {
- @media (max-width: #{$breakpoint-lg - 1}) {
- display: none !important;
- }
- }
- }
-}
-
-// headings use WandB serif font
-h1, h2, h3, h4, h5, h6 {
- font-weight: $font-weight-regular !important;
- font-family: "Source Serif 4", sans-serif;
- color: $text-primary;
-}
-
-.td-sidebar-nav-active-item {
- text-decoration: none;
-}
-
-ol.breadcrumb li {
- font-size: 13px;
-}
-ol.breadcrumb li:not(:first-child):before {
- content: ">" !important;
-}
-
-.td-sidebar-nav__section-title, .td-sidebar-nav__section {
- margin-bottom: 10px !important;
- .nav-link.dropdown-toggle {
- margin-left: -5px !important;
- }
-}
-
-.td-content .lead {
- font-size: 16px !important;
- font-weight: $font-weight-regular !important;
-}
-
-// Table of Contents Component
-#TableOfContents {
-
- font-size: 13px !important;
- font-family: "Source Sans Pro", sans-serif;
- a{
- margin: 0px !important;
- padding: 8px !important;
- font-weight: 500 !important;
- }
- // List styles
- ul {
- padding: 0px !important;
- li {
- padding: 0px !important;
- line-height: 14px;
- }
- }
-
- // Link styles
- a {
- display: block;
- color: $text-secondary !important;
-
- &:hover {
- color: $link-primary !important;
- text-decoration: none !important;
- }
-
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
-
- &:hover {
- color: $dark-link-primary !important;
- }
- }
- }
-
- // Dark mode
- [data-bs-theme=dark] & {
- color: $dark-text-primary;
- }
-}
-
-// tabs
-ul {
- &.nav-tabs {
- border-bottom: none !important;
-
- li {
- &.nav-item {
- margin: 0 !important;
-
- button {
- color: $text-primary !important;
-
- &.active {
- color: $link-primary !important;
- background-color: var(--td-pre-bg) !important;
- border-bottom-color: var(--td-pre-bg) !important;
- }
-
- // Add dark mode styles
- [data-bs-theme=dark] & {
- color: $dark-text-primary !important;
-
- &.active {
- color: $dark-link-primary !important;
- }
- }
- }
- }
- }
- }
-}
-
-.tab-content {
- > .tab-pane {
- border-radius: 0 $border-radius-medium $border-radius-medium $border-radius-medium !important;
- border: 1px solid $border-default !important;
-
- // Add dark mode styles
- [data-bs-theme=dark] & {
- border-color: $dark-border-default !important;
- }
- }
-}
-
-// Buttons Component
-// Base button styles
-.button-base {
- display: inline-flex;
- align-items: center;
- justify-content: center;
- font-size: 16px;
- line-height: 20px;
- font-weight: $font-weight-semibold;
- padding: 0 12px;
- min-height: 40px;
- border-radius: $border-radius-medium;
- transition: background-color 0.3s, color 0.3s;
- text-decoration: none !important;
- border: none;
-
- &:hover {
- text-decoration: none !important;
- }
-}
-
-// CTA Container styles
-.cta-container {
- display: flex;
- align-items: center; // Vertically center the buttons
- gap: 16px; // Consistent spacing between buttons
- margin: 24px 0;
- flex-wrap: wrap; // Allow wrapping on smaller screens
-}
-
-// CTA Button
-.cta-button {
- @extend .button-base;
- color: $moon-800 !important;
- background-color: $moon-100;
- height: 40px; // Fixed height for consistency
- padding: 0 16px;
- display: inline-flex;
- align-items: center; // Center button contents vertically
- white-space: nowrap; // Prevent text wrapping inside button
-
- &:hover {
- background-color: $hover-bg;
- }
-
- // Icon styles
- svg, img {
- width: 24px;
- height: 24px;
- margin-right: 8px;
- }
-
- [data-bs-theme=dark] & {
- color: $dark-text-primary !important;
- background-color: $dark-bg-secondary;
-
- &:hover {
- background-color: $dark-bg-tertiary;
- }
- }
-}
-
-// Contact Us Button
-.contact-us-button {
- @extend .button-base;
- background-color: $teal-500;
- color: $moon-white !important;
- gap: 8px;
-
- &:hover {
- background-color: $teal-450;
- }
-
- img {
- width: 112px;
- height: 24px;
- }
-
- p {
- margin: 0;
- color: $moon-white;
- }
-
- [data-bs-theme=dark] & {
- background-color: $teal-450;
-
- &:hover {
- background-color: $teal-400;
- }
- }
-}
-
-// Banners Component
-// Base banner styles
-.banner-base {
- display: flex;
- padding: 32px 40px;
- flex-direction: column;
- align-items: flex-start;
- gap: 24px;
- border-radius: $border-radius-large;
- margin-bottom: 30px;
-
- h1, h2, p, div {
- filter: drop-shadow(5px 5px 5px rgba(0, 0, 0, 0.5));
- }
-}
-
-// Standard Banner
-.banner {
- @extend .banner-base;
- background-position: center;
- background-repeat: no-repeat;
- background-size: cover;
- min-height: 200px;
- color: $moon-white;
- overflow: hidden;
-}
-
-// Help Banner
-.help-banner {
- @extend .banner-base;
- padding: 56px 40px 72px 40px;
- background: $moon-900;
- background-image: url('/images/support/support_banner.png');
- background-size: cover;
- background-position: center;
- box-shadow: none;
-
- &.card:hover {
- box-shadow: none;
- transform: none;
- }
-
- h2 {
- color: $moon-50;
- font-size: 40px;
- font-weight: $font-weight-semibold;
- font-family: "Source Sans Pro";
- line-height: 140%;
- }
-
- p {
- color: $moon-250;
- font-size: 20px;
- font-weight: $font-weight-regular;
- font-family: "Source Sans Pro";
- line-height: 140%;
- }
-}
-
-// Card Banner
-.card-banner {
- @extend .banner-base;
- align-items: center;
- background: $moon-50;
- color: $moon-800;
-
- h2 {
- font-size: 24px;
- font-weight: $font-weight-semibold;
- color: $moon-900;
- margin: 0;
- }
-
- p {
- font-size: 16px;
- font-weight: $font-weight-regular;
- margin: 0;
- }
-
- &.card-banner-gray {
- &:hover {
- box-shadow: none;
- transform: none;
- }
- }
-
- // Icon container
- &-icon {
- display: flex;
- width: 64px;
- height: 64px;
- justify-content: center;
- align-items: center;
- border-radius: 100px;
- background: rgba($teal-300, 0.48);
-
- img {
- width: 32px;
- height: 32px;
- }
- }
-
- [data-bs-theme=dark] & {
- background: $dark-bg-secondary;
- color: $dark-text-primary;
-
- h2 {
- color: $dark-text-primary;
- }
-
- p {
- color: $dark-text-secondary;
- }
- }
-}
-
-// Add border radius to all images, code blocks and alerts
-img, pre, .highlight, .code-toolbar, .alert {
- border-radius: $border-radius-medium !important;
-}
-
-// Style images and iframes consistently
-img, iframe {
- margin-top: 1rem;
- margin-bottom: 2rem;
- display: block;
-}
-
-.section-index {
- max-width: 80%;
-}
-
-.section-index .entry p {
- margin-top: 5px;
-}
-
-@media (min-width: $breakpoint-lg) {
- img, iframe {
- max-width: 80% !important;
- }
-}
-
-// Override section index heading font weight
-.section-index h5 a, .section-index .h5 a {
- font-size: 20px !important;
- font-weight: $font-weight-regular !important;
-}
-
-// Add dark mode styles using data-bs-theme attribute
-[data-bs-theme=dark] {
- body {
- color: $dark-text-primary;
- background-color: $dark-bg-primary;
- }
-
- // Update sidebar nav colors for dark mode
- .td-sidebar-nav {
- a {
- color: $dark-text-primary !important;
- &:hover {
- color: $dark-link-primary !important;
- }
- .td-sidebar-nav-active-item {
- color: $dark-link-primary !important;
- }
- }
- }
-
- // Update headings color for dark mode
- h1, h2, h3, h4, h5, h6 {
- color: $dark-text-primary;
- }
-
- // Update reading time color for dark mode
- .reading-time {
- color: $dark-text-secondary;
- }
-
- // Update table of contents color
- #TableOfContents {
- color: $dark-text-primary;
- }
-
- .td-footer {
- background-color: transparent !important;
- color: $dark-text-primary !important;
- border-top: 1px solid $moon-750 !important;
- }
-
- // Update border colors for dark mode
- .border,
- .border-top,
- .border-right,
- .border-bottom,
- .border-left {
- border-color: $dark-border-default !important;
-
- &:hover {
- border-color: $dark-border-hover !important;
- }
- }
-}
-
-// For light mode (default) borders
-.border,
-.border-top,
-.border-right,
-.border-bottom,
-.border-left {
- border-color: $border-default !important;
-
- &:hover {
- border-color: $border-hover !important;
- }
-}
-
-// Footer Component
-.td-footer {
- background-color: transparent !important;
- color: $text-primary !important;
- border-top: 1px solid $moon-200 !important;
- padding: 1.5rem 0 !important;
- min-height: auto !important;
- margin: 0 !important;
-
- // Container styles
- .container-fluid {
- padding: 0 2rem !important;
- }
-
- // Row alignment
- .row {
- align-items: center !important;
- }
-
- // Footer links
- &__links-item {
- margin-bottom: 0 !important;
- }
-
- // Links and icons
- a, i {
- color: inherit !important;
- transition: color 0.3s !important;
-
- &:hover {
- color: $link-hover !important;
- }
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- background-color: transparent !important;
- color: $dark-text-primary !important;
- border-top: 1px solid $moon-750 !important;
-
- a:hover,
- i:hover {
- color: $wandb-gold !important;
- }
- }
-}
-
-// Feedback Component
-.td-content > .d-print-none { // Target only the feedback section at bottom of content
- margin-top: 4rem;
- display: flex;
- align-items: center; // Change back to center for vertical alignment
- flex-wrap: wrap; // Allow wrapping on smaller screens
- gap: 8px; // Consistent spacing between elements
-
- // Hide feedback title
- .feedback--title {
- display: none;
- }
-
- // Question and buttons container
- .feedback--question {
- margin: 0;
- display: flex;
- align-items: center;
- gap: 16px;
- }
-
- // Buttons container
- .btn {
- min-width: 50px;
- display: flex;
- align-items: center;
- justify-content: center;
-
- &.feedback--answer-no {
- margin: 0 !important;
- }
- }
-
- // Response styling
- .feedback--response {
- width: 100%;
- margin-top: 1rem;
- }
-}
-
-// Last modified date with feedback links - mobile styles
-div.td-page-meta__lastmod {
- display: flex !important;
- flex-direction: column !important; // Stack elements vertically on mobile
- gap: 8px !important;
- color: $text-secondary !important;
- font-size: 14px !important;
- margin-top: 0 !important;
-
- // Container for last modified text and commit link
- &:first-child {
- display: flex !important;
- align-items: center !important;
- gap: 4px !important;
- }
-
- // Style the commit link
- a {
- color: $text-secondary !important;
- &:hover {
- color: $link-primary !important;
- }
- }
-
- .feedback--links {
- display: flex !important;
- margin-top: 8px !important;
- align-items: center !important;
- gap: 16px !important;
-
- // Style the edit/issue links
- a {
- color: $text-secondary !important;
- &:hover {
- color: $link-primary !important;
- }
- }
- }
-
- // Tablet and desktop styles
- @media (min-width: $breakpoint-lg) {
- flex-direction: row !important; // Switch to horizontal layout
- align-items: center !important;
- justify-content: space-between !important;
-
- .feedback--links {
- margin-top: 0 !important;
- margin-left: auto !important;
- }
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
-
- a {
- color: $dark-text-secondary !important;
- &:hover {
- color: $dark-link-primary !important;
- }
- }
- }
-}
-
-// Feedback buttons
-.feedback--answer {
- @extend .button-base;
- color: $moon-800 !important;
- background-color: $moon-100 !important;
- background-image: none !important;
- margin-right: 8px !important;
- box-shadow: none !important;
- display: inline-block !important;
- margin-bottom: 0 !important;
- border: none !important;
-
- &:hover {
- background-color: $hover-bg !important;
- text-decoration: none !important;
- border: none !important;
- box-shadow: none !important;
- }
-
- // Remove unwanted margin
- &.mb-4 {
- margin-bottom: 0 !important;
- }
-
- // Hide disabled buttons
- &:disabled {
- display: none !important;
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- color: $dark-text-primary !important;
- background-color: $dark-bg-secondary !important;
- border: none !important;
-
- &:hover {
- background-color: $dark-bg-tertiary !important;
- border: none !important;
- box-shadow: none !important;
- }
- }
-}
-
-// Remove feedback links from original location
-.td-content > .d-print-none .feedback--links {
- display: none !important;
-}
-
-// Update the sidebar navigation styles
-.td-sidebar-nav {
- font-family: "Source Sans Pro", sans-serif;
- padding-left: 15px !important;
-
- // Base link styles
- a {
- color: $text-secondary !important;
- font-weight: $font-weight-regular;
- text-decoration: none !important;
-
- &:hover {
- color: $link-primary !important;
- text-decoration: none !important;
- }
- }
-
- // Active item styles
- .td-sidebar-nav-active-item {
- color: $link-primary !important;
- font-weight: $font-weight-medium !important;
- text-decoration: none !important;
-
- &:hover {
- text-decoration: none !important;
- }
- }
-
- // Section title styles
- &__section-title {
- display: block;
- font-weight: $font-weight-regular !important;
- line-height: 19px;
- margin-bottom: 10px !important;
- margin-top: 5px !important;
-
- .active {
- font-weight: $font-weight-regular !important;
- }
-
- a {
- color: var(--bs-secondary-color);
- font-weight: $font-weight-regular !important;
- }
- }
-
- // Dark mode overrides
- [data-bs-theme=dark] & {
- a {
- color: $dark-text-secondary !important;
-
- &:hover {
- color: $dark-link-primary !important;
- }
- }
-
- .td-sidebar-nav-active-item {
- color: $dark-link-primary !important;
- }
- }
-}
-
-// Foldable Nav
-nav.foldable-nav .with-child, nav.foldable-nav .without-child {
- padding-left: 10px !important;
- }
- // Update existing chevron styles
- nav.foldable-nav .ul-1 .with-child {
- & > label {
- display: flex;
- justify-content: space-between;
- align-items: center;
- width: 100%;
- position: relative;
- padding-right: 20px; // Space for chevron
-
- // Remove default chevrons
- &:before {
- display: none !important;
- }
- }
- }
-
- // Add chevrons to all expandable items
- nav.foldable-nav .ul-1 .with-child > label:after {
- content: "\f105" !important; // fa-angle-right (thinner chevron)
- font-family: "Font Awesome 6 Free", "Font Awesome 5 Free";
- font-weight: 900;
- color: $moon-500;
- transition: color 0.3s;
- font-size: 14px;
- position: absolute;
- right: 0px;
- top: 35%;
- transform: translateY(-50%);
- }
-
- // Change chevron for expanded items
- nav.foldable-nav .ul-1 .with-child > input:checked + label:after {
- content: "\f107" !important; // fa-angle-down (thinner chevron)
- }
-
- // Hover states
- nav.foldable-nav .ul-1 .with-child > label:hover:after {
- color: $teal-600 !important;
- }
-
- // Dark mode hover state
- [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after {
- color: $wandb-gold !important;
- }
-
- // Dark mode hover state
- [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after {
- color: $wandb-gold !important;
- }
-
-
-// Theme-based logo visibility
-[data-bs-theme="dark"] {
- .theme-light-only {
- display: none;
- }
- .theme-dark-only {
- display: block;
- }
-}
-
-[data-bs-theme="light"] {
- .theme-light-only {
- display: block;
- }
- .theme-dark-only {
- display: none;
- }
-}
-
-
-// Add consistent padding for main content
-.td-main {
- main {
- &.ps-md-5 {
- padding-left: 2rem !important;
- padding-right: 2rem !important;
-
- // Tablet (768px and up)
- @media (min-width: $breakpoint-md) {
- padding-left: 3rem !important;
- padding-right: 3rem !important;
- }
- }
- }
-}
-
-// Google Search style overrides
-.gsc-control-cse, .gsc-webResult.gsc-result, .gsc-cursor-page, .gsc-refinementHeader, .gs-no-results-result div {
- background-color: transparent !important;
- border: 0px !important;
-}
-.gs-title {
- font-family: "Source Serif 4", sans-serif !important;
-}
-.gs-title b, .gs-title a, .gsc-cursor-page, .gsc-above-wrapper-area, .gcsc-find-more-on-google-query, .gcsc-find-more-on-google-magnifier {
- color: $link-primary !important;
- text-decoration: none !important;
- font-size: 18px !important;
- &:hover {
- color: $link-hover !important;
- text-decoration: none !important;
- }
- [data-bs-theme=dark] & {
- color: $dark-link-primary !important;
-
- &:hover {
- color: $dark-link-hover !important;
- }
- }
-}
-.gsc-above-wrapper-area a:hover{
- background-color: $bg-secondary !important;
-}
-.gsc-result-info, .gs-per-result-labels {
- font-family: "Source Sans Pro", sans-serif !important;
- font-size: 14px !important;
- color: $text-secondary !important;
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
- }
-}
-.gsc-refinementBlock span {
- margin-left: 10px !important;
- margin-right: 10px !important;
-}
-.gs-visibleUrl-breadcrumb {
- font-family: "Source Sans Pro", sans-serif !important;
- font-size: 14px !important;
- color: $text-tertiary !important;
- [data-bs-theme=dark] & {
- color: $wandb-gold !important;
- }
-}
-
-.gs-snippet, .gsc-refinementHeader {
- font-family: "Source Sans Pro", sans-serif !important;
- font-size: 16px !important;
- color: $text-primary !important;
- [data-bs-theme=dark] & {
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-}
-.gsc-above-wrapper-area {
- display: none !important;
-}
-body > div.container-fluid.td-default.td-outer > main > section > div > h2 {
- margin-left: 1.0rem !important;
-}
-.gs-per-result-labels {
- padding: 5px !important;
- border-radius: $border-radius-medium;
- font-size: 12px;
- max-width: fit-content;
-}
-.gs-per-result-labels span {
- display: none;
-}
-.gsc-inline-block:hover, .gsc-refinementhActive, .gs-per-result-labels {
- background-color: $bg-tertiary !important;
- [data-bs-theme=dark] & {
- background-color: $dark-bg-tertiary !important;
- }
-}
-.td-search__input, .form-control, .td-search-input
-{
- color: $text-tertiary !important;
- border: $text-tertiary !important;
- background-color: transparent !important;
- [data-bs-theme=dark] & {
- color: $dark-text-tertiary !important;
- border: $dark-text-tertiary !important;
- }
-}
-.gcsc-find-more-on-google {
- display: none !important;
-}
-
-// DocSearch Component
-.DocSearch {
- // Button styles
- &-Button {
- font-family: "Source Sans Pro", sans-serif;
- font-size: 16px;
- background: transparent !important;
- border: 1px solid $border-default !important;
-
- &:active,
- &:focus,
- &:hover {
- background: var(--docsearch-searchbox-focus-background);
- box-shadow: inset 0 0 0 2px $teal-500 !important;
- color: var(--docsearch-text-color);
- outline: none;
- border-color: $teal-500 !important;
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- border: 1px solid $dark-border-default !important;
-
- &:hover, &:focus, &:active {
- border-color: $teal-500 !important;
- box-shadow: inset 0 0 0 2px $teal-450 !important;
- }
- }
- }
-
- // Placeholder text color
- &-Button-Placeholder {
- color: $text-secondary !important;
-
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
- }
- }
-
- // Search icon color
- &-Search-Icon {
- color: $text-secondary !important;
-
- [data-bs-theme=dark] & {
- color: $dark-text-secondary !important;
- }
- }
-
- // Modal styles
- &-Modal {
- border-radius: $border-radius-large !important;
- }
-
- // Hide footer
- &-Footer {
- display: none !important;
- }
-}
-
-// DocSearch Container with CSS Variables
-.DocSearch-Container {
- font-family: "Source Sans Pro", sans-serif;
- font-size: 16px;
-
- // Light mode variables
- --docsearch-primary-color: #{$teal-500} !important;
- --docsearch-highlight-color: #{$teal-500} !important;
- --docsearch-text-color: #{$moon-800} !important;
- --docsearch-muted-color: #{$moon-500} !important;
- --docsearch-container-background: rgba(101, 108, 133, 0.8) !important;
- --docsearch-modal-background: #{$moon-white} !important;
- --docsearch-searchbox-background: #{$moon-100} !important;
- --docsearch-searchbox-focus-background: #{$moon-white} !important;
- --docsearch-hit-color: #{$moon-800} !important;
- --docsearch-hit-background: #{$moon-white} !important;
- --docsearch-hit-active-background: #{$teal-500} !important;
- --docsearch-footer-background: #{$moon-white} !important;
- --docsearch-logo-color: #{$teal-500} !important;
- --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-200}, #{$moon-100}) !important;
- --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-300} !important;
- --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-500} !important;
-
- .DocSearch-Modal {
- border-radius: 0 !important;
- max-width: 800px !important;
- }
-
- // Search form styles
- .DocSearch-Form {
- border-radius: 30px !important;
- }
-
- // Search results styles
- .DocSearch-Hit {
- border-radius: $border-radius-medium;
-
- > a {
- box-shadow: none !important;
- border-radius: $border-radius-medium;
- border: 1px solid $border-default !important;
-
- }
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
- --docsearch-primary-color: #{$teal-400} !important;
- --docsearch-highlight-color: #{$teal-400} !important;
- --docsearch-text-color: #{$moon-200} !important;
- --docsearch-muted-color: #{$moon-400} !important;
- --docsearch-container-background: rgba(21, 23, 31, 0.8) !important;
- --docsearch-modal-background: #{$moon-800} !important;
- --docsearch-searchbox-background: #{$moon-900} !important;
- --docsearch-searchbox-focus-background: #{$moon-900} !important;
- --docsearch-hit-color: #{$moon-200} !important;
- --docsearch-hit-background: #{$moon-800} !important;
- --docsearch-hit-active-background: #{$teal-400} !important;
- --docsearch-footer-background: #{$moon-800} !important;
- --docsearch-logo-color: #{$teal-400} !important;
- --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-800}, #{$moon-750}) !important;
- --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-700} !important;
- --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-400} !important;
- }
-
- .DocSearch-Hit-source {
- color: $text-secondary !important;
- font-size: 14px !important;
- }
-
- .DocSearch-Hit-title {
- font-size: 1rem !important;
- }
-
- // Dark mode styles
- [data-bs-theme=dark] & {
-
- .DocSearch-Modal {
- box-shadow: inset 1px 1px 0 0 hsla(0, 0%, 18%, 0.5), 0 3px 8px 0 $moon-900;
- }
-
- .DocSearch-Hit {
- > a {
- border: 1px solid $dark-border-default !important;
- }
- }
-
- .DocSearch-Hit-source {
- color: $dark-text-secondary !important;
- font-size: 14px !important;
- }
-
- .DocSearch-Hit-title {
- font-size: 1rem !important;
- }
- }
-}
-
-@media (min-width: $breakpoint-lg) {
- .DocSearch {
-
- .DocSearch-Modal {
- border-radius: $border-radius-large !important;
- }
- }
-}
diff --git a/blog.mdx b/blog.mdx
new file mode 100644
index 0000000000..a9fc4170fa
--- /dev/null
+++ b/blog.mdx
@@ -0,0 +1,4 @@
+---
+title: "W&B Tutorials & Blog"
+url: "https://wandb.ai/fully-connected"
+---
\ No newline at end of file
diff --git a/content/en/_includes/_index.md b/content/en/_includes/_index.md
deleted file mode 100644
index f26bedc3a6..0000000000
--- a/content/en/_includes/_index.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-headless: true
-cascade:
- headless: true
----
\ No newline at end of file
diff --git a/content/en/_includes/enterprise-cloud-only.md b/content/en/_includes/enterprise-cloud-only.md
deleted file mode 100644
index 79dfef4358..0000000000
--- a/content/en/_includes/enterprise-cloud-only.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This feature requires a [Pro or Enterprise plan](https://wandb.ai/site/pricing/).
-
diff --git a/content/en/_includes/enterprise-only.md b/content/en/_includes/enterprise-only.md
deleted file mode 100644
index f9f25d8b80..0000000000
--- a/content/en/_includes/enterprise-only.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This feature is available for all [Enterprise](https://wandb.ai/site/pricing/) licenses.
-
diff --git a/content/en/_includes/metric-naming-examples.md b/content/en/_includes/metric-naming-examples.md
deleted file mode 100644
index 35c37f22d4..0000000000
--- a/content/en/_includes/metric-naming-examples.md
+++ /dev/null
@@ -1,15 +0,0 @@
-**Valid metric names:**
-```python
-with wandb.init() as run:
- run.log({"accuracy": 0.9, "val_loss": 0.1, "epoch_5": 5})
- run.log({"modelAccuracy": 0.95, "learning_rate": 0.001})
-```
-
-**Invalid metric names (avoid these):**
-```python
-with wandb.init() as run:
- run.log({"acc,val": 0.9}) # Contains comma
- run.log({"loss-train": 0.1}) # Contains hyphen
- run.log({"test acc": 0.95}) # Contains space
- run.log({"5_fold_cv": 0.8}) # Starts with number
-```
diff --git a/content/en/_includes/metric-naming-rules.md b/content/en/_includes/metric-naming-rules.md
deleted file mode 100644
index 1ea9c267f2..0000000000
--- a/content/en/_includes/metric-naming-rules.md
+++ /dev/null
@@ -1,3 +0,0 @@
-* **Allowed characters**: Letters (A-Z, a-z), digits (0-9), and underscores (_)
-* **Starting character**: Names must start with a letter or underscore
-* **Pattern**: Metric names should match `/^[_a-zA-Z][_a-zA-Z0-9]*$/`
\ No newline at end of file
diff --git a/content/en/_includes/project-visibility-settings.md b/content/en/_includes/project-visibility-settings.md
deleted file mode 100644
index 1fdea11bce..0000000000
--- a/content/en/_includes/project-visibility-settings.md
+++ /dev/null
@@ -1,15 +0,0 @@
-1. In the W&B App, from any page in the project, click **Overview** in the left navigation.
-1. At the top right, click **Edit**.
-1. Choose a new value for **Project visibility**:
-
- - **Team** (default): Only your team can view and edit the project.
- - **Restricted**: Only invited members can access the project, and public access is turned off.
- - **Open**: Anyone can submit runs or create reports, but only your team can edit it. Appropriate only for classroom settings, public benchmark competitions, or other non-durable contexts.
- - **Public**: Anyone can view the project, but only your team can edit it.
-
- {{% alert %}}
- If your W&B admins have turned off **Public** visibility, you cannot choose it. Instead, you can share a view-only [W&B Report]({{< relref "/guides/core/reports/collaborate-on-reports.md#share-a-report" >}}), or contact your W&B organization's admins for assistance.
- {{% /alert %}}
-1. Click **Save**.
-
-If you update a project to a more strict privacy setting, you may need to re-invite individual users to restore their ability to access the project.
\ No newline at end of file
diff --git a/content/en/_includes/public-api-use.md b/content/en/_includes/public-api-use.md
deleted file mode 100644
index ca360bef77..0000000000
--- a/content/en/_includes/public-api-use.md
+++ /dev/null
@@ -1 +0,0 @@
-> Training and fine-tuning models is done elsewhere in [the W&B Python SDK]({{< relref "/ref/python" >}}). Use the Public API for querying and managing data *after* it has been logged to W&B.
diff --git a/content/en/_includes/release-notes-support-eol-reminder.md b/content/en/_includes/release-notes-support-eol-reminder.md
deleted file mode 100644
index 4330765d5a..0000000000
--- a/content/en/_includes/release-notes-support-eol-reminder.md
+++ /dev/null
@@ -1,3 +0,0 @@
-A W&B Server release is supported for 12 months from its initial release date. As a reminder, customers using [Self-managed](/guides/hosting/hosting-options/self-managed/) are responsible to upgrade to a [supported release](/ref/releases-notes/) in time to maintain support.
-
-Refer to [Release policies and processes](/ref/release-notes/release-policies/). For assistance or questions, contact [support](mailto:support@wandb.com).
\ No newline at end of file
diff --git a/content/en/_includes/service-account-benefits.md b/content/en/_includes/service-account-benefits.md
deleted file mode 100644
index cb0bcf0544..0000000000
--- a/content/en/_includes/service-account-benefits.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Key benefits of service accounts:
-- **No license consumption**: Service accounts do not consume user seats or licenses
-- **Dedicated API keys**: Secure credentials for automated workflows
-- **User attribution**: Optionally attribute automated runs to human users
-- **Enterprise-ready**: Built for production automation at scale
-- **Delegated operations**: Service accounts operate on behalf of the user or organization that creates them
\ No newline at end of file
diff --git a/content/en/_includes/unsupported_server_version.md b/content/en/_includes/unsupported_server_version.md
deleted file mode 100644
index 95c709f1a5..0000000000
--- a/content/en/_includes/unsupported_server_version.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This release is no longer supported. A major release and its patches are supported for 12 months from the initial release date.
-
-Customers using [Self-managed](/guides/hosting/hosting-options/self-managed/) are responsible to upgrade to a [supported release](/ref/releases-notes/) in time to maintain support. For assistance or questions, contact [support](mailto:support@wandb.com).
\ No newline at end of file
diff --git a/content/en/_index.md b/content/en/_index.md
deleted file mode 100644
index a303b77bb4..0000000000
--- a/content/en/_index.md
+++ /dev/null
@@ -1,122 +0,0 @@
----
-title: Weights & Biases Documentation
----
-
-
-
-{{< banner title="Weights & Biases Documentation" background="/images/support/support_banner.png" >}}
-Choose the product for which you need documentation.
-{{< /banner >}}
-
-
-
-### Develop AI models
-
-Use [W&B Models]({{< relref "/guides/" >}}) to manage AI model development. Features include training, fine-tuning, reporting, automating hyperparameter sweeps, and utilizing the model registry for versioning and reproducibility.
-
-- [Introduction]({{< relref "/guides/" >}})
-- [Quickstart]({{< relref "/guides/quickstart/" >}})
-- [YouTube Tutorial](https://www.youtube.com/watch?v=tHAFujRhZLA)
-- [Online Course](https://wandb.ai/site/courses/101/)
-
-
-
-### Use AI models in your app
-
-Use [W&B Weave](https://weave-docs.wandb.ai/) to manage AI models in your code. Features include tracing, output evaluation, cost estimates, and a hosted inference service and playground for comparing different large language models (LLMs) and settings.
-
-- [Introduction](https://weave-docs.wandb.ai/)
-- [Quickstart](https://weave-docs.wandb.ai/quickstart)
-- [YouTube Demo](https://www.youtube.com/watch?v=IQcGGNLN3zo)
-- [Try the Playground](https://weave-docs.wandb.ai/guides/tools/playground/)
-- [Use Weave in your W&B runs]({{< relref "/guides/weave/set-up-weave" >}})
-
-
-
-### Access foundation models
-
-Use [W&B Inference]({{< relref "/guides/inference/" >}}) to access leading open-source foundation models through an OpenAI-compatible API. Features include multiple model options, usage tracking, and integration with Weave for tracing and evaluation.
-
-- [Introduction]({{< relref "/guides/inference/" >}})
-- [Available Models]({{< relref "/guides/inference/models/" >}})
-- [API Reference]({{< relref "/guides/inference/api-reference/" >}})
-- [Try in Playground](https://wandb.ai/inference)
-
-
-
-### Post-train your models
-
-Now in public preview, use [W&B Training]({{< relref "/guides/training/" >}}) to post-train large language models using serverless reinforcement learning (RL). Features include fully managed GPU infrastructure, integration with ART and RULER, and automatic scaling for multi-turn agentic tasks.
-
-- [Introduction]({{< relref "/guides/training/" >}})
-- [Prerequisites]({{< relref "/guides/training/prerequisites/" >}})
-- [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}})
-- [API Reference]({{< relref "/ref/training" >}})
-
-
{{% /card %}}
-{{< /cardpane >}}
-
-
-
-
-
-
-
diff --git a/content/en/guides/_index.md b/content/en/guides/_index.md
deleted file mode 100644
index 62ae807b51..0000000000
--- a/content/en/guides/_index.md
+++ /dev/null
@@ -1,58 +0,0 @@
----
-description: An overview of W&B and how to get started.
-menu:
- default:
- identifier: guides
- weight: 1
-type: docs
-cascade:
- type: docs
-title: Guides
-no_list: true
----
-
-## What is W&B?
-
-W&B is the AI developer platform, with tools for training models, fine-tuning models, and leveraging foundation models.
-
-{{< img src="/images/general/architecture.png" alt="W&B platform architecture diagram" >}}
-
-W&B consists of three major components: [Models]({{< relref "/guides/models.md" >}}), [Weave](https://wandb.github.io/weave/), and [Core]({{< relref "/guides/core/" >}}):
-
-**[W&B Models]({{< relref "/guides/models/" >}})** is a set of lightweight, interoperable tools for machine learning practitioners training and fine-tuning models.
-- [Experiments]({{< relref "/guides/models/track/" >}}): Machine learning experiment tracking
-- [Sweeps]({{< relref "/guides/models/sweeps/" >}}): Hyperparameter tuning and model optimization
-- [Registry]({{< relref "/guides/core/registry/" >}}): Publish and share your ML models and datasets
-
-**[W&B Weave]({{< relref "/guides/weave/" >}})** is a lightweight toolkit for tracking and evaluating LLM applications.
-
-**[W&B Core]({{< relref "/guides/core/" >}})** is set of powerful building blocks for tracking and visualizing data and models, and communicating results.
-- [Artifacts]({{< relref "/guides/core/artifacts/" >}}): Version assets and track lineage
-- [Tables]({{< relref "/guides/models/tables/" >}}): Visualize and query tabular data
-- [Reports]({{< relref "/guides/core/reports/" >}}): Document and collaborate on your discoveries
-
-**[W&B Inference]({{< relref "/guides/inference/" >}})** is a set of tools for accessing open-source foundation models through W&B Weave and an OpenAI-compatible API.
-
-**[W&B Training]({{< relref "/guides/training/" >}})** provides serverless reinforcement learning for post-training LLMs to improve reliability on multi-turn agentic tasks.
-
-{{% alert %}}
-Learn about recent releases in the [W&B release notes]({{< relref "/ref/release-notes/" >}}).
-{{% /alert %}}
-
-## How does W&B work?
-
-Read the following sections in this order if you are a first-time user of W&B and you are interested in training, tracking, and visualizing machine learning models and experiments:
-
-1. Learn about [runs]({{< relref "/guides/models/track/runs/" >}}), W&B's basic unit of computation.
-2. Create and track machine learning experiments with [Experiments]({{< relref "/guides/models/track/" >}}).
-3. Discover W&B's flexible and lightweight building block for dataset and model versioning with [Artifacts]({{< relref "/guides/core/artifacts/" >}}).
-4. Automate hyperparameter search and explore the space of possible models with [Sweeps]({{< relref "/guides/models/sweeps/" >}}).
-5. Manage the model lifecycle from training to production with [Registry]({{< relref "/guides/core/registry/" >}}).
-6. Visualize predictions across model versions with our [Data Visualization]({{< relref "/guides/models/tables/" >}}) guide.
-7. Organize runs, embed and automate visualizations, describe your findings, and share updates with collaborators with [Reports]({{< relref "/guides/core/reports/" >}}).
-
-
-
-## Are you a first-time user of W&B?
-
-Try the [quickstart]({{< relref "/guides/quickstart/" >}}) to learn how to install W&B and how to add W&B to your code.
\ No newline at end of file
diff --git a/content/en/guides/core/_index.md b/content/en/guides/core/_index.md
deleted file mode 100644
index da95cd63f0..0000000000
--- a/content/en/guides/core/_index.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-menu:
- default:
- identifier: core
-title: W&B Core
-weight: 70
-no_list: true
----
-
-W&B Core is the foundational framework supporting [W&B Models]({{< relref "/guides/models/" >}}) and [W&B Weave]({{< relref "/guides/weave/" >}}), and is itself supported by the [W&B Platform]({{< relref "/guides/hosting/" >}}).
-
-{{< img src="/images/general/core.png" alt="W&B Core framework diagram" >}}
-
-W&B Core provides capabilities across the entire ML lifecycle. With W&B Core, you can:
-
-- [Version and manage ML]({{< relref "/guides/core/artifacts/" >}}) pipelines with full lineage tracing for easy auditing and reproducibility.
-- Explore and evaluate data and metrics using [interactive, configurable visualizations]({{< relref "/guides/models/tables/" >}}).
-- [Document and share]({{< relref "/guides/core/reports/" >}}) insights across the entire organization by generating live reports in digestible, visual formats that are easily understood by non-technical stakeholders.
-- [Query and create visualizations of your data]({{< relref "/guides/models/app/features/panels/query-panels/" >}}) that serve your custom needs.
-- [Protect sensitive strings using secrets]({{< relref "/guides/core/secrets.md" >}}).
-- Configure automations that trigger key workflows for [model CI/CD]({{< relref "/guides/core/automations/" >}}).
diff --git a/content/en/guides/core/artifacts/_index.md b/content/en/guides/core/artifacts/_index.md
deleted file mode 100644
index d8ae8a76e1..0000000000
--- a/content/en/guides/core/artifacts/_index.md
+++ /dev/null
@@ -1,93 +0,0 @@
----
-description: Overview of W&B Artifacts, how they work, and how to get started using them.
-menu:
- default:
- identifier: artifacts
- parent: core
-title: Artifacts
-url: guides/artifacts
-cascade:
-- url: guides/artifacts/:filename
-weight: 1
----
-
-
-{{< cta-button productLink="https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Artifact_fundamentals.ipynb" >}}
-
-Use W&B Artifacts to track and version data as the inputs and outputs of your [W&B Runs]({{< relref "/guides/models/track/runs/" >}}). For example, a model training run might take in a dataset as input and produce a trained model as output. You can log hyperparameters, metadata, and metrics to a run, and you can use an artifact to log, track, and version the dataset used to train the model as input and another artifact for the resulting model checkpoints as output.
-
-## Use cases
-You can use artifacts throughout your entire ML workflow as inputs and outputs of [runs]({{< relref "/guides/models/track/runs/" >}}). You can use datasets, models, or even other artifacts as inputs for processing.
-
-{{< img src="/images/artifacts/artifacts_landing_page2.png" >}}
-
-| Use Case | Input | Output |
-|------------------------|-----------------------------|------------------------------|
-| Model Training | Dataset (training and validation data) | Trained Model |
-| Dataset Pre-Processing | Dataset (raw data) | Dataset (pre-processed data) |
-| Model Evaluation | Model + Dataset (test data) | [W&B Table]({{< relref "/guides/models/tables/" >}}) |
-| Model Optimization | Model | Optimized Model |
-
-
-{{% alert %}}
-The proceeding code snippets are meant to be run in order.
-{{% /alert %}}
-
-## Create an artifact
-
-Create an artifact with four lines of code:
-1. Create a [W&B run]({{< relref "/guides/models/track/runs/" >}}).
-2. Create an artifact object with the [`wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md" >}}) API.
-3. Add one or more files, such as a model file or dataset, to your artifact object.
-4. Log your artifact to W&B.
-
-For example, the proceeding code snippet shows how to log a file called `dataset.h5` to an artifact called `example_artifact`:
-
-```python
-import wandb
-
-run = wandb.init(project="artifacts-example", job_type="add-dataset")
-artifact = wandb.Artifact(name="example_artifact", type="dataset")
-artifact.add_file(local_path="./dataset.h5", name="training_dataset")
-artifact.save()
-
-# Logs the artifact version "my_data" as a dataset with data from dataset.h5
-```
-
-- The `type` of the artifact affects how it appears in the W&B platform. If you do not specify a `type`, it defaults to `unspecified`.
-- Each label of the dropdown represents a different `type` parameter value. In the above code snippet, the artifact's `type` is `dataset`.
-
-{{% alert %}}
-See the [track external files]({{< relref "./track-external-files.md" >}}) page for information on how to add references to files or directories stored in external object storage, like an Amazon S3 bucket.
-{{% /alert %}}
-
-## Download an artifact
-Indicate the artifact you want to mark as input to your run with the [`use_artifact`]({{< relref "/ref/python/experiments/run.md#use_artifact" >}}) method.
-
-Following the preceding code snippet, this next code block shows how to use the `training_dataset` artifact:
-
-```python
-artifact = run.use_artifact(
- "training_dataset:latest"
-) # returns a run object using the "my_data" artifact
-```
-This returns an artifact object.
-
-Next, use the returned object to download all contents of the artifact:
-
-```python
-datadir = (
- artifact.download()
-) # downloads the full `my_data` artifact to the default directory.
-```
-
-{{% alert %}}
-You can pass a custom path into the `root` [parameter]({{< relref "/ref/python/experiments/artifact.md" >}}) to download an artifact to a specific directory. For alternate ways to download artifacts and to see additional parameters, see the guide on [downloading and using artifacts]({{< relref "./download-and-use-an-artifact.md" >}}).
-{{% /alert %}}
-
-
-## Next steps
-* Learn how to [version]({{< relref "./create-a-new-artifact-version.md" >}}) and [update]({{< relref "./update-an-artifact.md" >}}) artifacts.
-* Learn how to trigger downstream workflows or notify a Slack channel in response to changes to your artifacts with [automations]({{< relref "/guides/core/automations/" >}}).
-* Learn about the [registry]({{< relref "/guides/core/registry/" >}}), a space that houses trained models.
-* Explore the [Python SDK]({{< relref "/ref/python/experiments/artifact.md" >}}) and [CLI]({{< relref "/ref/cli/wandb-artifact/" >}}) reference guides.
diff --git a/content/en/guides/core/artifacts/artifacts-walkthrough.md b/content/en/guides/core/artifacts/artifacts-walkthrough.md
deleted file mode 100644
index dbee141b1a..0000000000
--- a/content/en/guides/core/artifacts/artifacts-walkthrough.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-description: >-
- Artifacts quickstart shows how to create, track, and use a dataset artifact
- with W&B.
-displayed_sidebar: default
-title: "Tutorial: Create, track, and use a dataset artifact"
----
-This walkthrough demonstrates how to create, track, and use a dataset artifact from [W&B Runs]({{< relref "/guides/models/track/runs/" >}}).
-
-## 1. Log into W&B
-
-Import the W&B library and log in to W&B. You will need to sign up for a free W&B account if you have not done so already.
-
-```python
-import wandb
-
-wandb.login()
-```
-
-## 2. Initialize a run
-
-Use the [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) API to generate a background process to sync and log data as a W&B Run. Provide a project name and a job type:
-
-```python
-# Create a W&B Run. Here we specify 'dataset' as the job type since this example
-# shows how to create a dataset artifact.
-run = wandb.init(project="artifacts-example", job_type="upload-dataset")
-```
-
-## 3. Create an artifact object
-
-Create an artifact object with the [`wandb.Artifact()`]({{< relref "/ref/python/experiments/artifact.md" >}}) API. Provide a name for the artifact and a description of the file type for the `name` and `type` parameters, respectively.
-
-For example, the following code snippet demonstrates how to create an artifact called `‘bicycle-dataset’` with a `‘dataset’` label:
-
-```python
-artifact = wandb.Artifact(name="bicycle-dataset", type="dataset")
-```
-
-For more information about how to construct an artifact, see [Construct artifacts]({{< relref "./construct-an-artifact.md" >}}).
-
-## Add the dataset to the artifact
-
-Add a file to the artifact. Common file types include models and datasets. The following example adds a dataset named `dataset.h5` that is saved locally on our machine to the artifact:
-
-```python
-# Add a file to the artifact's contents
-artifact.add_file(local_path="dataset.h5")
-```
-
-Replace the filename `dataset.h5` in the preceding code snippet with the path to the file you want to add to the artifact.
-
-## 4. Log the dataset
-
-Use the W&B run objects `log_artifact()` method to both save your artifact version and declare the artifact as an output of the run.
-
-```python
-# Save the artifact version to W&B and mark it
-# as the output of this run
-run.log_artifact(artifact)
-```
-
-A `'latest'` alias is created by default when you log an artifact. For more information about artifact aliases and versions, see [Create a custom alias]({{< relref "./create-a-custom-alias.md" >}}) and [Create new artifact versions]({{< relref "./create-a-new-artifact-version.md" >}}), respectively.
-
-## 5. Download and use the artifact
-
-The following code example demonstrates the steps you can take to use an artifact you have logged and saved to the W&B servers.
-
-1. First, initialize a new run object with **`wandb.init()`.**
-2. Second, use the run objects [`use_artifact()`]({{< relref "/ref/python/experiments/run.md#use_artifact" >}}) method to tell W&B what artifact to use. This returns an artifact object.
-3. Third, use the artifacts [`download()`]({{< relref "/ref/python/experiments/artifact.md#download" >}}) method to download the contents of the artifact.
-
-```python
-# Create a W&B Run. Here we specify 'training' for 'type'
-# because we will use this run to track training.
-run = wandb.init(project="artifacts-example", job_type="training")
-
-# Query W&B for an artifact and mark it as input to this run
-artifact = run.use_artifact("bicycle-dataset:latest")
-
-# Download the artifact's contents
-artifact_dir = artifact.download()
-```
-
-Alternatively, you can use the Public API (`wandb.Api`) to export (or update data) data already saved in a W&B outside of a Run. See [Track external files]({{< relref "./track-external-files.md" >}}) for more information.
diff --git a/content/en/guides/core/artifacts/construct-an-artifact.md b/content/en/guides/core/artifacts/construct-an-artifact.md
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index 57430a7c73..0000000000
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----
-description: Create, construct a W&B Artifact. Learn how to add one or more files
- or a URI reference to an Artifact.
-menu:
- default:
- identifier: construct-an-artifact
- parent: artifacts
-title: Create an artifact
-weight: 2
----
-
-Use the W&B Python SDK to construct artifacts from [W&B Runs]({{< relref "/ref/python/experiments/run.md" >}}). You can add [files, directories, URIs, and files from parallel runs to artifacts]({{< relref "#add-files-to-an-artifact" >}}). After you add a file to an artifact, save the artifact to the W&B Server or [your own private server]({{< relref "/guides/hosting/hosting-options/self-managed.md" >}}).
-
-For information on how to track external files, such as files stored in Amazon S3, see the [Track external files]({{< relref "./track-external-files.md" >}}) page.
-
-## How to construct an artifact
-
-Construct a [W&B Artifact]({{< relref "/ref/python/experiments/artifact.md" >}}) in three steps:
-
-### 1. Create an artifact Python object with `wandb.Artifact()`
-
-Initialize the [`wandb.Artifact()`]({{< relref "/ref/python/experiments/artifact.md" >}}) class to create an artifact object. Specify the following parameters:
-
-* **Name**: Specify a name for your artifact. The name should be unique, descriptive, and easy to remember. Use an artifacts name to both: identify the artifact in the W&B App UI and when you want to use that artifact.
-* **Type**: Provide a type. The type should be simple, descriptive and correspond to a single step of your machine learning pipeline. Common artifact types include `'dataset'` or `'model'`.
-
-
-{{% alert %}}
-The "name" and "type" you provide is used to create a directed acyclic graph. This means you can view the lineage of an artifact on the W&B App.
-
-See the [Explore and traverse artifact graphs]({{< relref "./explore-and-traverse-an-artifact-graph.md" >}}) for more information.
-{{% /alert %}}
-
-
-{{% alert color="secondary" %}}
-Artifacts can not have the same name, even if you specify a different type for the types parameter. In other words, you can not create an artifact named `cats` of type `dataset` and another artifact with the same name of type `model`.
-{{% /alert %}}
-
-You can optionally provide a description and metadata when you initialize an artifact object. For more information on available attributes and parameters, see the [`wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md" >}}) Class definition in the Python SDK Reference Guide.
-
-The proceeding example demonstrates how to create a dataset artifact:
-
-```python
-import wandb
-
-artifact = wandb.Artifact(name="", type="")
-```
-
-Replace the string arguments in the preceding code snippet with your own name and type.
-
-### 2. Add one more files to the artifact
-
-Add files, directories, external URI references (such as Amazon S3) and more with artifact methods. For example, to add a single text file, use the [`add_file`]({{< relref "/ref/python/experiments/artifact.md#add_file" >}}) method:
-
-```python
-artifact.add_file(local_path="hello_world.txt", name="optional-name")
-```
-
-You can also add multiple files with the [`add_dir`]({{< relref "/ref/python/experiments/artifact.md#add_dir" >}}) method. To add files, see [Update an artifact]({{< relref "./update-an-artifact.md" >}}).
-
-### 3. Save your artifact to the W&B server
-
-Finally, save your artifact to the W&B server. Artifacts are associated with a run. Therefore, use a run objects [`log_artifact()`]({{< relref "/ref/python/experiments/run.md#log_artifact" >}}) method to save the artifact.
-
-```python
-# Create a W&B Run. Replace 'job-type'.
-run = wandb.init(project="artifacts-example", job_type="job-type")
-
-run.log_artifact(artifact)
-```
-
-{{% alert title="When to use Artifact.save() or wandb.Run.log_artifact()" %}}
-- Use `Artifact.save()` to update an existing artifact without creating a new run.
-- Use `wandb.Run.log_artifact()` to create a new artifact and associate it with a specific run.
-{{% /alert %}}
-
-{{% alert color="secondary" %}}
-Calls to `log_artifact` are performed asynchronously for performant uploads. This can cause surprising behavior when logging artifacts in a loop. For example:
-
-```python
-for i in range(10):
- a = wandb.Artifact(
- "race",
- type="dataset",
- metadata={
- "index": i,
- },
- )
- # ... add files to artifact a ...
- run.log_artifact(a)
-```
-
-The artifact version **v0** is NOT guaranteed to have an index of 0 in its metadata, as the artifacts may be logged in an arbitrary order.
-{{% /alert %}}
-
-## Add files to an artifact
-
-The following sections demonstrate how to construct artifacts with different file types and from parallel runs.
-
-For the following examples, assume you have a project directory with multiple files and a directory structure:
-
-```
-project-directory
-|-- images
-| |-- cat.png
-| +-- dog.png
-|-- checkpoints
-| +-- model.h5
-+-- model.h5
-```
-
-### Add a single file
-
-The proceeding code snippet demonstrates how to add a single, local file to your artifact:
-
-```python
-# Add a single file
-artifact.add_file(local_path="path/file.format")
-```
-
-For example, suppose you had a file called `'file.txt'` in your working local directory.
-
-```python
-artifact.add_file("path/file.txt") # Added as `file.txt'
-```
-
-The artifact now has the following content:
-
-```
-file.txt
-```
-
-Optionally, pass the desired path within the artifact for the `name` parameter.
-
-```python
-artifact.add_file(local_path="path/file.format", name="new/path/file.format")
-```
-
-The artifact is stored as:
-
-```
-new/path/file.txt
-```
-
-| API Call | Resulting artifact |
-| --------------------------------------------------------- | ------------------ |
-| `artifact.add_file('model.h5')` | model.h5 |
-| `artifact.add_file('checkpoints/model.h5')` | model.h5 |
-| `artifact.add_file('model.h5', name='models/mymodel.h5')` | models/mymodel.h5 |
-
-### Add multiple files
-
-The proceeding code snippet demonstrates how to add an entire, local directory to your artifact:
-
-```python
-# Recursively add a directory
-artifact.add_dir(local_path="path/file.format", name="optional-prefix")
-```
-
-The proceeding API calls produce the proceeding artifact content:
-
-| API Call | Resulting artifact |
-| ------------------------------------------- | ------------------------------------------------------ |
-| `artifact.add_dir('images')` |
|
-| `artifact.new_file('hello.txt')` | `hello.txt` |
-
-### Add a URI reference
-
-Artifacts track checksums and other information for reproducibility if the URI has a scheme that W&B library knows how to handle.
-
-Add an external URI reference to an artifact with the [`add_reference`]({{< relref "/ref/python/experiments/artifact.md#add_reference" >}}) method. Replace the `'uri'` string with your own URI. Optionally pass the desired path within the artifact for the name parameter.
-
-```python
-# Add a URI reference
-artifact.add_reference(uri="uri", name="optional-name")
-```
-
-Artifacts currently support the following URI schemes:
-
-* `http(s)://`: A path to a file accessible over HTTP. The artifact will track checksums in the form of etags and size metadata if the HTTP server supports the `ETag` and `Content-Length` response headers.
-* `s3://`: A path to an object or object prefix in S3. The artifact will track checksums and versioning information (if the bucket has object versioning enabled) for the referenced objects. Object prefixes are expanded to include the objects under the prefix, up to a maximum of 10,000 objects.
-* `gs://`: A path to an object or object prefix in GCS. The artifact will track checksums and versioning information (if the bucket has object versioning enabled) for the referenced objects. Object prefixes are expanded to include the objects under the prefix, up to a maximum of 10,000 objects.
-
-The proceeding API calls will produce the proceeding artifacts:
-
-| API call | Resulting artifact contents |
-| ----------------------------------------------------------------------------- | -------------------------------------------------------------------- |
-| `artifact.add_reference('s3://my-bucket/model.h5')` | `model.h5` |
-| `artifact.add_reference('s3://my-bucket/checkpoints/model.h5')` | `model.h5` |
-| `artifact.add_reference('s3://my-bucket/model.h5', name='models/mymodel.h5')` | `models/mymodel.h5` |
-| `artifact.add_reference('s3://my-bucket/images')` |
|
-
-### Add files to artifacts from parallel runs
-
-For large datasets or distributed training, multiple parallel runs might need to contribute to a single artifact.
-
-```python
-import wandb
-import time
-
-# We will use ray to launch our runs in parallel
-# for demonstration purposes. You can orchestrate
-# your parallel runs however you want.
-import ray
-
-ray.init()
-
-artifact_type = "dataset"
-artifact_name = "parallel-artifact"
-table_name = "distributed_table"
-parts_path = "parts"
-num_parallel = 5
-
-# Each batch of parallel writers should have its own
-# unique group name.
-group_name = "writer-group-{}".format(round(time.time()))
-
-
-@ray.remote
-def train(i):
- """
- Our writer job. Each writer will add one image to the artifact.
- """
- with wandb.init(group=group_name) as run:
- artifact = wandb.Artifact(name=artifact_name, type=artifact_type)
-
- # Add data to a wandb table. In this case we use example data
- table = wandb.Table(columns=["a", "b", "c"], data=[[i, i * 2, 2**i]])
-
- # Add the table to folder in the artifact
- artifact.add(table, "{}/table_{}".format(parts_path, i))
-
- # Upserting the artifact creates or appends data to the artifact
- run.upsert_artifact(artifact)
-
-
-# Launch your runs in parallel
-result_ids = [train.remote(i) for i in range(num_parallel)]
-
-# Join on all the writers to make sure their files have
-# been added before finishing the artifact.
-ray.get(result_ids)
-
-# Once all the writers are finished, finish the artifact
-# to mark it ready.
-with wandb.init(group=group_name) as run:
- artifact = wandb.Artifact(artifact_name, type=artifact_type)
-
- # Create a "PartitionTable" pointing to the folder of tables
- # and add it to the artifact.
- artifact.add(wandb.data_types.PartitionedTable(parts_path), table_name)
-
- # Finish artifact finalizes the artifact, disallowing future "upserts"
- # to this version.
- run.finish_artifact(artifact)
-```
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/create-a-custom-alias.md b/content/en/guides/core/artifacts/create-a-custom-alias.md
deleted file mode 100644
index b6c0fcf20e..0000000000
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----
-description: Create custom aliases for W&B Artifacts.
-menu:
- default:
- identifier: create-a-custom-alias
- parent: artifacts
-title: Create an artifact alias
-weight: 5
----
-
-Use aliases as pointers to specific versions. By default, `Run.log_artifact` adds the `latest` alias to the logged version.
-
-An artifact version `v0` is created and attached to your artifact when you log an artifact for the first time. W&B checksums the contents when you log again to the same artifact. If the artifact changed, W&B saves a new version `v1`.
-
-For example, if you want your training script to pull the most recent version of a dataset, specify `latest` when you use that artifact. The proceeding code example demonstrates how to download a recent dataset artifact named `bike-dataset` that has an alias, `latest`:
-
-```python
-import wandb
-
-run = wandb.init(project="")
-
-artifact = run.use_artifact("bike-dataset:latest")
-
-artifact.download()
-```
-
-You can also apply a custom alias to an artifact version. For example, if you want to mark that model checkpoint is the best on the metric AP-50, you could add the string `'best-ap50'` as an alias when you log the model artifact.
-
-```python
-artifact = wandb.Artifact("run-3nq3ctyy-bike-model", type="model")
-artifact.add_file("model.h5")
-run.log_artifact(artifact, aliases=["latest", "best-ap50"])
-```
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/create-a-new-artifact-version.md b/content/en/guides/core/artifacts/create-a-new-artifact-version.md
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index 07d64c2b8e..0000000000
--- a/content/en/guides/core/artifacts/create-a-new-artifact-version.md
+++ /dev/null
@@ -1,270 +0,0 @@
----
-description: Create a new artifact version from a single run or from a distributed
- process.
-menu:
- default:
- identifier: create-a-new-artifact-version
- parent: artifacts
-title: Create an artifact version
-weight: 6
----
-
-Create a new artifact version with a single [run]({{< relref "/guides/models/track/runs/" >}}) or collaboratively with distributed runs. You can optionally create a new artifact version from a previous version known as an [incremental artifact]({{< relref "#create-a-new-artifact-version-from-an-existing-version" >}}).
-
-{{% alert %}}
-We recommend that you create an incremental artifact when you need to apply changes to a subset of files in an artifact, where the size of the original artifact is significantly larger.
-{{% /alert %}}
-
-## Create new artifact versions from scratch
-There are two ways to create a new artifact version: from a single run and from distributed runs. They are defined as follows:
-
-
-* **Single run**: A single run provides all the data for a new version. This is the most common case and is best suited when the run fully recreates the needed data. For example: outputting saved models or model predictions in a table for analysis.
-* **Distributed runs**: A set of runs collectively provides all the data for a new version. This is best suited for distributed jobs which have multiple runs generating data, often in parallel. For example: evaluating a model in a distributed manner, and outputting the predictions.
-
-
-W&B will create a new artifact and assign it a `v0` alias if you pass a name to the `wandb.Artifact` API that does not exist in your project. W&B checksums the contents when you log again to the same artifact. If the artifact changed, W&B saves a new version `v1`.
-
-W&B will retrieve an existing artifact if you pass a name and artifact type to the `wandb.Artifact` API that matches an existing artifact in your project. The retrieved artifact will have a version greater than 1.
-
-{{< img src="/images/artifacts/single_distributed_artifacts.png" alt="Artifact workflow comparison" >}}
-
-### Single run
-Log a new version of an Artifact with a single run that produces all the files in the artifact. This case occurs when a single run produces all the files in the artifact.
-
-Based on your use case, select one of the tabs below to create a new artifact version inside or outside of a run:
-
-{{< tabpane text=true >}}
- {{% tab header="Inside a run" %}}
-Create an artifact version within a W&B run:
-
-1. Create a run with `wandb.init`.
-2. Create a new artifact or retrieve an existing one with `wandb.Artifact`.
-3. Add files to the artifact with `.add_file`.
-4. Log the artifact to the run with `.log_artifact`.
-
-```python
-with wandb.init() as run:
- artifact = wandb.Artifact("artifact_name", "artifact_type")
-
- # Add Files and Assets to the artifact using
- # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
- artifact.add_file("image1.png")
- run.log_artifact(artifact)
-```
- {{% /tab %}}
- {{% tab header="Outside of a run" %}}
-Create an artifact version outside of a W&B run:
-
-1. Create a new artifact or retrieve an existing one with `wanb.Artifact`.
-2. Add files to the artifact with `.add_file`.
-3. Save the artifact with `.save`.
-
-```python
-artifact = wandb.Artifact("artifact_name", "artifact_type")
-# Add Files and Assets to the artifact using
-# `.add`, `.add_file`, `.add_dir`, and `.add_reference`
-artifact.add_file("image1.png")
-artifact.save()
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-
-### Distributed runs
-
-Allow a collection of runs to collaborate on a version before committing it. This is in contrast to single run mode described above where one run provides all the data for a new version.
-
-
-{{% alert %}}
-1. Each run in the collection needs to be aware of the same unique ID (called `distributed_id`) in order to collaborate on the same version. By default, if present, W&B uses the run's `group` as set by `wandb.init(group=GROUP)` as the `distributed_id`.
-2. There must be a final run that "commits" the version, permanently locking its state.
-3. Use `upsert_artifact` to add to the collaborative artifact and `finish_artifact` to finalize the commit.
-{{% /alert %}}
-
-Consider the following example. Different runs (labelled below as **Run 1**, **Run 2**, and **Run 3**) add a different image file to the same artifact with `upsert_artifact`.
-
-
-#### Run 1:
-
-```python
-with wandb.init() as run:
- artifact = wandb.Artifact("artifact_name", "artifact_type")
- # Add Files and Assets to the artifact using
- # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
- artifact.add_file("image1.png")
- run.upsert_artifact(artifact, distributed_id="my_dist_artifact")
-```
-
-#### Run 2:
-
-```python
-with wandb.init() as run:
- artifact = wandb.Artifact("artifact_name", "artifact_type")
- # Add Files and Assets to the artifact using
- # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
- artifact.add_file("image2.png")
- run.upsert_artifact(artifact, distributed_id="my_dist_artifact")
-```
-
-#### Run 3
-
-Must run after Run 1 and Run 2 complete. The Run that calls `finish_artifact` can include files in the artifact, but does not need to.
-
-```python
-with wandb.init() as run:
- artifact = wandb.Artifact("artifact_name", "artifact_type")
- # Add Files and Assets to the artifact
- # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
- artifact.add_file("image3.png")
- run.finish_artifact(artifact, distributed_id="my_dist_artifact")
-```
-
-
-
-
-## Create a new artifact version from an existing version
-
-Add, modify, or remove a subset of files from a previous artifact version without the need to re-index the files that didn't change. Adding, modifying, or removing a subset of files from a previous artifact version creates a new artifact version known as an *incremental artifact*.
-
-{{< img src="/images/artifacts/incremental_artifacts.png" alt="Incremental artifact versioning" >}}
-
-Here are some scenarios for each type of incremental change you might encounter:
-
-- add: you periodically add a new subset of files to a dataset after collecting a new batch.
-- remove: you discovered several duplicate files and want to remove them from your artifact.
-- update: you corrected annotations for a subset of files and want to replace the old files with the correct ones.
-
-You could create an artifact from scratch to perform the same function as an incremental artifact. However, when you create an artifact from scratch, you will need to have all the contents of your artifact on your local disk. When making an incremental change, you can add, remove, or modify a single file without changing the files from a previous artifact version.
-
-
-{{% alert %}}
-You can create an incremental artifact within a single run or with a set of runs (distributed mode).
-{{% /alert %}}
-
-
-Follow the procedure below to incrementally change an artifact:
-
-1. Obtain the artifact version you want to perform an incremental change on:
-
-
-{{< tabpane text=true >}}
-{{% tab header="Inside a run" %}}
-
-```python
-saved_artifact = run.use_artifact("my_artifact:latest")
-```
-
-{{% /tab %}}
-{{% tab header="Outside of a run" %}}
-
-```python
-client = wandb.Api()
-saved_artifact = client.artifact("my_artifact:latest")
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-2. Create a draft with:
-
-```python
-draft_artifact = saved_artifact.new_draft()
-```
-
-3. Perform any incremental changes you want to see in the next version. You can either add, remove, or modify an existing entry.
-
-Select one of the tabs for an example on how to perform each of these changes:
-
-
-{{< tabpane text=true >}}
- {{% tab header="Add" %}}
-Add a file to an existing artifact version with the `add_file` method:
-
-```python
-draft_artifact.add_file("file_to_add.txt")
-```
-
-{{% alert %}}
-You can also add multiple files by adding a directory with the `add_dir` method.
-{{% /alert %}}
- {{% /tab %}}
- {{% tab header="Remove" %}}
-Remove a file from an existing artifact version with the `remove` method:
-
-```python
-draft_artifact.remove("file_to_remove.txt")
-```
-
-{{% alert %}}
-You can also remove multiple files with the `remove` method by passing in a directory path.
-{{% /alert %}}
- {{% /tab %}}
- {{% tab header="Modify" %}}
-Modify or replace contents by removing the old contents from the draft and adding the new contents back in:
-
-```python
-draft_artifact.remove("modified_file.txt")
-draft_artifact.add_file("modified_file.txt")
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-
-4. Lastly, log or save your changes. The following tabs show you how to save your changes inside and outside of a W&B run. Select the tab that is appropriate for your use case:
-
-{{< tabpane text=true >}}
- {{% tab header="Inside a run" %}}
-```python
-run.log_artifact(draft_artifact)
-```
-
- {{% /tab %}}
- {{% tab header="Outside of a run" %}}
-```python
-draft_artifact.save()
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-Putting it all together, the code examples above look like:
-
-{{< tabpane text=true >}}
- {{% tab header="Inside a run" %}}
-```python
-with wandb.init(job_type="modify dataset") as run:
- saved_artifact = run.use_artifact(
- "my_artifact:latest"
- ) # fetch artifact and input it into your run
- draft_artifact = saved_artifact.new_draft() # create a draft version
-
- # modify a subset of files in the draft version
- draft_artifact.add_file("file_to_add.txt")
- draft_artifact.remove("dir_to_remove/")
- run.log_artifact(
- draft_artifact
- ) # log your changes to create a new version and mark it as output to your run
-```
- {{% /tab %}}
- {{% tab header="Outside of a run" %}}
-```python
-client = wandb.Api()
-saved_artifact = client.artifact("my_artifact:latest") # load your artifact
-draft_artifact = saved_artifact.new_draft() # create a draft version
-
-# modify a subset of files in the draft version
-draft_artifact.remove("deleted_file.txt")
-draft_artifact.add_file("modified_file.txt")
-draft_artifact.save() # commit changes to the draft
-```
- {{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/artifacts/data-privacy-and-compliance.md b/content/en/guides/core/artifacts/data-privacy-and-compliance.md
deleted file mode 100644
index f63d2ed840..0000000000
--- a/content/en/guides/core/artifacts/data-privacy-and-compliance.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-description: Learn where W&B files are stored by default. Explore how to save, store
- sensitive information.
-menu:
- default:
- identifier: data-privacy-and-compliance
- parent: artifacts
-title: Artifact data privacy and compliance
----
-
-Files are uploaded to Google Cloud bucket managed by W&B when you log artifacts. The contents of the bucket are encrypted both at rest and in transit. Artifact files are only visible to users who have access to the corresponding project.
-
-{{< img src="/images/artifacts/data_and_privacy_compliance_1.png" alt="GCS W&B Client Server diagram" >}}
-
-When you delete a version of an artifact, it is marked for soft deletion in our database and removed from your storage cost. When you delete an entire artifact, it is queued for permanently deletion and all of its contents are removed from the W&B bucket. If you have specific needs around file deletion please reach out to [Customer Support](mailto:support@wandb.com).
-
-For sensitive datasets that cannot reside in a multi-tenant environment, you can use either a private W&B server connected to your cloud bucket or _reference artifacts_. Reference artifacts track references to private buckets without sending file contents to W&B. Reference artifacts maintain links to files on your buckets or servers. In other words, W&B only keeps track of the metadata associated with the files and not the files themselves.
-
-{{< img src="/images/artifacts/data_and_privacy_compliance_2.png" alt="W&B Client Server Cloud diagram" >}}
-
-Create a reference artifact similar to how you create a non reference artifact:
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = wandb.Artifact("animals", type="dataset")
-artifact.add_reference("s3://my-bucket/animals")
-```
-
-For alternatives, contact us at [contact@wandb.com](mailto:contact@wandb.com) to talk about private cloud and on-premises installations.
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/download-and-use-an-artifact.md b/content/en/guides/core/artifacts/download-and-use-an-artifact.md
deleted file mode 100644
index aa61771a2e..0000000000
--- a/content/en/guides/core/artifacts/download-and-use-an-artifact.md
+++ /dev/null
@@ -1,133 +0,0 @@
----
-description: Download and use Artifacts from multiple projects.
-menu:
- default:
- identifier: download-and-use-an-artifact
- parent: artifacts
-title: Download and use artifacts
-weight: 3
----
-
-Download and use an artifact that is already stored on the W&B server or construct an artifact object and pass it in to for de-duplication as necessary.
-
-{{% alert %}}
-Team members with view-only seats cannot download artifacts.
-{{% /alert %}}
-
-
-### Download and use an artifact stored on W&B
-
-Download and use an artifact stored in W&B either inside or outside of a W&B Run. Use the Public API ([`wandb.Api`]({{< relref "/ref/python/public-api/api.md" >}})) to export (or update data) already saved in W&B. For more information, see the W&B [Public API Reference guide]({{< relref "/ref/python/public-api/index.md" >}}).
-
-{{< tabpane text=true >}}
- {{% tab header="During a run" %}}
-First, import the W&B Python SDK. Next, create a W&B [Run]({{< relref "/ref/python/experiments/run.md" >}}):
-
-```python
-import wandb
-
-run = wandb.init(project="", job_type="")
-```
-
-Indicate the artifact you want to use with the [`use_artifact`]({{< relref "/ref/python/experiments/run.md#use_artifact" >}}) method. This returns a run object. In the proceeding code snippet specifies an artifact called `'bike-dataset'` with the alias `'latest'`:
-
-```python
-artifact = run.use_artifact("bike-dataset:latest")
-```
-
-Use the object returned to download all the contents of the artifact:
-
-```python
-datadir = artifact.download()
-```
-
-You can optionally pass a path to the root parameter to download the contents of the artifact to a specific directory. For more information, see the [Python SDK Reference Guide]({{< relref "/ref/python/experiments/artifact.md#download" >}}).
-
-Use the [`get_path`]({{< relref "/ref/python/experiments/artifact.md#get_path" >}}) method to download only subset of files:
-
-```python
-path = artifact.get_path(name)
-```
-
-This fetches only the file at the path `name`. It returns an `Entry` object with the following methods:
-
-* `Entry.download`: Downloads file from the artifact at path `name`
-* `Entry.ref`: If `add_reference` stored the entry as a reference, returns the URI
-
-References that have schemes that W&B knows how to handle get downloaded just like artifact files. For more information, see [Track external files]({{< relref "/guides/core/artifacts/track-external-files.md" >}}).
- {{% /tab %}}
- {{% tab header="Outside of a run" %}}
-First, import the W&B SDK. Next, create an artifact from the Public API Class. Provide the entity, project, artifact, and alias associated with that artifact:
-
-```python
-import wandb
-
-api = wandb.Api()
-artifact = api.artifact("entity/project/artifact:alias")
-```
-
-Use the object returned to download the contents of the artifact:
-
-```python
-artifact.download()
-```
-
-You can optionally pass a path the `root` parameter to download the contents of the artifact to a specific directory. For more information, see the [API Reference Guide]({{< relref "/ref/python/experiments/artifact.md#download" >}}).
- {{% /tab %}}
- {{% tab header="W&B CLI" %}}
-Use the `wandb artifact get` command to download an artifact from the W&B server.
-
-```
-$ wandb artifact get project/artifact:alias --root mnist/
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-### Partially download an artifact
-
-You can optionally download part of an artifact based on a prefix. Using the `path_prefix` parameter, you can download a single file or the content of a sub-folder.
-
-```python
-artifact = run.use_artifact("bike-dataset:latest")
-
-artifact.download(path_prefix="bike.png") # downloads only bike.png
-```
-
-Alternatively, you can download files from a certain directory:
-
-```python
-artifact.download(path_prefix="images/bikes/") # downloads files in the images/bikes directory
-```
-### Use an artifact from a different project
-
-Specify the name of artifact along with its project name to reference an artifact. You can also reference artifacts across entities by specifying the name of the artifact with its entity name.
-
-The following code example demonstrates how to query an artifact from another project as input to the current W&B run.
-
-```python
-import wandb
-
-run = wandb.init(project="", job_type="")
-# Query W&B for an artifact from another project and mark it
-# as an input to this run.
-artifact = run.use_artifact("my-project/artifact:alias")
-
-# Use an artifact from another entity and mark it as an input
-# to this run.
-artifact = run.use_artifact("my-entity/my-project/artifact:alias")
-```
-
-### Construct and use an artifact simultaneously
-
-Simultaneously construct and use an artifact. Create an artifact object and pass it to use_artifact. This creates an artifact in W&B if it does not exist yet. The [`use_artifact`]({{< relref "/ref/python/experiments/run.md#use_artifact" >}}) API is idempotent, so you can call it as many times as you like.
-
-```python
-import wandb
-
-artifact = wandb.Artifact("reference model")
-artifact.add_file("model.h5")
-run.use_artifact(artifact)
-```
-
-For more information about constructing an artifact, see [Construct an artifact]({{< relref "/guides/core/artifacts/construct-an-artifact.md" >}}).
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/explore-and-traverse-an-artifact-graph.md b/content/en/guides/core/artifacts/explore-and-traverse-an-artifact-graph.md
deleted file mode 100644
index 25a74a1360..0000000000
--- a/content/en/guides/core/artifacts/explore-and-traverse-an-artifact-graph.md
+++ /dev/null
@@ -1,84 +0,0 @@
----
-description: Traverse automatically created direct acyclic W&B Artifact graphs.
-menu:
- default:
- identifier: explore-and-traverse-an-artifact-graph
- parent: artifacts
-title: Explore artifact graphs
-weight: 9
----
-
-W&B automatically tracks the artifacts a given run logged as well as the artifacts a given run uses. These artifacts can include datasets, models, evaluation results, or more. You can explore an artifact's lineage to track and manage the various artifacts produced throughout the machine learning lifecycle.
-
-## Lineage
-Tracking an artifact's lineage has several key benefits:
-
-- Reproducibility: By tracking the lineage of all artifacts, teams can reproduce experiments, models, and results, which is essential for debugging, experimentation, and validating machine learning models.
-
-- Version Control: Artifact lineage involves versioning artifacts and tracking their changes over time. This allows teams to roll back to previous versions of data or models if needed.
-
-- Auditing: Having a detailed history of the artifacts and their transformations enables organizations to comply with regulatory and governance requirements.
-
-- Collaboration and Knowledge Sharing: Artifact lineage facilitates better collaboration among team members by providing a clear record of attempts as well as what worked, and what didn’t. This helps in avoiding duplication of efforts and accelerates the development process.
-
-### Finding an artifact's lineage
-When selecting an artifact in the **Artifacts** tab, you can see your artifact's lineage. This graph view shows a general overview of your pipeline.
-
-To view an artifact graph:
-
-1. Navigate to your project in the W&B App UI
-2. Choose the artifact icon on the left panel.
-3. Select **Lineage**.
-
-{{< img src="/images/artifacts/lineage1.gif" alt="Getting to the Lineage tab" >}}
-
-### Navigating the lineage graph
-
-The artifact or job type you provide appears in front of its name, with artifacts represented by blue icons and runs represented by green icons. Arrows detail the input and output of a run or artifact on the graph.
-
-{{< img src="/images/artifacts/lineage2.png" alt="Run and artifact nodes" >}}
-
-{{% alert %}}
-You can view the type and the name of artifact in both the left sidebar and in the **Lineage** tab.
-{{% /alert %}}
-
-{{< img src="/images/artifacts/lineage2a.gif" alt="Inputs and outputs" >}}
-
-For a more detailed view, click any individual artifact or run to get more information on a particular object.
-
-{{< img src="/images/artifacts/lineage3a.gif" alt="Previewing a run" >}}
-
-### Artifact clusters
-
-When a level of the graph has five or more runs or artifacts, it creates a cluster. A cluster has a search bar to find specific versions of runs or artifacts and pulls an individual node from a cluster to continue investigating the lineage of a node inside a cluster.
-
-Clicking on a node opens a preview with an overview of the node. Clicking on the arrow extracts the individual run or artifact so you can examine the lineage of the extracted node.
-
-{{< img src="/images/artifacts/lineage3b.gif" alt="Searching a run cluster" >}}
-
-## Use the API to track lineage
-You can also navigate a graph using the [W&B API]({{< relref "/ref/python/public-api/api.md" >}}).
-
-Create an artifact. First, create a run with `wandb.init`. Then,create a new artifact or retrieve an existing one with `wandb.Artifact`. Next, add files to the artifact with `.add_file`. Finally, log the artifact to the run with `.log_artifact`. The finished code looks something like this:
-
-```python
-with wandb.init() as run:
- artifact = wandb.Artifact("artifact_name", "artifact_type")
-
- # Add Files and Assets to the artifact using
- # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
- artifact.add_file("image1.png")
- run.log_artifact(artifact)
-```
-
-Use the artifact object's [`logged_by`]({{< relref "/ref/python/experiments/artifact.md#logged_by" >}}) and [`used_by`]({{< relref "/ref/python/experiments/artifact.md#used_by" >}}) methods to walk the graph from the artifact:
-
-```python
-# Walk up and down the graph from an artifact:
-producer_run = artifact.logged_by()
-consumer_runs = artifact.used_by()
-```
-## Next steps
-- [Explore artifacts in more detail]({{< relref "/guides/core/artifacts/artifacts-walkthrough.md" >}})
-- [Manage artifact storage]({{< relref "/guides/core/artifacts/manage-data/delete-artifacts.md" >}})
-- [Explore an artifacts project](https://wandb.ai/wandb-smle/artifact_workflow/artifacts/raw_dataset/raw_data/v0/lineage)
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/manage-data/_index.md b/content/en/guides/core/artifacts/manage-data/_index.md
deleted file mode 100644
index e99dec6127..0000000000
--- a/content/en/guides/core/artifacts/manage-data/_index.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-menu:
- default:
- identifier: manage-data
- parent: artifacts
-title: Manage data
-weight: 8
-url: guides/artifacts/manage-data
----
-
diff --git a/content/en/guides/core/artifacts/manage-data/delete-artifacts.md b/content/en/guides/core/artifacts/manage-data/delete-artifacts.md
deleted file mode 100644
index 7b6fafac51..0000000000
--- a/content/en/guides/core/artifacts/manage-data/delete-artifacts.md
+++ /dev/null
@@ -1,194 +0,0 @@
----
-description: Delete artifacts interactively with the App UI or programmatically with
- the W&B SDK/
-menu:
- default:
- identifier: delete-artifacts
- parent: manage-data
-title: Delete an artifact
----
-
-Delete artifacts interactively with the App UI or programmatically with the W&B SDK. When you delete an artifact, W&B marks that artifact as a *soft-delete*. In other words, the artifact is marked for deletion but files are not immediately deleted from storage.
-
-The contents of the artifact remain as a soft-delete, or pending deletion state, until a regularly run garbage collection process reviews all artifacts marked for deletion. The garbage collection process deletes associated files from storage if the artifact and its associated files are not used by a previous or subsequent artifact versions.
-
-## Artifact garbage collection workflow
-
-The following diagram illustrates the complete artifact garbage collection process:
-
-```mermaid
-graph TB
- Start([Artifact Deletion Initiated]) --> DeleteMethod{Deletion Method}
-
- DeleteMethod -->|UI| UIDelete[Delete via W&B App UI]
- DeleteMethod -->|SDK| SDKDelete[Delete via W&B SDK]
- DeleteMethod -->|TTL| TTLDelete[TTL Policy Expires]
-
- UIDelete --> SoftDelete[Artifact Marked as 'Soft Delete']
- SDKDelete --> SoftDelete
- TTLDelete --> SoftDelete
-
- SoftDelete --> GCWait[(Wait for Garbage Collection Process)]
-
- GCWait --> GCRun[Garbage Collection Process Runs
- Reviews all soft-deleted artifacts - Checks file dependencies]
-
- GCRun --> CheckUsage{Are files used by other artifact versions?}
-
- CheckUsage -->|Yes| KeepFiles[Files Kept in Storage
- Artifact marked deleted - Files remain for other versions]
- CheckUsage -->|No| DeleteFiles[Files Deleted from Storage
- Artifact fully removed - Storage space reclaimed]
-
- KeepFiles --> End([End])
- DeleteFiles --> End
-
- style Start fill:#e1f5fe,stroke:#333,stroke-width:2px,color:#000
- style SoftDelete fill:#fff3e0,stroke:#333,stroke-width:2px,color:#000
- style GCRun fill:#f3e5f5,stroke:#333,stroke-width:2px,color:#000
- style KeepFiles fill:#e8f5e9,stroke:#333,stroke-width:2px,color:#000
- style DeleteFiles fill:#ffebee,stroke:#333,stroke-width:2px,color:#000
- style End fill:#e0e0e0,stroke:#333,stroke-width:2px,color:#000
-```
-
-The sections in this page describe how to delete specific artifact versions, how to delete an artifact collection, how to delete artifacts with and without aliases, and more. You can schedule when artifacts are deleted from W&B with TTL policies. For more information, see [Manage data retention with Artifact TTL policy]({{< relref "./ttl.md" >}}).
-
-{{% alert %}}
-Artifacts that are scheduled for deletion with a TTL policy, deleted with the W&B SDK, or deleted with the W&B App UI are first soft-deleted. Artifacts that are soft deleted undergo garbage collection before they are hard-deleted.
-{{% /alert %}}
-
-{{% alert %}}
-Deleting an entity, project, or artifact collection will also trigger the artifact deletion process described on this page. When deleting a run, if you choose to delete associated artifacts, those artifacts will follow the same soft-delete and garbage collection workflow.
-{{% /alert %}}
-
-### Delete an artifact version
-
-To delete an artifact version:
-
-1. Select the name of the artifact. This will expand the artifact view and list all the artifact versions associated with that artifact.
-2. From the list of artifacts, select the artifact version you want to delete.
-3. On the right hand side of the workspace, select the kebab dropdown.
-4. Choose Delete.
-
-An artifact version can also be deleted programmatically via the [delete()]({{< relref "/ref/python/experiments/artifact.md#delete" >}}) method. See the examples below.
-
-### Delete multiple artifact versions with aliases
-
-The following code example demonstrates how to delete artifacts that have aliases associated with them. Provide the entity, project name, and run ID that created the artifacts.
-
-```python
-import wandb
-
-run = api.run("entity/project/run_id")
-
-for artifact in run.logged_artifacts():
- artifact.delete()
-```
-
-Set the `delete_aliases` parameter to the boolean value, `True` to delete aliases if the artifact has one or more aliases.
-
-```python
-import wandb
-
-run = api.run("entity/project/run_id")
-
-for artifact in run.logged_artifacts():
- # Set delete_aliases=True in order to delete
- # artifacts with one more aliases
- artifact.delete(delete_aliases=True)
-```
-
-### Delete multiple artifact versions with a specific alias
-
-The proceeding code demonstrates how to delete multiple artifact versions that have a specific alias. Provide the entity, project name, and run ID that created the artifacts. Replace the deletion logic with your own:
-
-```python
-import wandb
-
-runs = api.run("entity/project_name/run_id")
-
-# Delete artifact ith alias 'v3' and 'v4
-for artifact_version in runs.logged_artifacts():
- # Replace with your own deletion logic.
- if artifact_version.name[-2:] == "v3" or artifact_version.name[-2:] == "v4":
- artifact.delete(delete_aliases=True)
-```
-
-### Protected aliases and deletion permissions
-
-Artifacts with protected aliases have special deletion restrictions. [Protected aliases]({{< relref "/guides/core/registry/model_registry/access_controls.md" >}}) are aliases in the Model Registry that registry admins can set to prevent unauthorized deletion.
-
-{{% alert %}}
-**Important considerations for protected aliases:**
-- Artifacts with protected aliases cannot be deleted by non-registry admins
-- Within a registry, registry admins can unlink protected artifact versions and delete collections/registries that contain protected aliases
-- For source artifacts: if a source artifact is linked to a registry with a protected alias, it cannot be deleted by any user
-- Registry admins can remove the protected aliases from source artifacts and then delete them
-{{% /alert %}}
-
-### Delete all versions of an artifact that do not have an alias
-
-The following code snippet demonstrates how to delete all versions of an artifact that do not have an alias. Provide the name of the project and entity for the `project` and `entity` keys in `wandb.Api`, respectively. Replace the `<>` with the name of your artifact:
-
-```python
-import wandb
-
-# Provide your entity and a project name when you
-# use wandb.Api methods.
-api = wandb.Api(overrides={"project": "project", "entity": "entity"})
-
-artifact_type, artifact_name = "<>" # provide type and name
-for v in api.artifact_versions(artifact_type, artifact_name):
- # Clean up versions that don't have an alias such as 'latest'.
- # NOTE: You can put whatever deletion logic you want here.
- if len(v.aliases) == 0:
- v.delete()
-```
-
-### Delete an artifact collection
-
-To delete an artifact collection:
-
-1. Navigate to the artifact collection you want to delete and hover over it.
-3. Select the kebab dropdown next to the artifact collection name.
-4. Choose Delete.
-
-You can also delete artifact collection programmatically with the [delete()]({{< relref "/ref/python/experiments/artifact.md#delete" >}}) method. Provide the name of the project and entity for the `project` and `entity` keys in `wandb.Api`, respectively:
-
-```python
-import wandb
-
-# Provide your entity and a project name when you
-# use wandb.Api methods.
-api = wandb.Api(overrides={"project": "project", "entity": "entity"})
-collection = api.artifact_collection(
- "", "entity/project/artifact_collection_name"
-)
-collection.delete()
-```
-
-## How to enable garbage collection based on how W&B is hosted
-Garbage collection is enabled by default if you use W&B's shared cloud. Based on how you host W&B, you might need to take additional steps to enable garbage collection, this includes:
-
-
-* Set the `GORILLA_ARTIFACT_GC_ENABLED` environment variable to true: `GORILLA_ARTIFACT_GC_ENABLED=true`
-* Enable bucket versioning if you use [AWS](https://docs.aws.amazon.com/AmazonS3/latest/userguide/manage-versioning-examples.html), [GCP](https://cloud.google.com/storage/docs/object-versioning) or any other storage provider such as [Minio](https://min.io/docs/minio/linux/administration/object-management/object-versioning.html#enable-bucket-versioning). If you use Azure, [enable soft deletion](https://learn.microsoft.com/azure/storage/blobs/soft-delete-blob-overview).
- {{% alert %}}
- Soft deletion in Azure is equivalent to bucket versioning in other storage providers.
- {{% /alert %}}
-
-The following table describes how to satisfy requirements to enable garbage collection based on your deployment type.
-
-The `X` indicates you must satisfy the requirement:
-
-| | Environment variable | Enable versioning |
-| -----------------------------------------------| ------------------------| ----------------- |
-| Shared cloud | | |
-| Shared cloud with [secure storage connector]({{< relref "/guides/hosting/data-security/secure-storage-connector.md" >}})| | X |
-| Dedicated cloud | | |
-| Dedicated cloud with [secure storage connector]({{< relref "/guides/hosting/data-security/secure-storage-connector.md" >}})| | X |
-| Customer-managed cloud | X | X |
-| Customer managed on-prem | X | X |
-
-
-
-{{% alert %}}note
-Secure storage connector is currently only available for Google Cloud Platform and Amazon Web Services.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/manage-data/storage.md b/content/en/guides/core/artifacts/manage-data/storage.md
deleted file mode 100644
index 91842e9183..0000000000
--- a/content/en/guides/core/artifacts/manage-data/storage.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-description: Manage storage, memory allocation of W&B Artifacts.
-menu:
- default:
- identifier: storage
- parent: manage-data
-title: Manage artifact storage and memory allocation
----
-
-W&B stores artifact files in a private Google Cloud Storage bucket located in the United States by default. All files are encrypted at rest and in transit.
-
-For sensitive files, we recommend you set up [Private Hosting]({{< relref "/guides/hosting/" >}}) or use [reference artifacts]({{< relref "../track-external-files.md" >}}).
-
-During training, W&B locally saves logs, artifacts, and configuration files in the following local directories:
-
-| File | Default location | To change default location set: |
-| ---- | ---------------- | ------------------------------- |
-| logs | `./wandb` | `dir` in `wandb.init` or set the `WANDB_DIR` environment variable |
-| artifacts | `~/.cache/wandb` | the `WANDB_CACHE_DIR` environment variable |
-| configs | `~/.config/wandb` | the `WANDB_CONFIG_DIR` environment variable |
-| staging artifacts for upload | `~/.cache/wandb-data/` | the `WANDB_DATA_DIR` environment variable |
-| downloaded artifacts | `./artifacts` | the `WANDB_ARTIFACT_DIR` environment variable |
-
-For a complete guide to using environment variables to configure W&B, see the [environment variables reference]({{< relref "/guides/models/track/environment-variables.md" >}}).
-
-{{% alert color="secondary" %}}
-Depending on the machine on `wandb` is initialized on, these default folders may not be located in a writeable part of the file system. This might trigger an error.
-{{% /alert %}}
-
-### Clean up local artifact cache
-
-W&B caches artifact files to speed up downloads across versions that share files in common. Over time this cache directory can become large. Run the [`wandb artifact cache cleanup`]({{< relref "/ref/cli/wandb-artifact/wandb-artifact-cache/" >}}) command to prune the cache and to remove any files that have not been used recently.
-
-The proceeding code snippet demonstrates how to limit the size of the cache to 1GB. Copy and paste the code snippet into your terminal:
-
-```bash
-$ wandb artifact cache cleanup 1GB
-```
diff --git a/content/en/guides/core/artifacts/manage-data/ttl.md b/content/en/guides/core/artifacts/manage-data/ttl.md
deleted file mode 100644
index b7751604fb..0000000000
--- a/content/en/guides/core/artifacts/manage-data/ttl.md
+++ /dev/null
@@ -1,238 +0,0 @@
----
-description: Time to live policies (TTL)
-menu:
- default:
- identifier: ttl
- parent: manage-data
-title: Manage artifact data retention
----
-
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/kas-artifacts-ttl-colab/colabs/wandb-artifacts/WandB_Artifacts_Time_to_live_TTL_Walkthrough.ipynb" >}}
-
-Schedule when artifacts are deleted from W&B with a W&B Artifact time-to-live (TTL) policy. When you delete an artifact, W&B marks that artifact as a *soft-delete*. In other words, the artifact is marked for deletion but files are not immediately deleted from storage. For more information on how W&B deletes artifacts, see the [Delete artifacts]({{< relref "./delete-artifacts.md" >}}) page.
-
-Watch a [Managing data retention with Artifacts TTL](https://www.youtube.com/watch?v=hQ9J6BoVmnc) video tutorial to learn how to manage data retention with Artifacts TTL in the W&B App.
-
-{{% alert %}}
-W&B deactivates the option to set a TTL policy for model artifacts linked to the Model Registry. This is to help ensure that linked models do not accidentally expire if used in production workflows.
-{{% /alert %}}
-{{% alert %}}
-* Only team admins can view a [team's settings]({{< relref "/guides/models/app/settings-page/teams.md" >}}) and access team level TTL settings such as (1) permitting who can set or edit a TTL policy or (2) setting a team default TTL.
-* If you do not see the option to set or edit a TTL policy in an artifact's details in the W&B App UI or if setting a TTL programmatically does not successfully change an artifact's TTL property, your team admin has not given you permissions to do so.
-{{% /alert %}}
-
-## Auto-generated Artifacts
-Only user-generated artifacts can use TTL policies. Artifacts auto-generated by W&B cannot have TTL policies set for them.
-
-The following Artifact types indicate an auto-generated Artifact:
-- `run_table`
-- `code`
-- `job`
-- Any Artifact type starting with: `wandb-*`
-
-You can check an Artifact's type on the [W&B platform]({{< relref "/guides/core/artifacts/explore-and-traverse-an-artifact-graph.md" >}}) or programmatically:
-
-```python
-import wandb
-
-run = wandb.init(project="")
-artifact = run.use_artifact(artifact_or_name="")
-print(artifact.type)
-```
-
-Replace the values enclosed with `<>` with your own.
-
-## Define who can edit and set TTL policies
-Define who can set and edit TTL policies within a team. You can either grant TTL permissions only to team admins, or you can grant both team admins and team members TTL permissions.
-
-{{% alert %}}
-Only team admins can define who can set or edit a TTL policy.
-{{% /alert %}}
-
-1. Navigate to your team’s profile page.
-2. Select the **Settings** tab.
-3. Navigate to the **Artifacts time-to-live (TTL) section**.
-4. From the **TTL permissions dropdown**, select who can set and edit TTL policies.
-5. Click on **Review and save settings**.
-6. Confirm the changes and select **Save settings**.
-
-{{< img src="/images/artifacts/define_who_sets_ttl.gif" alt="Setting TTL permissions" >}}
-
-## Create a TTL policy
-Set a TTL policy for an artifact either when you create the artifact or retroactively after the artifact is created.
-
-For all the code snippets below, replace the content wrapped in `<>` with your information to use the code snippet.
-
-### Set a TTL policy when you create an artifact
-Use the W&B Python SDK to define a TTL policy when you create an artifact. TTL policies are typically defined in days.
-
-{{% alert %}}
-Defining a TTL policy when you create an artifact is similar to how you normally [create an artifact]({{< relref "../construct-an-artifact.md" >}}). With the exception that you pass in a time delta to the artifact's `ttl` attribute.
-{{% /alert %}}
-
-The steps are as follows:
-
-1. [Create an artifact]({{< relref "../construct-an-artifact.md" >}}).
-2. [Add content to the artifact]({{< relref "../construct-an-artifact.md#add-files-to-an-artifact" >}}) such as files, a directory, or a reference.
-3. Define a TTL time limit with the [`datetime.timedelta`](https://docs.python.org/3/library/datetime.html) data type that is part of Python's standard library.
-4. [Log the artifact]({{< relref "../construct-an-artifact.md#3-save-your-artifact-to-the-wb-server" >}}).
-
-The following code snippet demonstrates how to create an artifact and set a TTL policy.
-
-```python
-import wandb
-from datetime import timedelta
-
-run = wandb.init(project="", entity="")
-artifact = wandb.Artifact(name="", type="")
-artifact.add_file("")
-
-artifact.ttl = timedelta(days=30) # Set TTL policy
-run.log_artifact(artifact)
-```
-
-The preceding code snippet sets the TTL policy for the artifact to 30 days. In other words, W&B deletes the artifact after 30 days.
-
-### Set or edit a TTL policy after you create an artifact
-Use the W&B App UI or the W&B Python SDK to define a TTL policy for an artifact that already exists.
-
-{{% alert %}}
-When you modify an artifact's TTL, the time the artifact takes to expire is still calculated using the artifact's `createdAt` timestamp.
-{{% /alert %}}
-
-{{< tabpane text=true >}}
- {{% tab header="Python SDK" %}}
-1. [Fetch your artifact]({{< relref "../download-and-use-an-artifact.md" >}}).
-2. Pass in a time delta to the artifact's `ttl` attribute.
-3. Update the artifact with the [`save`]({{< relref "/ref/python/experiments/run.md#save" >}}) method.
-
-
-The following code snippet shows how to set a TTL policy for an artifact:
-```python
-import wandb
-from datetime import timedelta
-
-artifact = run.use_artifact("")
-artifact.ttl = timedelta(days=365 * 2) # Delete in two years
-artifact.save()
-```
-
-The preceding code example sets the TTL policy to two years.
- {{% /tab %}}
- {{% tab header="W&B App" %}}
-1. Navigate to your W&B project in the W&B App UI.
-2. Select the artifact icon on the left panel.
-3. From the list of artifacts, expand the artifact type you
-4. Select on the artifact version you want to edit the TTL policy for.
-5. Click on the **Version** tab.
-6. From the dropdown, select **Edit TTL policy**.
-7. Within the modal that appears, select **Custom** from the TTL policy dropdown.
-8. Within the **TTL duration** field, set the TTL policy in units of days.
-9. Select the **Update TTL** button to save your changes.
-
-{{< img src="/images/artifacts/edit_ttl_ui.gif" alt="Editing TTL policy" >}}
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-### Set default TTL policies for a team
-
-{{% alert %}}
-Only team admins can set a default TTL policy for a team.
-{{% /alert %}}
-
-Set a default TTL policy for your team. Default TTL policies apply to all existing and future artifacts based on their respective creation dates. Artifacts with existing version-level TTL policies are not affected by the team's default TTL.
-
-1. Navigate to your team’s profile page.
-2. Select the **Settings** tab.
-3. Navigate to the **Artifacts time-to-live (TTL) section**.
-4. Click on the **Set team's default TTL policy**.
-5. Within the **Duration** field, set the TTL policy in units of days.
-6. Click on **Review and save settings**.
-7/ Confirm the changes and then select **Save settings**.
-
-{{< img src="/images/artifacts/set_default_ttl.gif" alt="Setting default TTL policy" >}}
-
-### Set a TTL policy outside of a run
-
-Use the public API to retrieve an artifact without fetching a run, and set the TTL policy. TTL policies are typically defined in days.
-
-The following code sample shows how to fetch an artifact using the public API and set the TTL policy.
-
-```python
-api = wandb.Api()
-
-artifact = api.artifact("entity/project/artifact:alias")
-
-artifact.ttl = timedelta(days=365) # Delete in one year
-
-artifact.save()
-```
-
-## Deactivate a TTL policy
-Use the W&B Python SDK or W&B App UI to deactivate a TTL policy for a specific artifact version.
-
-
-{{< tabpane text=true >}}
- {{% tab header="Python SDK" %}}
-1. [Fetch your artifact]({{< relref "../download-and-use-an-artifact.md" >}}).
-2. Set the artifact's `ttl` attribute to `None`.
-3. Update the artifact with the [`save`]({{< relref "/ref/python/experiments/run.md#save" >}}) method.
-
-
-The following code snippet shows how to turn off a TTL policy for an artifact:
-```python
-artifact = run.use_artifact("")
-artifact.ttl = None
-artifact.save()
-```
- {{% /tab %}}
- {{% tab header="W&B App" %}}
-1. Navigate to your W&B project in the W&B App UI.
-2. Select the artifact icon on the left panel.
-3. From the list of artifacts, expand the artifact type you
-4. Select on the artifact version you want to edit the TTL policy for.
-5. Click on the Version tab.
-6. Click on the meatball UI icon next to the **Link to registry** button.
-7. From the dropdown, select **Edit TTL policy**.
-8. Within the modal that appears, select **Deactivate** from the TTL policy dropdown.
-9. Select the **Update TTL** button to save your changes.
-
-{{< img src="/images/artifacts/remove_ttl_polilcy.gif" alt="Removing TTL policy" >}}
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-
-## View TTL policies
-View TTL policies for artifacts with the Python SDK or with the W&B App UI.
-
-{{< tabpane text=true >}}
- {{% tab header="Python SDK" %}}
-Use a print statement to view an artifact's TTL policy. The following example shows how to retrieve an artifact and view its TTL policy:
-
-```python
-artifact = run.use_artifact("")
-print(artifact.ttl)
-```
- {{% /tab %}}
- {{% tab header="W&B App" %}}
-View a TTL policy for an artifact with the W&B App UI.
-
-1. Navigate to the [W&B App](https://wandb.ai).
-2. Go to your W&B Project.
-3. Within your project, select the Artifacts tab in the left sidebar.
-4. Click on a collection.
-
-Within the collection view you can see all of the artifacts in the selected collection. Within the `Time to Live` column you will see the TTL policy assigned to that artifact.
-
-{{< img src="/images/artifacts/ttl_collection_panel_ui.png" alt="TTL collection view" >}}
- {{% /tab %}}
-{{< /tabpane >}}
-
diff --git a/content/en/guides/core/artifacts/track-external-files.md b/content/en/guides/core/artifacts/track-external-files.md
deleted file mode 100644
index aea0f08afa..0000000000
--- a/content/en/guides/core/artifacts/track-external-files.md
+++ /dev/null
@@ -1,253 +0,0 @@
----
-description: Track files saved in an external bucket, HTTP file server, or an NFS share.
-menu:
- default:
- identifier: track-external-files
- parent: artifacts
-title: Track external files
-weight: 7
----
-
-Use **reference artifacts** to track and use files saved outside of W&B servers, for example in CoreWeave AI Object Storage, an Amazon Simple Storage Service (Amazon S3) bucket, GCS bucket, Azure blob, HTTP file server, or NFS share.
-
-W&B logs metadata about the the object, such as the object's ETag and size. If object versioning is enabled on the bucket, the version ID is also logged.
-
-{{% alert %}}
-If you log an artifact that does not track external files, W&B saves the artifact's files to W&B servers. This is the default behavior when you log artifacts with the W&B Python SDK.
-
-See the [Artifacts quickstart]({{< relref "/guides/core/artifacts/artifacts-walkthrough" >}}) for information on how to save files and directories to W&B servers instead.
-{{% /alert %}}
-
-The following describes how to construct reference artifacts.
-
-## Track an artifact in an external bucket
-
-Use the W&B Python SDK to track references to files stored outside of W&B.
-
-1. Initialize a run with `wandb.init()`.
-2. Create an artifact object with `wandb.Artifact()`.
-3. Specify the reference to the bucket path with the artifact object's `add_reference()` method.
-4. Log the artifact's metadata with `run.log_artifact()`.
-
-```python
-import wandb
-
-# Initialize a W&B run
-run = wandb.init()
-
-# Create an artifact object
-artifact = wandb.Artifact(name="name", type="type")
-
-# Add a reference to the bucket path
-artifact.add_reference(uri = "uri/to/your/bucket/path")
-
-# Log the artifact's metadata
-run.log_artifact(artifact)
-run.finish()
-```
-
-Suppose your bucket has the following directory structure:
-
-```text
-s3://my-bucket
-
-|datasets/
- |---- mnist/
-|models/
- |---- cnn/
-```
-
-The `datasets/mnist/` directory contains a collection of images. Track the directory as a dataset with `wandb.Artifact.add_reference()`. The following code sample creates a reference artifact `mnist:latest` using the artifact object's `add_reference()` method.
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = wandb.Artifact(name="mnist", type="dataset")
-artifact.add_reference(uri="s3://my-bucket/datasets/mnist")
-run.log_artifact(artifact)
-run.finish()
-```
-
-Within the W&B App, you can look through the contents of the reference artifact using the file browser, [explore the full dependency graph]({{< relref "/guides/core/artifacts/explore-and-traverse-an-artifact-graph" >}}), and scan through the versioned history of your artifact. The W&B App does not render rich media such as images, audio, and so forth because the data itself is not contained within the artifact.
-
-{{% alert %}}
-W&B Artifacts support any Amazon S3 compatible interface, including CoreWeave Storage and MinIO. The scripts described below work as-is with both providers, when you set the `AWS_S3_ENDPOINT_URL` environment variable to point at your CoreWeave Storage or MinIO server.
-{{% /alert %}}
-
-{{% alert color="secondary" %}}
-By default, W&B imposes a 10,000 object limit when adding an object prefix. You can adjust this limit by specifying `max_objects=` when you call `add_reference()`.
-{{% /alert %}}
-
-## Download an artifact from an external bucket
-
-W&B retrieves the files from the underlying bucket when it downloads a reference artifact using the metadata recorded when the artifact is logged. If your bucket has object versioning enabled, W&B retrieves the object version that corresponds to the state of the file at the time an artifact was logged. As you evolve the contents of your bucket, you can always point to the exact version of your data a given model was trained on, because the artifact serves as a snapshot of your bucket during the training run.
-
-The following code sample shows how to download a reference artifact. The the APIs for downloading artifacts are the same for both reference and non-reference artifacts:
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = run.use_artifact("mnist:latest", type="dataset")
-artifact_dir = artifact.download()
-```
-
-{{% alert %}}
-W&B recommends that you enable 'Object Versioning' on your storage buckets if you overwrite files as part of your workflow. With versioning enabled on your buckets, artifacts with references to files that have been overwritten will still be intact because the older object versions are retained.
-
-Based on your use case, read the instructions to enable object versioning: [AWS](https://docs.aws.amazon.com/AmazonS3/latest/userguide/manage-versioning-examples.html), [GCP](https://cloud.google.com/storage/docs/using-object-versioning#set), [Azure](https://learn.microsoft.com/azure/storage/blobs/versioning-enable).
-{{% /alert %}}
-
-### Add and download an external reference example
-
-The following code sample uploads a dataset to an Amazon S3 bucket, tracks it with a reference artifact, then downloads it:
-
-```python
-import boto3
-import wandb
-
-run = wandb.init()
-
-# Training here...
-
-s3_client = boto3.client("s3")
-s3_client.upload_file(file_name="my_model.h5", bucket="my-bucket", object_name="models/cnn/my_model.h5")
-
-# Log the model artifact
-model_artifact = wandb.Artifact("cnn", type="model")
-model_artifact.add_reference("s3://my-bucket/models/cnn/")
-run.log_artifact(model_artifact)
-```
-
-At a later point, you can download the model artifact. Specify the name of the artifact and its type:
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = run.use_artifact(artifact_or_name = "cnn", type="model")
-datadir = artifact.download()
-```
-
-{{% alert %}}
-See the following reports for an end-to-end walkthrough on how to track artifacts by reference for GCP or Azure:
-
-* [Guide to Tracking Artifacts by Reference with GCP](https://wandb.ai/stacey/artifacts/reports/Tracking-Artifacts-by-Reference--Vmlldzo1NDMwOTE)
-* [Working with Reference Artifacts in Microsoft Azure](https://wandb.ai/andrea0/azure-2023/reports/Efficiently-Harnessing-Microsoft-Azure-Blob-Storage-with-Weights-Biases--Vmlldzo0NDA2NDgw)
-{{% /alert %}}
-
-## Cloud storage credentials
-
-W&B uses the default mechanism to look for credentials based on the cloud provider you use. Read the documentation from your cloud provider to learn more about the credentials used:
-
-| Cloud provider | Credentials Documentation |
-| -------------- | ------------------------- |
-| CoreWeave AI Object Storage | [CoreWeave AI Object Storage documentation](https://docs.coreweave.com/docs/products/storage/object-storage/how-to/manage-access-keys/cloud-console-tokens) |
-| AWS | [Boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials) |
-| GCP | [Google Cloud documentation](https://cloud.google.com/docs/authentication/provide-credentials-adc) |
-| Azure | [Azure documentation](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python) |
-
-For AWS, if the bucket is not located in the configured user's default region, you must set the `AWS_REGION` environment variable to match the bucket region.
-
-{{% alert color="secondary" %}}
-Rich media such as images, audio, video, and point clouds may fail to render in the App UI depending on the CORS configuration of your bucket. Allow listing **app.wandb.ai** in your bucket's CORS settings will allow the App UI to properly render such rich media.
-
-If rich media such as images, audio, video, and point clouds does not render in the App UI, ensure that `app.wandb.ai` is allowlisted in your bucket's CORS policy.
-{{% /alert %}}
-
-## Track an artifact in a filesystem
-
-Another common pattern for fast access to datasets is to expose an NFS mount point to a remote filesystem on all machines running training jobs. This can be an even simpler solution than a cloud storage bucket because from the perspective of the training script, the files look just like they are sitting on your local filesystem. Luckily, that ease of use extends into using Artifacts to track references to file systems, whether they are mounted or not.
-
-Suppose you have a filesystem mounted at `/mount` with the following structure:
-
-```bash
-mount
-|datasets/
- |-- mnist/
-|models/
- |-- cnn/
-```
-
-Within `mnist/` is a dataset, a collection of images. You can track it with an artifact:
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = wandb.Artifact("mnist", type="dataset")
-artifact.add_reference("file:///mount/datasets/mnist/")
-run.log_artifact(artifact)
-```
-{{% alert color="secondary" %}}
-By default, W&B imposes a 10,000 file limit when adding a reference to a directory. You can adjust this limit by specifying `max_objects=` when you call `add_reference()`.
-{{% /alert %}}
-
-Note the triple slash in the URL. The first component is the `file://` prefix that denotes the use of filesystem references. The second component begins the path to the dataset, `/mount/datasets/mnist/`.
-
-The resulting artifact `mnist:latest` looks and acts like a regular artifact. The only difference is that the artifact only consists of metadata about the files, such as their sizes and MD5 checksums. The files themselves never leave your system.
-
-You can interact with this artifact just as you would a normal artifact. In the UI, you can browse the contents of the reference artifact using the file browser, explore the full dependency graph, and scan through the versioned history of your artifact. However, the UI cannot render rich media such as images, audio, because the data itself is not contained within the artifact.
-
-Downloading a reference artifact:
-
-```python
-import wandb
-
-run = wandb.init()
-artifact = run.use_artifact("entity/project/mnist:latest", type="dataset")
-artifact_dir = artifact.download()
-```
-
-For a filesystem reference, a `download()` operation copies the files from the referenced paths to construct the artifact directory. In the above example, the contents of `/mount/datasets/mnist` are copied into the directory `artifacts/mnist:v0/`. If an artifact contains a reference to a file that was overwritten, then `download()` will throw an error because the artifact can no longer be reconstructed.
-
-Putting it all together, you can use the following code to track a dataset under a mounted filesystem that feeds into a training job:
-
-```python
-import wandb
-
-run = wandb.init()
-
-artifact = wandb.Artifact("mnist", type="dataset")
-artifact.add_reference("file:///mount/datasets/mnist/")
-
-# Track the artifact and mark it as an input to
-# this run in one swoop. A new artifact version
-# is only logged if the files under the directory
-# changed.
-run.use_artifact(artifact)
-
-artifact_dir = artifact.download()
-
-# Perform training here...
-```
-
-To track a model, log the model artifact after the training script writes the model files to the mount point:
-
-```python
-import wandb
-
-run = wandb.init()
-
-# Training here...
-
-# Write model to disk
-
-model_artifact = wandb.Artifact("cnn", type="model")
-model_artifact.add_reference("file:///mount/cnn/my_model.h5")
-run.log_artifact(model_artifact)
-```
-
-
-
\ No newline at end of file
diff --git a/content/en/guides/core/artifacts/update-an-artifact.md b/content/en/guides/core/artifacts/update-an-artifact.md
deleted file mode 100644
index 5930171342..0000000000
--- a/content/en/guides/core/artifacts/update-an-artifact.md
+++ /dev/null
@@ -1,87 +0,0 @@
----
-description: Update an existing Artifact inside and outside of a W&B Run.
-menu:
- default:
- identifier: update-an-artifact
- parent: artifacts
-title: Update an artifact
-weight: 4
----
-
-Pass desired values to update the `description`, `metadata`, and `alias` of an artifact. Call the `save()` method to update the artifact on the W&B servers. You can update an artifact during a W&B Run or outside of a Run.
-
-{{% alert title="When to use Artifact.save() or wandb.Run.log_artifact()" %}}
-- Use `Artifact.save()` to update an existing artifact without creating a new run.
-- Use `wandb.Run.log_artifact()` to create a new artifact and associate it with a specific run.
-{{% /alert %}}
-
-Use the W&B Public API ([`wandb.Api`]({{< relref "/ref/python/public-api/api.md" >}})) to update an artifact outside of a run. Use the Artifact API ([`wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md" >}})) to update an artifact during a run.
-
-{{% alert color="secondary" %}}
-You can not update the alias of artifact linked to a model in Model Registry.
-{{% /alert %}}
-
-{{< tabpane text=true >}}
- {{% tab header="During a run" %}}
-
-The proceeding code example demonstrates how to update the description of an artifact using the [`wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md" >}}) API:
-
-```python
-import wandb
-
-run = wandb.init(project="")
-artifact = run.use_artifact(":")
-artifact.description = ""
-artifact.save()
-```
- {{% /tab %}}
- {{% tab header="Outside of a run" %}}
-The proceeding code example demonstrates how to update the description of an artifact using the `wandb.Api` API:
-
-```python
-import wandb
-
-api = wandb.Api()
-
-artifact = api.artifact("entity/project/artifact:alias")
-
-# Update the description
-artifact.description = "My new description"
-
-# Selectively update metadata keys
-artifact.metadata["oldKey"] = "new value"
-
-# Replace the metadata entirely
-artifact.metadata = {"newKey": "new value"}
-
-# Add an alias
-artifact.aliases.append("best")
-
-# Remove an alias
-artifact.aliases.remove("latest")
-
-# Completely replace the aliases
-artifact.aliases = ["replaced"]
-
-# Persist all artifact modifications
-artifact.save()
-```
-
-For more information, see the Weights and Biases [Artifact API]({{< relref "/ref/python/experiments/artifact.md" >}}).
- {{% /tab %}}
- {{% tab header="With collections" %}}
-You can also update an Artifact collection in the same way as a singular artifact:
-
-```python
-import wandb
-run = wandb.init(project="")
-api = wandb.Api()
-artifact = api.artifact_collection(type="", collection="")
-artifact.name = ""
-artifact.description = ""
-artifact.save()
-```
-For more information, see the [Artifacts Collection]({{< relref "/ref/python/public-api/api.md" >}}) reference.
- {{% /tab %}}
-{{% /tabpane %}}
-
diff --git a/content/en/guides/core/automations/_index.md b/content/en/guides/core/automations/_index.md
deleted file mode 100644
index ebc72b6259..0000000000
--- a/content/en/guides/core/automations/_index.md
+++ /dev/null
@@ -1,55 +0,0 @@
----
-menu:
- default:
- identifier: automations
- parent: core
-title: Automations
-weight: 4
-url: guides/automations
-cascade:
-- url: guides/automations/:filename
----
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-cloud-only.md" >}}
-{{% /pageinfo %}}
-
-This page describes _automations_ in W&B. [Create an automation]({{< relref "create-automations/" >}}) to trigger workflow steps, such as automated model testing and deployment, based on an event in W&B, such as when an [artifact]({{< relref "/guides/core/artifacts" >}}) artifact version is created or when a [run metric]({{< relref "/guides/models/track/runs.md" >}}) meets or changes by a threshold.
-
-For example, an automation can notify a Slack channel when a new version is created, trigger an automated testing webhook when the `production` alias is added to an artifact, or start a validation job only when a run's `loss` is within acceptable bounds.
-
-## Overview
-An automation can start when a specific [event]({{< relref "automation-events.md" >}}) occurs in a registry or project.
-
-In a [Registry]({{< relref "/guides/core/registry/">}}), an automation can start:
-- When a new artifact version is linked to a collection. For example, trigger testing and validation workflows for new candidate models.
-- When an alias is added to an artifact version. For example, trigger a deployment workflow when an alias is added to a model version.
-
-In a [project]({{< relref "/guides/models/track/project-page.md" >}}), an automation can start:
-- When a new version is added to an artifact. For example, start a training job when a new version of a dataset artifact is added to a given collection.
-- When an alias is added to an artifact version. For example, trigger a PII redaction workflow when the alias "redaction" is added to a dataset artifact.
-- When a tag is added to an artifact version. For example, trigger a geo-specific workflow when the tag "europe" is added to an artifact version.
-- When a metric for a run meets or exceeds a configured threshold.
-- When a metric for a run changes by a configured threshold.
-- When a run's status changes to **Running**, **Failed**, or **Finished**.
-
-Optionally, you can:
-- Filter artifacts, aliases, or tags by name
-- Filter runs by user or run name.
-
-For more details, see [Automation events and scopes]({{< relref "automation-events.md" >}}).
-
-To [create an automation]({{< relref "create-automations/" >}}), you:
-
-1. If required, configure [secrets]({{< relref "/guides/core/secrets.md" >}}) for sensitive strings the automation requires, such as access tokens, passwords, or sensitive configuration details. Secrets are defined in your **Team Settings**. Secrets are most commonly used in webhook automations to securely pass credentials or tokens to the webhook's external service without exposing it in plain text or hard-coding it in the webhook's payload.
-1. Configure team-level webhook or Slack integrations to authorize W&B to post to Slack or run the webhook on your behalf. A single automation action (webhook or Slack notification) can be used by multiple automations. These actions are defined in your **Team Settings**.
-1. In the project or registry, create the automation:
- 1. Define the [event]({{< relref "#automation-events" >}}) to watch for, such as when a new artifact version is added.
- 1. Define the action to take when the event occurs (posting to a Slack channel or running a webhook). For a webhook, specify a secret to use for the access token and/or a secret to send with the payload, if required.
-
-## Limitations
-[Run metric automations]({{< relref "automation-events.md#run-metrics-events">}}) are currently supported only in [W&B Multi-tenant Cloud]({{< relref "/guides/hosting/#wb-multi-tenant-cloud" >}}).
-
-## Next steps
-- [Create an automation]({{< relref "create-automations/" >}}).
-- Learn about [Automation events and scopes]({{< relref "automation-events.md" >}}).
-- [Create a secret]({{< relref "/guides/core/secrets.md" >}}).
diff --git a/content/en/guides/core/automations/automation-events.md b/content/en/guides/core/automations/automation-events.md
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index bb781baeb1..0000000000
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----
-menu:
- default:
- identifier: automation-scopes
- parent: automations
-title: Automation events and scopes
-weight: 2
----
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-cloud-only.md" >}}
-{{% /pageinfo %}}
-
-An automation can start when a specific event occurs within a project or registry. This page describes the events that can trigger an automation within each scope. Learn more about automations in the [Automations overview]({{< relref "/guides/core/automations/" >}}) or [Create an automation]({{< relref "create-automations/" >}}).
-
-## Registry
-This section describes the scopes and events for an automation in a [Registry]({{< relref "/guides/core/registry/">}}).
-
-1. Navigate to the **Registry** App at https://wandb.ai/registry/.
-1. Click the name of a registry, then view and create automations in the **Automations** tab.
-
-
-
-Learn more about [creating automations]({{< relref "create-automations/" >}}).
-
-### Scopes
-A [Registry]({{< relref "/guides/core/registry/">}}) automation watches for the event taking place on any collection within a specific registry, including collections added in the future.
-
-### Events {#registry-events}
-A Registry automation can watch for these events:
-- **A new version is linked to a collection**: Test and validate new models or datasets when they are added to a registry.
-- **An artifact alias is added**: Trigger a specific step of your workflow when a new artifact version has a specific alias applied. For example, deploy a model when it has the `production` alias applied.
-
-## Project
-This section describes the scopes and events for an automation in a [project]({{< relref "/guides/models/track/project-page.md" >}}).
-
-1. Navigate to your W&B project on the W&B App at `https://wandb.ai//`.
-1. View and create automations in the **Automations** tab.
-
-
-
-Learn more about [creating automations]({{< relref "create-automations/" >}}).
-
-### Scopes
-A project-level automation watches for the event taking place on any collection in the project. Depending on the event you specify, you can further limit the scope of the automation.
-
-### Artifact events
-This section describes the events related to an artifact that can trigger an automation.
-
-- **A new version is added to an artifact**: Apply recurring actions to each version of an artifact. For example, start a training job when a new dataset artifact version is created. To limit the automation's scope, select a specific artifact in the **Artifact filter** field.
-- **An artifact alias is added**: Trigger a specific step of your workflow when a new artifact version in a project has an alias applied that matches the **Alias regex** you specify. For example, run a series of downstream processing steps when an artifact has the `test-set-quality-check` alias applied, or run a workflow each time a new artifact version has the `latest` alias. Only one artifact version can have a given alias at a point in time.
-- **An artifact tag is added**: Trigger a specific step of your workflow when an artifact version in a project has a tag applied that matches the **Tag regex** you specify. For example, specify `^europe.*` to trigger a geo-specific workflow when a tag beginning with the string `europe` is added to an artifact version. Artifact tags are used for grouping and filtering, and a given tag can be assigned to multiple artifact versions simultaneously.
-
-### Run events
-An automation can be triggered by a change in a [run's status]({{< relref "/guides/models/track/runs/#run-states" >}}) or a change in a [metric value]({{< relref "/guides/models/track/log/#what-data-is-logged-with-specific-wb-api-calls" >}}).
-
-#### Run status change
-{{% alert %}}
-- Currently available only in [W&B Multi-tenant Cloud]({{< relref "/guides/hosting/#wb-multi-tenant-cloud" >}}).
-- A run with **Killed** status cannot trigger an automation. This status indicates that the run was stopped forcibly by an admin user.
-{{% /alert %}}
-
-Trigger a workflow when a run changes its [status]({{< relref "/guides/models/track/runs/_index.md#run-states" >}}) to **Running**, **Finished**, or **Failed**. Optionally, you can further limit the runs that can trigger an automation by specifying a user or run name filter.
-
-
-
-Because run status is a property of the entire run, you can create a run status automation only from the the **Automations** page, not from a workspace.
-
-#### Run metrics change
-{{% alert %}}
-Currently available only in [W&B Multi-tenant Cloud]({{< relref "/guides/hosting/#wb-multi-tenant-cloud" >}}).
-{{% /alert %}}
-
-Trigger a workflow based on a logged value for a metric, either a metric in a run's history or a [system metric]({{< relref "/ref/python/experiments/system-metrics.md" >}}) such as `cpu`, which tracks the percentage of CPU utilization. W&B logs system metrics automatically every 15 seconds.
-
-You can create a run metrics automation from the project's **Automations** tab or directly from a line plot panel in a workspace.
-
-To set up a run metric automation, you configure how to compare the metric's value with the threshold you specify. Your choices depend on the event type and on any filters you specify.
-
-Optionally, you can further limit the runs that can trigger an automation by specifying a user or run name filter.
-
-##### Threshold
-For **Run metrics threshold met** events, you configure:
-1. The window of most recently logged values to consider (defaults to 5).
-1. Whether to evaluate the **Average**, **Min**, or **Max** value within the window.
-1. The comparison to make:
- - Above
- - Above or equal to
- - Below
- - Below or equal to
- - Not equal to
- - Equal to
-
-For example, trigger an automation when average `accuracy` is above `.6`.
-
-
-
-##### Change threshold
-For **Run metrics change threshold met** events, the automation uses two "windows" of values to check whether to start:
-
-- The _current window_ of recently logged values to consider (defaults to 10).
-- The _prior window_ of recently logged values to consider (defaults to 50).
-
-The current and prior windows are consecutive and do not overlap.
-
-To create the automation, you configure:
-1. The current window of logged values (defaults to 10).
-1. The prior window of logged values (defaults to 50).
-1. Whether to evaluate the values as relative or absolute (defaults to **Relative**).
-1. The comparison to make:
- - Increases by at least
- - Decreases by at least
- - Increases or decreases by at least
-
-For example, trigger an automation when average `loss` decreases by at least `.25`.
-
-
-
-#### Run filters
-This section describes how the automation selects runs to evaluate.
-
-- By default, any run in the project triggers the automation when the event occurs. You can limit which runs trigger an automation by configuring one of the following filters:
- - **Filter to one user's runs**: Include only runs created by the specified user.
- - **Filter on run name**: Include only runs whose names match the given regular expression.
-
- For details, see [Create automations]({{< relref "/guides/core/automations/create-automations/" >}}).
-- Each run is considered individually and can potentially trigger the automation.
-- Each run's values are put into a separate window and compared to the threshold separately.
-- In a 24 hour period, a particular automation can fire at most once per run.
-
-## Next steps
-- [Create a Slack automation]({{< relref "create-automations/slack.md" >}})
-- [Create a webhook automation]({{< relref "create-automations/webhook.md" >}})
diff --git a/content/en/guides/core/automations/create-automations/_index.md b/content/en/guides/core/automations/create-automations/_index.md
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----
-menu:
- default:
- identifier: create-automations
- parent: automations
-title: Create an automation
-weight: 1
-url: guides/automations/create-automations
-cascade:
-- url: guides/automations/create-automations/:filename
----
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-cloud-only.md" >}}
-{{% /pageinfo %}}
-
-This page gives an overview of creating and managing W&B [automations]({{< relref "/guides/core/automations/">}}). For more detailed instructions, refer to [Create a Slack automation]({{< relref "/guides/core/automations/create-automations/slack.md" >}}) or [Create a webhook automation]({{< relref "/guides/core/automations/create-automations/webhook.md" >}}).
-
-{{% alert %}}
-Looking for companion tutorials for automations?
-- [Learn to automatically triggers a Github Action for model evaluation and deployment](https://wandb.ai/wandb/wandb-model-cicd/reports/Model-CI-CD-with-W-B--Vmlldzo0OTcwNDQw).
-- [Watch a video demonstrating automatically deploying a model to a Sagemaker endpoint](https://www.youtube.com/watch?v=s5CMj_w3DaQ).
-- [Watch a video series introducing automations](https://youtube.com/playlist?list=PLD80i8An1OEGECFPgY-HPCNjXgGu-qGO6&feature=shared).
-{{% /alert %}}
-
-## Requirements
-- A team admin can create and manage automations for the team's projects, as well as components of their automations, such as webhooks, secrets, and Slack integrations. Refer to [Team settings]({{< relref "/guides/models/app/settings-page/teams/" >}}).
-- To create a registry automation, you must have access to the registry. Refer to [Configure Registry access]({{< relref "/guides/core/registry/configure_registry.md#registry-roles" >}}).
-- To create a Slack automation, you must have permission to post to the Slack instance and channel you select.
-
-## Create an automation
-Create an automation from the project or registry's **Automations** tab. At a high level, to create an automation, follow these steps:
-
-1. If necessary, [create a W&B secret]({{< relref "/guides/core/secrets.md" >}}) for each sensitive string required by the automation, such as an access token, password, or SSH key. Secrets are defined in your **Team Settings**. Secrets are most commonly used in webhook automations.
-1. Configure the webhook or Slack integration to authorize W&B to post to Slack or run the webhook on your behalf. A single webhook or Slack integration can be used by multiple automations. These actions are defined in your **Team Settings**.
-1. In the project or registry, create the automation, which specifies the event to watch for and the action to take (such as posting to Slack or running a webhook). When you create a webhook automation, you configure the payload it sends.
-
-Or, from a line plot in the workspace, you can quickly create a [run metric automation]({{< relref "/guides/core/automations/automation-events.md#run-events" >}}) for the metric it shows:
-
-1. Hover over the panel, then click the bell icon at the top of the panel.
-
- {{< img src="/images/automations/run_metric_automation_from_panel.png" alt="Automation bell icon location" >}}
-1. Configure the automation using the basic or advanced configuration controls. For example, apply a run filter to limit the scope of the automation, or configure an absolute threshold.
-
-For details, refer to:
-
-- [Create a Slack automation]({{< relref "slack.md" >}})
-- [Create a webhook automation]({{< relref "webhook.md" >}})
-
-## View and manage automations
-View and manage automations from a project or registry's **Automations** tab.
-
-- To view an automation's details, click its name.
-- To edit an automation, click its action `...` menu, then click **Edit automation**.
-- To delete an automation, click its action `...` menu, then click **Delete automation**.
-
-## Next steps
-- Learn more about [automation events and scopes]({{< relref "/guides/core/automations/automation-events.md" >}})
-- [Create a Slack automation]({{< relref "slack.md" >}}).
-- [Create a webhook automation]({{< relref "webhook.md" >}}).
-- [Create a secret]({{< relref "/guides/core/secrets.md" >}}).
diff --git a/content/en/guides/core/automations/create-automations/slack.md b/content/en/guides/core/automations/create-automations/slack.md
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----
-menu:
- default:
- identifier: create-slack-automations
- parent: create-automations
-title: Create a Slack automation
-weight: 1
----
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-cloud-only.md" >}}
-{{% /pageinfo %}}
-
-This page shows how to create a Slack [automation]({{< relref "/guides/core/automations/" >}}> ). To create a webhook automation, refer to [Create a webhook automation]({{< relref "/guides/core/automations/create-automations/webhook.md" >}}) instead.
-
-At a high level, to create a Slack automation, you take these steps:
-1. [Add a Slack integration]({{< relref "#add-a-slack-integration" >}}), which authorizes W&B to post to the Slack instance and channel.
-1. [Create the automation]({{< relref "#create-an-automation" >}}), which defines the [event]({{< relref "/guides/core/automations/automation-events.md" >}}) to watch for and the channel to notify.
-
-## Add a Slack integration
-A team admin can add a Slack integration to the team.
-
-1. Log in to W&B and go to **Team Settings**.
-1. In the **Slack channel integrations** section, click **Connect Slack** to add a new Slack instance. To add a channel for an existing Slack instance, click **New integration**.
-
- 
-1. If necessary, sign in to Slack in your browser. When prompted, grant W&B permission to post to the Slack channel you select. Read the page, then click **Search for a channel** and begin typing the channel name. Select the channel from the list, then click **Allow**.
-1. In Slack, go to the channel you selected. If you see a post like `[Your Slack handle] added an integration to this channel: Weights & Biases`, the integration is configured correctly.
-
-Now you can [create an automation]({{< relref "#create-an-automation" >}}) that notifies the Slack channel you configured.
-
-## View and manage Slack integrations
-A team admin can view and manage the team's Slack instances and channels.
-
-1. Log in to W&B and go to **Team Settings**.
-1. View each Slack destination in the **Slack channel integrations** section.
-1. Delete a destination by clicking its trash icon.
-
-## Create an automation
-After you [add a Slack integration]({{< relref "#add-a-slack-integreation" >}}), select **Registry** or **Project**, then follow these steps to create an automation that notifies the Slack channel.
-
-{{< tabpane text=true >}}
-{{% tab "Registry" %}}
-A Registry admin can create automations in that registry.
-
-1. Log in to W&B.
-1. Click the name of a registry to view its details,
-1. To create an automation scoped to the registry, click the **Automations** tab, then click **Create automation**. An automation that is scoped to a registry is automatically applied to all of its collections (including those created in the future).
-1. Choose the [event]({{< relref "/guides/core/automations/automation-events.md#registry-events" >}}) to watch for.
-
- Fill in any additional fields that appear, which depend upon the event. For example, if you select **An artifact alias is added**, you must specify the **Alias regex**.
-
- Click **Next step**.
-
-1. Select the team that owns the [Slack integration]({{< relref "#add-a-slack-integration" >}}).
-1. Set **Action type** to **Slack notification**. Select the Slack channel, then click **Next step**.
-1. Provide a name for the automation. Optionally, provide a description.
-1. Click **Create automation**.
-
-{{% /tab %}}
-{{% tab "Project" %}}
-A W&B admin can create automations in a project.
-
-1. Log in to W&B.
-1. Go the project page and click the **Automations** tab, then click **Create automation**.
-
- Or, from a line plot in the workspace, you can quickly create a [run metric automation]({{< relref "/guides/core/automations/automation-events.md#run-events" >}}) for the metric it shows. Hover over the panel, then click the bell icon at the top of the panel.
- {{< img src="/images/automations/run_metric_automation_from_panel.png" alt="Automation bell icon location" >}}
-1. Choose the [event]({{< relref "/guides/core/automations/automation-events.md#project" >}}) to watch for.
-
- 1. Fill in any additional fields that appear. For example, if you select **An artifact alias is added**, you must specify the **Alias regex**.
-
- 1. For automations triggered by a run, optionally specify one or more run filters.
-
- - **Filter to one user's runs**: Include only runs created by the specified user. Click the toggle to turn on the filter, then specify a username.
- - **Filter on run name**: Include only runs whose names match the given regular expression. Click the toggle to turn on the filter, then specify a regular expression.
- 1. Click **Next step**.
-
-1. Select the team that owns the [Slack integration]({{< relref "#add-a-slack-integration" >}}).
-1. Set **Action type** to **Slack notification**. Select the Slack channel, then click **Next step**.
-1. Provide a name for the automation. Optionally, provide a description.
-1. Click **Create automation**.
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## View and manage automations
-
-{{< tabpane text=true >}}
-{{% tab "Registry" %}}
-
-Manage the registry's automations from the registry's **Automations** tab.
-- To view an automation's details, click its name.
-- To edit an automation, click its action `...` menu, then click **Edit automation**.
-- To delete an automation, click its action `...` menu, then click **Delete automation**. Confirmation is required.
-{{% /tab %}}
-{{% tab "Project" %}}
-A W&B admin can view and manage a project's automations from the project's **Automations** tab.
-
-- To view an automation's details, click its name.
-- To edit an automation, click its action `...` menu, then click **Edit automation**.
-- To delete an automation, click its action `...` menu, then click **Delete automation**. Confirmation is required.
-{{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/automations/create-automations/webhook.md b/content/en/guides/core/automations/create-automations/webhook.md
deleted file mode 100644
index 833cc531a5..0000000000
--- a/content/en/guides/core/automations/create-automations/webhook.md
+++ /dev/null
@@ -1,313 +0,0 @@
----
-menu:
- default:
- identifier: create-webhook-automations
- parent: automations
-title: Create a webhook automation
-weight: 3
----
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-cloud-only.md" >}}
-{{% /pageinfo %}}
-
-This page shows how to create a webhook [automation]({{< relref "/guides/core/automations/" >}}> ). To create a Slack automation, refer to [Create a Slack automation]({{< relref "/guides/core/automations/create-automations/slack.md" >}}) instead.
-
-At a high level, to create a webhook automation, you take these steps:
-1. If necessary, [create a W&B secret]({{< relref "/guides/core/secrets.md" >}}) for each sensitive string required by the automation, such as an access token, password, or SSH key. Secrets are defined in your **Team Settings**.
-1. [Create a webhook]({{< relref "#create-a-webhook" >}}) to define the endpoint and authorization details and grant the integration access to any secrets it needs.
-1. [Create the automation]({{< relref "#create-an-automation" >}}) to define the [event]({{< relref "/guides/core/automations/automation-events.md" >}}) to watch for and the payload W&B will send. Grant the automation access to any secrets it needs for the payload.
-
-## Create a webhook
-A team admin can add a webhook for the team.
-
-{{% alert %}}
-If the webhook requires a Bearer token or its payload requires a sensitive string, [create a secret that contains it]({{< relref "/guides/core/secrets.md#add-a-secret" >}}) before creating the webhook. You can configure at most one access token and one other secret for a webhook. Your webhook's authentication and authorization requirements are determined by the webhook's service.
-{{% /alert %}}
-
-1. Log in to W&B and go to **Team Settings** page.
-1. In the **Webhooks** section, click **New webhook**.
-1. Provide a name for the webhook.
-1. Provide the endpoint URL for the webhook.
-1. If the webhook requires a Bearer token, set **Access token** to the [secret]({{< relref "/guides/core/secrets.md" >}}) that contains it. When using the webhook automation, W&B sets the `Authorization: Bearer` HTTP header to the access token, and you can access the token in the `${ACCESS_TOKEN}` [payload variable]({{< relref "#payload-variables" >}}). Learn more about the structure of the `POST` request W&B sends to the webhook service in [Troubleshoot your webhook]({{< relref "#troubleshoot-your-webhook" >}}).
-1. If the webhook requires a password or other sensitive string in its payload, set **Secret** to the secret that contains it. When you configure the automation that uses the webhook, you can access the secret as a [payload variable]({{< relref "#payload-variables" >}}) by prefixing its name with `$`.
-
- If the webhook's access token is stored in a secret, you must _also_ complete the next step to specify the secret as the access token.
-1. To verify that the W&B can connect and authenticate to the endpoint:
- 1. Optionally, provide a payload to test. To refer to a secret the webhook has access to in the payload, prefix its name with `$`. This payload is only used for testing and is not saved. You configure an automation's payload when you [create the automation]({{< relref "#create-a-webhook-automation" >}}). See [Troubleshoot your webhook]({{< relref "#troubleshoot-your-webhook" >}}) to view where the secret and access token are specified in the `POST` request.
- 1. Click **Test**. W&B attempts to connect to the webhook's endpoint using the credentials you configured. If you provided a payload, W&B sends it.
-
- If the test does not succeed, verify the webhook's configuration and try again. If necessary, refer to [Troubleshoot your webhook]({{< relref "#troubleshoot-your-webhook" >}}).
-
-
-
-Now you can [create an automation]({{< relref "#create-a-webhook-automation" >}}) that uses the webhook.
-
-## Create an automation
-After you [configure a webhook]({{< relref "#create-a-webhook" >}}), select **Registry** or **Project**, then follow these steps to create an automation that triggers the webhook.
-
-{{< tabpane text=true >}}
-{{% tab "Registry" %}}
-A Registry admin can create automations in that registry. Registry automations are applied to all collections in the registry, including those added in the future.
-
-1. Log in to W&B.
-1. Click the name of a registry to view its details,
-1. To create an automation scoped to the registry, click the **Automations** tab, then click **Create automation**.
-
-1. Choose the [event]({{< relref "/guides/core/automations/automation-events.md#registry-events" >}}) to watch for.
-
- Fill in any additional fields that appear. For example, if you select **An artifact alias is added**, you must specify the **Alias regex**.
-
- Click **Next step**.
-
-1. Select the team that owns the [webhook]({{< relref "#create-a-webhook" >}}).
-1. Set **Action type** to **Webhooks**. then select the [webhook]({{< relref "#create-a-webhook" >}}) to use.
-1. If you configured an access token for the webhook, you can access the token in the `${ACCESS_TOKEN}` [payload variable]({{< relref "#payload-variables" >}}). If you configured a secret for the webhook, you can access it in the payload by prefixing its name with `$`. Your webhook's requirements are determined by the webhook's service.
-1. Click **Next step**.
-1. Provide a name for the automation. Optionally, provide a description. Click **Create automation**.
-
-{{% /tab %}}
-{{% tab "Project" %}}
-A W&B admin can create automations in a project.
-
-1. Log in to W&B and go to the project page.
-1. In the sidebar, click **Automations**, then click **Create automation**.
-
- Or, from a line plot in the workspace, you can quickly create a [run metric automation]({{< relref "/guides/core/automations/automation-events.md#run-events" >}}) for the metric it shows. Hover over the panel, then click the bell icon at the top of the panel.
- {{< img src="/images/automations/run_metric_automation_from_panel.png" alt="Automation bell icon location" >}}
-1. Choose the [event]({{< relref "/guides/core/automations/automation-events.md#project" >}}) to watch for, such as when an artifact alias is added or when a run metric meets a given threshold.
-
- 1. Fill in any additional fields that appear, which depend upon the event. For example, if you select **An artifact alias is added**, you must specify the **Alias regex**.
-
- 1. For automations triggered by a run, optionally specify one or more run filters.
-
- - **Filter to one user's runs**: Include only runs created by the specified user. Click the toggle to turn on the filter, then specify a username.
- - **Filter on run name**: Include only runs whose names match the given regular expression. Click the toggle to turn on the filter, then specify a regular expression.
-
- The automation is applied to all collections in the project, including those added in the future.
- 1. Click **Next step**.
-1. Select the team that owns the [webhook]({{< relref "#create-a-webhook" >}}).
-1. Set **Action type** to **Webhooks**. then select the [webhook]({{< relref "#create-a-webhook" >}}) to use.
-1. If your webhook requires a payload, construct it and paste it into the **Payload** field. If you configured an access token for the webhook, you can access the token in the `${ACCESS_TOKEN}` [payload variable]({{< relref "#payload-variables" >}}). If you configured a secret for the webhook, you can access it in the payload by prefixing its name with `$`. Your webhook's requirements are determined by the webhook's service.
-1. Click **Next step**.
-1. Provide a name for the automation. Optionally, provide a description. Click **Create automation**.
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## View and manage automations
-{{< tabpane text=true >}}
-{{% tab "Registry" %}}
-
-Manage a registry's automations from the registry's **Automations** tab.
-
-- To view an automation's details, click its name.
-- To edit an automation, click its action `...` menu, then click **Edit automation**.
-- To delete an automation, click its action `...` menu, then click **Delete automation**. Confirmation is required.
-
-{{% /tab %}}
-{{% tab "Project" %}}
-A W&B admin can view and manage a project's automations from the project's **Automations** tab.
-
-- To view an automation's details, click its name.
-- To edit an automation, click its action `...` menu, then click **Edit automation**.
-- To delete an automation, click its action `...` menu, then click **Delete automation**. Confirmation is required.
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Payload reference
-Use these sections to construct your webhoook's payload. For details about testing your webhook and its payload, refer to [Troubleshoot your webhook]({{< relref "#troubleshoot-your-webhook" >}}).
-
-### Payload variables
-This section describes the variables you can use to construct your webhook's payload.
-
-| Variable | Details |
-|----------|---------|
-| `${project_name}` | The name of the project that owns the mutation that triggered the action. |
-| `${entity_name}` | The name of the entity or team that owns the mutation that triggered the action.
-| `${event_type}` | The type of event that triggered the action. |
-| `${event_author}` | The user that triggered the action. |
-| `${alias}` | Contains an artifact's alias if the automation is triggered by the **An artifact alias is added** event. For other automations, this variable is blank. |
-| `${tag}` | Contains an artifact's tags if the automation is triggered by the **An artifact tag is added** event. For other automations, this variable is blank. |
-| `${artifact_collection_name}` | The name of the artifact collection that the artifact version is linked to. |
-| `${artifact_metadata.}` | The value of an arbitrary top-level metadata key from the artifact version that triggered the action. Replace `` with the name of a top-level metadata key. Only top-level metadata keys are available in the webhook's payload. |
-| `${artifact_version}` | The [`Wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md/" >}}) representation of the artifact version that triggered the action. |
-| `${artifact_version_string}` | The `string` representation of the artifact version that triggered the action. |
-| `${ACCESS_TOKEN}` | The value of the access token configured in the [webhook]({{< relref "#create-a-webhook" >}}), if an access token is configured. The access token is automatically passed in the `Authorization: Bearer` HTTP header. |
-| `${SECRET_NAME}` | If configured, the value of a secret configured in the [webhook]({{< relref "#create-a-webhook" >}}). Replace `SECRET_NAME` with the name of the secret. |
-
-### Payload examples
-This section includes examples of webhook payloads for some common use cases. The examples demonstrate how to use [payload variables]({{< relref "#payload-variables" >}}).
-
-{{< tabpane text=true >}}
-{{% tab header="GitHub repository dispatch" value="github" %}}
-
-{{% alert %}}
-Verify that your access tokens have required set of permissions to trigger your GHA workflow. For more information, [see these GitHub Docs](https://docs.github.com/en/rest/repos/repos?#create-a-repository-dispatch-event).
-{{% /alert %}}
-
-Send a repository dispatch from W&B to trigger a GitHub action. For example, suppose you have a GitHub workflow file that accepts a repository dispatch as a trigger for the `on` key:
-
-```yaml
-on:
-repository_dispatch:
- types: BUILD_AND_DEPLOY
-```
-
-The payload for the repository might look something like:
-
-```json
-{
- "event_type": "BUILD_AND_DEPLOY",
- "client_payload":
- {
- "event_author": "${event_author}",
- "artifact_version": "${artifact_version}",
- "artifact_version_string": "${artifact_version_string}",
- "artifact_collection_name": "${artifact_collection_name}",
- "project_name": "${project_name}",
- "entity_name": "${entity_name}"
- }
-}
-```
-
-{{% alert %}}
-The `event_type` key in the webhook payload must match the `types` field in the GitHub workflow YAML file.
-{{% /alert %}}
-
-The contents and positioning of rendered template strings depends on the event or model version the automation is configured for. `${event_type}` will render as either `LINK_ARTIFACT` or `ADD_ARTIFACT_ALIAS`. See below for an example mapping:
-
-```text
-${event_type} --> "LINK_ARTIFACT" or "ADD_ARTIFACT_ALIAS"
-${event_author} --> ""
-${artifact_version} --> "wandb-artifact://_id/QXJ0aWZhY3Q6NTE3ODg5ODg3""
-${artifact_version_string} --> "/model-registry/:"
-${artifact_collection_name} --> ""
-${project_name} --> "model-registry"
-${entity_name} --> ""
-```
-
-Use template strings to dynamically pass context from W&B to GitHub Actions and other tools. If those tools can call Python scripts, they can consume the registered model artifacts through the [W&B API]({{< relref "/guides/core/artifacts/download-and-use-an-artifact.md" >}}).
-
-- For more information about repository dispatch, see the [official documentation on the GitHub Marketplace](https://github.com/marketplace/actions/repository-dispatch).
-
-- Watch the videos [Webhook Automations for Model Evaluation](https://www.youtube.com/watch?v=7j-Mtbo-E74&ab_channel=Weights%26Biases) and [Webhook Automations for Model Deployment](https://www.youtube.com/watch?v=g5UiAFjM2nA&ab_channel=Weights%26Biases), which guide you to create automations for model evaluation and deployment.
-
-- Review a W&B [report](https://wandb.ai/wandb/wandb-model-cicd/reports/Model-CI-CD-with-W-B--Vmlldzo0OTcwNDQw), which illustrates how to use a Github Actions webhook automation for Model CI. Check out this [GitHub repository](https://github.com/hamelsmu/wandb-modal-webhook) to learn how to create model CI with a Modal Labs webhook.
-
-{{% /tab %}}
-
-{{% tab header="Microsoft Teams notification" value="microsoft"%}}
-
-This example payload shows how to notify your Teams channel using a webhook:
-
-```json
-{
-"@type": "MessageCard",
-"@context": "http://schema.org/extensions",
-"summary": "New Notification",
-"sections": [
- {
- "activityTitle": "Notification from WANDB",
- "text": "This is an example message sent via Teams webhook.",
- "facts": [
- {
- "name": "Author",
- "value": "${event_author}"
- },
- {
- "name": "Event Type",
- "value": "${event_type}"
- }
- ],
- "markdown": true
- }
-]
-}
-```
-
-You can use template strings to inject W&B data into your payload at the time of execution (as shown in the Teams example above).
-
-{{% /tab %}}
-
-{{% tab header="Slack notifications" value="slack"%}}
-
-{{% alert %}}
-This section is provided for historical purposes. If you currently use a webhook to integrate with Slack, W&B recommends that you update your configuration to use the [new Slack integration]({{ relref "#create-a-slack-automation"}}) instead.
-{{% /alert %}}
-
-Set up your Slack app and add an incoming webhook integration with the instructions highlighted in the [Slack API documentation](https://api.slack.com/messaging/webhooks). Ensure that you have the secret specified under `Bot User OAuth Token` as your W&B webhook’s access token.
-
-The following is an example payload:
-
-```json
-{
- "text": "New alert from WANDB!",
-"blocks": [
- {
- "type": "section",
- "text": {
- "type": "mrkdwn",
- "text": "Registry event: ${event_type}"
- }
- },
- {
- "type":"section",
- "text": {
- "type": "mrkdwn",
- "text": "New version: ${artifact_version_string}"
- }
- },
- {
- "type": "divider"
- },
- {
- "type": "section",
- "text": {
- "type": "mrkdwn",
- "text": "Author: ${event_author}"
- }
- }
- ]
-}
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Troubleshoot your webhook
-Interactively troubleshoot your webhook with the W&B App UI or programmatically with a Bash script. You can troubleshoot a webhook when you create a new webhook or edit an existing webhook.
-
-For details about the format W&B uses for the `POST` request, refer to the **Bash script** tab.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App UI" value="app" %}}
-
-A team admin can test a webhook interactively with the W&B App UI.
-
-1. Navigate to your W&B Team Settings page.
-2. Scroll to the **Webhooks** section.
-3. Click on the horizontal three docs (meatball icon) next to the name of your webhook.
-4. Select **Test**.
-5. From the UI panel that appears, paste your POST request to the field that appears.
- {{< img src="/images/models/webhook_ui.png" alt="Demo of testing a webhook payload" >}}
-6. Click on **Test webhook**. Within the W&B App UI, W&B posts the response from your endpoint.
- {{< img src="/images/models/webhook_ui_testing.gif" alt="Demo of testing a webhook" >}}
-
-Watch the video [Testing Webhooks in W&B](https://www.youtube.com/watch?v=bl44fDpMGJw&ab_channel=Weights%26Biases) for a demonstration.
-{{% /tab %}}
-
-{{% tab header="Bash script" value="bash"%}}
-
-This shell script shows one method to generate a `POST` request similar to the request W&B sends to your webhook automation when it is triggered.
-
-Copy and paste the code below into a shell script to troubleshoot your webhook. Specify your own values for:
-
-* `ACCESS_TOKEN`
-* `SECRET`
-* `PAYLOAD`
-* `API_ENDPOINT`
-
-{{< prism file="/webhook_test.sh" title="webhook_test.sh">}}{{< /prism >}}
-
-{{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/registry/_index.md b/content/en/guides/core/registry/_index.md
deleted file mode 100644
index 33392f337f..0000000000
--- a/content/en/guides/core/registry/_index.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-menu:
- default:
- identifier: registry
- parent: core
-title: Registry
-weight: 3
-url: guides/registry
-cascade:
-- url: guides/registry/:filename
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb_registry/zoo_wandb.ipynb" >}}
-
-W&B Registry is a curated central repository of [W&B Artifact]({{< relref "/guides/core/artifacts/" >}}) versions within your organization. Users who [have permission]({{< relref "./configure_registry.md" >}}) within your organization can [download and use artifacts]({{< relref "./download_use_artifact.md" >}}), share, and collaboratively manage the lifecycle of all artifacts, regardless of the team that user belongs to.
-
-You can use the Registry to [track artifact versions]({{< relref "./link_version.md" >}}), audit the history of an artifact's usage and changes, ensure governance and compliance of your artifacts, and [automate downstream processes such as model CI/CD]({{< relref "/guides/core/automations/" >}}).
-
-In summary, use W&B Registry to:
-
-- [Promote]({{< relref "./link_version.md" >}}) artifact versions that satisfy a machine learning task to other users in your organization.
-- Organize [artifacts with tags]({{< relref "./organize-with-tags.md" >}}) so that you can find or reference specific artifacts.
-- Track an [artifact’s lineage]({{< relref "/guides/core/registry/lineage.md" >}}) and audit the history of changes.
-- [Automate]({{< relref "/guides/core/automations/" >}}) downstream processes such as model CI/CD.
-- [Limit who in your organization]({{< relref "./configure_registry.md" >}}) can access artifacts in each registry.
-
-
-
-{{< img src="/images/registry/registry_landing_page.png" alt="W&B Registry" >}}
-
-The preceding image shows the Registry App with "Model" and "Dataset" core registries along with custom registries.
-
-
-## Learn the basics
-Each organization initially contains two registries that you can use to organize your model and dataset artifacts called **Models** and **Datasets**, respectively. You can create [additional registries to organize other artifact types based on your organization's needs]({{< relref "./registry_types.md" >}}).
-
-Each [registry]({{< relref "./configure_registry.md" >}}) consists of one or more [collections]({{< relref "./create_collection.md" >}}). Each collection represents a distinct task or use case.
-
-{{< img src="/images/registry/homepage_registry.png" alt="W&B Registry" >}}
-
-To add an artifact to a registry, you first log a [specific artifact version to W&B]({{< relref "/guides/core/artifacts/create-a-new-artifact-version.md" >}}). Each time you log an artifact, W&B automatically assigns a version to that artifact. Artifact versions use 0 indexing, so the first version is `v0`, the second version is `v1`, and so on.
-
-Once you log an artifact to W&B, you can then link that specific artifact version to a collection in the registry.
-
-{{% alert %}}
-The term "link" refers to pointers that connect where W&B stores the artifact and where the artifact is accessible in the registry. W&B does not duplicate artifacts when you link an artifact to a collection.
-{{% /alert %}}
-
-As an example, the proceeding code example shows how to log and link a model artifact called "my_model.txt" to a collection named "first-collection" in the [core registry]({{< relref "./registry_types.md" >}}):
-
-1. Initialize a W&B Run.
-2. Log the artifact to W&B.
-3. Specify the name of the collection and registry to link your artifact version to.
-4. Link the artifact to the collection.
-
-Save this Python code to a script and run it. W&B Python SDK version 0.18.6 or newer is required.
-
-```python title="hello_collection.py"
-import wandb
-import random
-
-# Initialize a W&B Run to track the artifact
-run = wandb.init(project="registry_quickstart")
-
-# Create a simulated model file so that you can log it
-with open("my_model.txt", "w") as f:
- f.write("Model: " + str(random.random()))
-
-# Log the artifact to W&B
-logged_artifact = run.log_artifact(
- artifact_or_path="./my_model.txt",
- name="gemma-finetuned",
- type="model" # Specifies artifact type
-)
-
-# Specify the name of the collection and registry
-# you want to publish the artifact to
-COLLECTION_NAME = "first-collection"
-REGISTRY_NAME = "model"
-
-# Link the artifact to the registry
-run.link_artifact(
- artifact=logged_artifact,
- target_path=f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
-)
-```
-
-W&B automatically creates a collection for you if the collection you specify in the returned run object's `link_artifact(target_path = "")` method does not exist within the registry you specify.
-
-{{% alert %}}
-The URL that your terminal prints directs you to the project where W&B stores your artifact.
-{{% /alert %}}
-
-Navigate to the Registry App to view artifact versions that you and other members of your organization publish. To do so, first navigate to W&B. Select **Registry** in the left sidebar below **Applications**. Select the "Model" registry. Within the registry, you should see the "first-collection" collection with your linked artifact version.
-
-Once you link an artifact version to a collection within a registry, members of your organization can view, download, and manage your artifact versions, create downstream automations, and more if they have the proper permissions.
-
-{{% alert %}}
-If an artifact version logs metrics (such as by using `run.log_artifact()`), you can view metrics for that version from its details page, and you can compare metrics across artifact versions from the collection's page. Refer to [View linked artifacts in a registry]({{< relref "link_version.md#view-linked-artifacts-in-a-registry" >}}).
-{{% /alert %}}
-
-## Enable W&B Registry
-
-Based on your deployment type, satisfy the following conditions to enable W&B Registry:
-
-| Deployment type | How to enable |
-| ----- | ----- |
-| Multi-tenant Cloud | No action required. W&B Registry is available on the W&B App. |
-| Dedicated Cloud | Contact your account team to enable W&B Registry for your deployment. |
-| Self-Managed | For Server v0.70.0 or newer, no action required. For older supported Server versions, set the environment variable `ENABLE_REGISTRY_UI` to `true`. Refer to [Configure environment variables]({{< relref "/guides/hosting/env-vars.md" >}}). |
-
-
-## Resources to get started
-
-Depending on your use case, explore the following resources to get started with the W&B Registry:
-
-* Check out the tutorial video:
- * [Getting started with Registry from W&B](https://www.youtube.com/watch?v=p4XkVOsjIeM)
-* Take the W&B [Model CI/CD](https://www.wandb.courses/courses/enterprise-model-management) course and learn how to:
- * Use W&B Registry to manage and version your artifacts, track lineage, and promote models through different lifecycle stages.
- * Automate your model management workflows using webhooks.
- * Integrate the registry with external ML systems and tools for model evaluation, monitoring, and deployment.
-
-
-
-## Migrate from the legacy Model Registry to W&B Registry
-
-The legacy Model Registry is scheduled for deprecation with the exact date not yet decided. Before deprecating the legacy Model Registry, W&B will migrate the contents of the legacy Model Registry to the W&B Registry.
-
-
-See [Migrating from legacy Model Registry]({{< relref "./model_registry_eol.md" >}}) for more information about the migration process from the legacy Model Registry to W&B Registry.
-
-Until the migration occurs, W&B supports both the legacy Model Registry and the new Registry.
-
-{{% alert %}}
-To view the legacy Model Registry, navigate to the Model Registry in the W&B App. A banner appears at the top of the page that enables you to use the legacy Model Registry App UI.
-
-{{< img src="/images/registry/nav_to_old_model_reg.gif" alt="Legacy Model Registry UI" >}}
-{{% /alert %}}
-
-
-Reach out to support@wandb.com with any questions or to speak to the W&B Product Team about any concerns about the migration.
\ No newline at end of file
diff --git a/content/en/guides/core/registry/aliases.md b/content/en/guides/core/registry/aliases.md
deleted file mode 100644
index daf2103e1a..0000000000
--- a/content/en/guides/core/registry/aliases.md
+++ /dev/null
@@ -1,171 +0,0 @@
----
-title: Reference an artifact version with aliases
-weight: 5
----
-
-Reference a specific [artifact version]({{< relref "guides/core/artifacts/create-a-new-artifact-version" >}}) with one or more aliases. [W&B automatically assigns aliases]({{< relref "aliases#default-aliases" >}}) to each artifact you link with the same name. You can also [create one or more custom aliases]({{< relref "aliases#custom-aliases" >}}) to reference a specific artifact version.
-
-Aliases appear as rectangles with the name of that alias in the rectangle in the Registry UI. If an [alias is protected]({{< relref "aliases#protected-aliases" >}}), it appears as a gray rectangle with a lock icon. Otherwise, the alias appears as an orange rectangle. Aliases are not shared across registries.
-
-{{% alert title="When to use an alias versus using a tag" %}}
-Use an alias to reference a specific artifact version. Each alias within a collection is unique. Only one artifact version can have a specific alias at a time.
-
-Use tags to organize and group artifact versions or collections based on a common theme. Multiple artifact versions and collections can share the same tag.
-{{% /alert %}}
-
-When you add an alias to an artifact version, you can optionally start a [Registry automation]({{< relref "/guides/core/automations/automation-events/#registry" >}}) to notify a Slack channel or trigger a webhook.
-
-## Default aliases
-
-W&B automatically assigns the following aliases to each artifact version you link with the same name:
-
-* The `latest` alias to the most recent artifact version you link to a collection.
-* A unique version number. W&B counts each artifact version (zero indexing) you link. W&B uses the count number to assign a unique version number to that artifact.
-
-For example, if you link an artifact named `zoo_model` three times, W&B creates three aliases `v0`, `v1`, and `v2` respectively. `v2` also has the `latest` alias.
-
-## Custom aliases
-
-Create one or more custom aliases for a specific artifact versions based on your unique use case. For example:
-
-- You might use aliases such as `dataset_version_v0`, `dataset_version_v1`, and `dataset_version_v2` to identify which dataset a model was trained on.
-- You might use a `best_model` alias to keep track of the best performing artifact model version.
-
-Any user with a [**Member** or **Admin** registry role]({{< relref "guides/core/registry/configure_registry/#registry-roles" >}}) on a registry can add or remove a custom alias from a linked artifact in that registry. Users with the [**Restricted Viewer** or **Viewer** roles]({{< relref "guides/core/registry/configure_registry/#registry-roles" >}}) cannot add or remove aliases.
-
-{{% alert %}}
-[Protected aliases]({{< relref "aliases/#protected-aliases" >}}) provide a way to label and identify which artifact versions to protect from modification or deletion.
-{{% /alert %}}
-
-
-You can create a custom alias with the W&B Registry or the Python SDK. Based on your use case, click on a tab below that best fits your needs.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B Registry" value="app" %}}
-
-1. Navigate to the W&B Registry.
-2. Click the **View details** button in a collection.
-3. Within the **Versions** section, click the **View** button for a specific artifact version.
-4. Click the **+** button to add one or more aliases next to the **Aliases** field.
-
-{{% /tab %}}
-
-{{% tab header="Python SDK" value="python" %}}
-When you link an artifact version to a collection with the Python SDK you can optionally provide a list of one or more aliases as an argument to the `alias` parameter in [`link_artifact()`]({{< relref "/ref/python/experiments/run.md/#link_artifact" >}}). W&B creates an alias ([non protected alias]({{< relref "#custom-aliases" >}})) for you if the alias you provide does not already exist.
-
-The following code snippet demonstrates how to link an artifact version to a collection and add aliases to that artifact version with the Python SDK. Replace values within `<>` with your own:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(entity = "", project = "")
-
-# Create an artifact object
-# The type parameter specifies both the type of the
-# artifact object and the collection type
-artifact = wandb.Artifact(name = "", type = "")
-
-# Add the file to the artifact object.
-# Specify the path to the file on your local machine.
-artifact.add_file(local_path = "")
-
-# Specify the collection and registry to link the artifact to
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-target_path=f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
-
-# Link the artifact version to the collection
-# Add one or more aliases to this artifact version
-run.link_artifact(
- artifact = artifact,
- target_path = target_path,
- aliases = ["", ""]
- )
-```
-{{% /tab %}}
-{{< /tabpane >}}
-
-### Protected aliases
-Use a [protected alias]({{< relref "aliases/#protected-aliases" >}}) to both label and identify artifact versions that should not be modified or deleted. For example, consider using a `production` protected alias to label and identify artifact versions that are in used in your organization's machine learning production pipeline.
-
-[Registry admin]({{< relref "/guides/core/registry/configure_registry/#registry-roles" >}}) users and [service accounts]({{< relref "/support/kb-articles/service_account_useful" >}}) with the **Admin** role can create protected aliases and add or remove protected aliases from an artifact version. Users and service accounts with **Member**, **Viewer**, and **Restricted Viewer** roles cannot unlink a protected version or delete a collection that contains a protected alias. See [Configure registry access]({{< relref "/guides/core/registry/configure_registry.md" >}}) for details.
-
-Common protected aliases include:
-
-- **Production**: The artifact version is ready for production use.
-- **Staging**: The artifact version is ready for testing.
-
-#### Create a protected alias
-
-The following steps describe how to create a protected alias in the W&B Registry UI:
-
-1. Navigate to the Registry App.
-2. Select a registry.
-3. Click the gear button on the top right of the page to view the registry's settings.
-4. Within the **Protected Aliases** section, click the **+** button to add one or more protected aliases.
-
-After creation, each protected alias appears as a gray rectangle with a lock icon in the **Protected Aliases** section.
-
-{{% alert %}}
-Unlike custom aliases that are not protected, creating protected aliases is available exclusively in the W&B Registry UI and not programmatically with the Python SDK. To add a protected alias to an artifact version, you can use the W&B Registry UI or the Python SDK.
-{{% /alert %}}
-
-The following steps describe how to add a protected alias to an artifact version with the W&B Registry UI:
-
-1. Navigate to the W&B Registry.
-2. Click the **View details** button in a collection.
-3. Within the **Versions** section, select the **View** button for a specific artifact version.
-4. Click the **+** button to add one or more protected aliases next to the **Aliases** field.
-
-After a protected alias is created, an admin can add it to an artifact version programmatically with the Python SDK. See the W&B Registry and Python SDK tabs in [Create a custom alias](#custom-aliases) section above for an example on how to add a protected alias to an artifact version.
-
-## Find existing aliases
-You can find existing aliases with the [global search bar in the W&B Registry]({{< relref "/guides/core/registry/search_registry/#search-for-registry-items" >}}). To find a protected alias:
-
-1. Navigate to the W&B Registry App.
-2. Specify the search term in the search bar at the top of the page. Press Enter to search.
-
-Search results appear below the search bar if the term you specify matches an existing registry, collection name, artifact version tag, collection tag, or alias.
-
-## Example
-
-{{% alert %}}
-The following code example is a continuation of [the W&B Registry Tutorial](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb_registry/zoo_wandb.ipynb). To use the following code, you must first [retrieve and process the Zoo dataset as described in the notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb_registry/zoo_wandb.ipynb#scrollTo=87fecd29-8146-41e2-86fb-0bb4e3e3350a). Once you have the Zoo dataset, you can create an artifact version and add custom aliases to it.
-{{% /alert %}}
-
-The following code snippet shows how to create an artifact version and add custom aliases to it. The example uses the Zoo dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/111/zoo) and the `Model` collection in the `Zoo_Classifier_Models` registry.
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(entity = "smle-reg-team-2", project = "zoo_experiment")
-
-# Create an artifact object
-# The type parameter specifies both the type of the
-# artifact object and the collection type
-artifact = wandb.Artifact(name = "zoo_dataset", type = "dataset")
-
-# Add the file to the artifact object.
-# Specify the path to the file on your local machine.
-artifact.add_file(local_path="zoo_dataset.pt", name="zoo_dataset")
-artifact.add_file(local_path="zoo_labels.pt", name="zoo_labels")
-
-# Specify the collection and registry to link the artifact to
-REGISTRY_NAME = "Model"
-COLLECTION_NAME = "Zoo_Classifier_Models"
-target_path=f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
-
-# Link the artifact version to the collection
-# Add one or more aliases to this artifact version
-run.link_artifact(
- artifact = artifact,
- target_path = target_path,
- aliases = ["production-us", "production-eu"]
- )
-```
-
-1. First, you create an artifact object (`wandb.Artifact()`).
-2. Next, you add two dataset PyTorch tensors to the artifact object with `wandb.Artifact.add_file()`.
-3. Lastly, you link the artifact version to the `Model` collection in the `Zoo_Classifier_Models` registry with `link_artifact()`. You also add two custom aliases to the artifact version by passing `production-us` and `production-eu` as arguments to the `aliases` parameter.
diff --git a/content/en/guides/core/registry/configure_registry.md b/content/en/guides/core/registry/configure_registry.md
deleted file mode 100644
index 1f551bae5a..0000000000
--- a/content/en/guides/core/registry/configure_registry.md
+++ /dev/null
@@ -1,170 +0,0 @@
----
-menu:
- default:
- identifier: configure_registry
- parent: registry
-title: Configure registry access
-weight: 3
----
-
-A registry admin can [configure registry roles]({{< relref "configure_registry.md#configure-registry-roles" >}}), [add users]({{< relref "configure_registry.md#add-a-user-or-a-team-to-a-registry" >}}), or [remove users]({{< relref "configure_registry.md#remove-a-user-or-team-from-a-registry" >}}) from a registry by configuring the registry's settings.
-
-## Manage users
-
-### Add a user or a team
-
-Registry admins can add individual users or entire teams to a registry. To add a user or team to a registry:
-
-1. Navigate to the **Registry** App in the W&B App UI.
-2. Select the registry you want to add a user or team to.
-3. Click on the gear icon on the upper right hand corner to access the registry settings.
-4. In the **Registry access** section, click **Add access**.
-5. Specify one or more user names, emails, or the team names to the **Include users and teams** field.
-6. Click **Add access**.
-
-{{< img src="/images/registry/add_team_registry.gif" alt="Adding teams to registry" >}}
-
-Learn more about [configuring user roles in a registry]({{< relref "configure_registry.md#configure-registry-roles" >}}), or [Registry role permissions]({{< relref "configure_registry.md#registry-role-permissions" >}}) .
-
-### Remove a user or team
-A registry admin can remove individual users or entire teams from a registry. To remove a user or team from a registry:
-
-1. Navigate to the **Registry** App in the W&B App UI.
-2. Select the registry you want to remove a user from.
-3. Click on the gear icon on the upper right hand corner to access the registry settings.
-4. Navigate to the **Registry access** section and type in the username, email, or team you want to remove.
-5. Click the **Delete** button.
-
-{{% alert %}}
-Removing a user from a team also removes that user's access to the registry.
-{{% /alert %}}
-
-### Change the owner of a registry
-
-A registry admin can designate any member as a registry's owner, including a **Restricted Viewer** or a **Viewer**. Registry ownership is primarily for accountability purposes and does not confer any additional permissions beyond those granted by the user's assigned role.
-
-To change the owner:
-1. Navigate to the **Registry** App in the W&B App UI.
-2. Select the registry you want to configure.
-3. Click the gear icon on the upper right hand corner.
-4. Scroll to the **Registry members and roles** section.
-5. Hover over the row for a member.
-6. Click the **...** action menu at the end of the row, then click **Make owner**.
-
-
-## Configure Registry roles
-
-This section shows how to configure roles for Registry members. For more information about Registry roles, including the cabilities of each role, order of precedence, defaults, and more, see [Details about Registry roles](#details-about-registry-roles).
-
-1. Navigate to the **Registry** App in the W&B App UI.
-2. Select the registry you want to configure.
-3. Click the gear icon on the upper right hand corner.
-4. Scroll to the **Registry members and roles** section.
-5. Within the **Member** field, search for the user or team you want to edit permissions for.
-6. In the **Registry role** column, click the user's role.
-7. From the dropdown, select the role you want to assign to the user.
-
-## Details about Registry roles
-
-The following sections give more information about Registry roles.
-
-{{% alert %}}
-Your [role in a team]({{< ref "/guides/models/app/settings-page/teams.md#team-roles-and-permissions" >}}) has no impact or relationship to your role in any registry.
-{{% /alert %}}
-
-### Default roles
-W&B automatically assigns a default **registry role** to a user or team when they are added to a registry. This role determines what they can do in that registry.
-
-| Entity | Default registry role (Dedicated Cloud / Self-Managed) | Default registry role (Multi-tenant Cloud) |
-|----------------------------------------|-------------------------------------------------------------|-------------------------------------------------|
-| Team | Viewer | Restricted Viewer |
-| User or service account (non admin) | Viewer | Restricted Viewer |
-| Service account (non admin) | Member1 | Member1 |
-| Org admin | Admin | Admin |
-
-1: Service accounts cannot have **Viewer** or **Restricted Viewer** roles.
-
-A registry admin can assign or modify roles for users and teams in the registry.
-See [Configure user roles in a registry]({{< relref "configure_registry.md#configure-registry-roles" >}}) for more information.
-
-{{% alert title="Restricted Viewer role availability" %}}
-The **Restricted Viewer** role is currently available only in Multi-Tenant Cloud organizations by invitation only. To request access, or to express interest in the feature on Dedicated Cloud or Self-Managed, [contact support](mailto:support@wandb.ai).
-
-This role provides read-only access to registry artifacts without the ability to create, update, or delete collections, automations, or other registry resources.
-
-Unlike a **Viewer**, a **Restricted Viewer**:
-- Cannot download artifact files or access file contents.
-- Cannot use artifacts with use_artifact() in the W&B SDK.
-{{% /alert %}}
-
-### Role permissions
-The following table lists each Registry role, along with the permissions provided by each role:
-
-| Permission | Permission Group | Restricted Viewer (Multi-tenant Cloud, by invitation) | Viewer | Member | Admin |
-|--------------------------------------------------------------- |------------------|-------------------|--------|--------|-------|
-| View a collection's details | Read | ✓ | ✓ | ✓ | ✓ |
-| View a linked artifact's details | Read | ✓ | ✓ | ✓ | ✓ |
-| Usage: Consume an artifact in a registry with use_artifact | Read | | ✓ | ✓ | ✓ |
-| Download a linked artifact | Read | | ✓ | ✓ | ✓ |
-| Download files from an artifact's file viewer | Read | | ✓ | ✓ | ✓ |
-| Search a registry | Read | ✓ | ✓ | ✓ | ✓ |
-| View a registry's settings and user list | Read | ✓ | ✓ | ✓ | ✓ |
-| Create a new automation for a collection | Create | | | ✓ | ✓ |
-| Turn on Slack notifications for new version being added | Create | | | ✓ | ✓ |
-| Create a new collection | Create | | | ✓ | ✓ |
-| Create a new custom registry | Create | | | ✓ | ✓ |
-| Edit collection card (description) | Update | | | ✓ | ✓ |
-| Edit linked artifact description | Update | | | ✓ | ✓ |
-| Add or delete a collection's tag | Update | | | ✓ | ✓ |
-| Add or delete an alias from a linked artifact | Update | | | ✓ | ✓ |
-| Link a new artifact | Update | | | ✓ | ✓ |
-| Edit allowed types list for a registry | Update | | | ✓ | ✓ |
-| Edit custom registry name | Update | | | ✓ | ✓ |
-| Delete a collection | Delete | | | ✓ | ✓ |
-| Delete an automation | Delete | | | ✓ | ✓ |
-| Unlink an artifact from a registry | Delete | | | ✓ | ✓ |
-| Edit accepted artifact types for a registry | Admin | | | | ✓ |
-| Change registry visibility (Organization or Restricted) | Admin | | | | ✓ |
-| Add users to a registry | Admin | | | | ✓ |
-| Assign or change a user's role in a registry | Admin | | | | ✓ |
-
-
-### Inherited Registry role
-The registry's membership list shows each user's inherited (effective) registry role (in light gray) next to the role dropdown in their row.
-
-{{< img src="/images/registry/role_conflict.png" alt="Registry membership list showing the user's effective registry role" >}}
-
-A user's effective role in a particular registry matches their _highest_ role among their role in the organization, the registry, and the team that owns the registry, whether inherited or explicitly assigned. For example:
-
-- A team **Admin** or organization **Admin** with the **Viewer** role in a particular registry owned by the team is effectively an **Admin** of the registry.
-- A registry **Viewer** with the **Member** role in the team is effectively a **Member** of the registry.
-- A team **Viewer** with the **Member** role in a particular registry is effectively a **Member** of the registry.
-
-### SDK compatibility
-
-{{% alert title="SDK version requirement" %}}
-To use the W&B SDK to access artifacts as a **Restricted Viewer**, you must use W&B SDK version 0.19.9 or higher. Otherwise, some SDK commands will result in permission errors.
-{{% /alert %}}
-
-When a **Restricted Viewer** uses the SDK, certain functions are not available or work differently.
-
-The following methods are not available and result in permission errors:
-- [`Run.use_artifact()`]({{< relref "/ref/python/experiments/run/#method-runuse_artifact" >}})
-- [`Artifact.download()`]({{< relref "/ref/python/experiments/artifact/#method-artifactdownload" >}})
-- [`Artifact.file()`]({{< relref "/ref/python/experiments/artifact/#method-artifactfile" >}})
-- [`Artifact.files()`]({{< relref "/ref/python/experiments/artifact/#method-artifactfiles" >}})
-
-The following methods are limited to artifact metadata:
-- [`Artifact.get_entry()`]({{< relref "/ref/python/experiments/artifact/#method-artifactget_entry" >}})
-- [`Artifact.get_path()`]({{< relref "/ref/python/experiments/artifact/#method-artifactget_path" >}})
-- [`Artifact.get()`]({{< relref "/ref/python/experiments/artifact/#method-artifactget" >}})
-- [`Artifact.verify()`]({{< relref "/ref/python/experiments/artifact/#method-artifactverify" >}})
-
-### Cross-registry permissions
-
-A user can have different roles in different registries. For example, a user can be a **Restricted Viewer** in Registry A but a **Viewer** in Registry B. In this case:
-
-- The same artifact linked to both registries will have different access levels
-- In Registry A, the user is a **Restricted Viewer** and cannot download files or use the artifact
-- In Registry B, the user is a **Viewer** and can download files and use the artifact
-- In other words, access is determined by the registry in which the artifact is accessed
diff --git a/content/en/guides/core/registry/create_collection.md b/content/en/guides/core/registry/create_collection.md
deleted file mode 100644
index 304265437b..0000000000
--- a/content/en/guides/core/registry/create_collection.md
+++ /dev/null
@@ -1,166 +0,0 @@
----
-menu:
- default:
- identifier: create_collection
- parent: registry
-title: Create a collection
-weight: 4
----
-
-
-A *collection* is a set of linked artifact versions within a registry. Each collection represents a distinct task or use case.
-
-For example, within the core Dataset registry you might have multiple collections. Each collection contains a different dataset such as MNIST, CIFAR-10, or ImageNet.
-
-As another example, you might have a registry called "chatbot" that contains a collection for model artifacts, another collection for dataset artifacts, and another collection for fine-tuned model artifacts.
-
-How you organize a registry and their collections is up to you.
-
-{{% alert %}}
-If you are familiar with W&B Model Registry, you might aware of registered models. Registered models in the Model Registry are now referred to as collections in the W&B Registry.
-{{% /alert %}}
-
-## Collection types
-
-Each collection accepts one, and only one, *type* of artifact. The type you specify restricts what sort of artifacts you, and other members of your organization, can link to that collection.
-
-{{% alert %}}
-You can think of artifact types similar to data types in programming languages such as Python. In this analogy, a collection can store strings, integers, or floats but not a mix of these data types.
-{{% /alert %}}
-
-For example, suppose you create a collection that accepts "dataset" artifact types. This means that you can only link future artifact versions that have the type "dataset" to this collection. Similarly, you can only link artifacts of type "model" to a collection that accepts only model artifact types.
-
-{{% alert %}}
-You specify an artifact's type when you create that artifact object. Note the `type` field in `wandb.Artifact()`:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(
- entity = "",
- project = ""
- )
-
-# Create an artifact object
-artifact = wandb.Artifact(
- name="",
- type=""
- )
-```
-{{% /alert %}}
-
-
-When you create a collection, you can select from a list of predefined artifact types. The artifact types available to you depend on the registry that the collection belongs to. .
-
-Before you link an artifact to a collection or create a new collection, [investigate the types of artifacts that collection accepts]({{< relref "#check-the-types-of-artifact-that-a-collection-accepts" >}}).
-
-### Check the types of artifact that a collection accepts
-
-Before you link to a collection, inspect the artifact type that the collection accepts. You can inspect the artifact types that collection accepts programmatically with the W&B Python SDK or interactively with the W&B App
-
-{{% alert %}}
-An error message appears if you try to create link an artifact to a collection that does not accept that artifact type.
-{{% /alert %}}
-
-{{< tabpane text=true >}}
- {{% tab header="W&B App" %}}
-You can find the accepted artifact types on the registry card on the homepage or within a registry's settings page.
-
-For both methods, first navigate to your W&B Registry App.
-
-Within the homepage of the Registry App, you can view the accepted artifact types by scrolling to the registry card of that registry. The gray horizontal ovals within the registry card lists the artifact types that registry accepts.
-
-{{< img src="/images/registry/artifact_types_model_card.png" alt="Artifact types selection" >}}
-
-For example, the preceding image shows multiple registry cards on the Registry App homepage. Within the **Model** registry card, you can see two artifact types: **model** and **model-new**.
-
-
-To view accepted artifact types within a registry's settings page:
-
-1. Click on the registry card you want to view the settings for.
-2. Click on the gear icon in the upper right corner.
-3. Scroll to the **Accepted artifact types** field.
- {{% /tab %}}
- {{% tab header="Python SDK (Beta)" %}}
-Programmatically view the artifact types that a registry accepts with the W&B Python SDK:
-
-```python
-import wandb
-
-registry_name = ""
-artifact_types = wandb.Api().project(name=f"wandb-registry-{registry_name}").artifact_types()
-print(artifact_type.name for artifact_type in artifact_types)
-```
-
-{{% alert %}}
-Note that you do not initialize a run with the proceeding code snippet. This is because it is unnecessary to create a run if you are only querying the W&B API and not tracking an experiment, artifact and so on.
-{{% /alert %}}
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-Once you know what type of artifact a collection accepts, you can [create a collection]({{< relref "#create-a-collection" >}}).
-
-
-## Create a collection
-
-Interactively or programmatically create a collection within a registry. You can not change the type of artifact that a collection accepts after you create it.
-
-### Programmatically create a collection
-
-Use the `wandb.init.link_artifact()` method to link an artifact to a collection. Specify both the collection and the registry to the `target_path` field as a path that takes the form of:
-
-```python
-f"wandb-registry-{registry_name}/{collection_name}"
-```
-
-Where `registry_name` is the name of the registry and `collection_name` is the name of the collection. Ensure to append the prefix `wandb-registry-` to the registry name.
-
-{{% alert %}}
-W&B automatically creates a collection for you if you try to link an artifact to a collection that does not exist. If you specify a collection that does exists, W&B links the artifact to the existing collection.
-{{% /alert %}}
-
-The proceeding code snippet shows how to programmatically create a collection. Ensure to replace other the values enclosed in `<>` with your own:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(entity = "", project = "")
-
-# Create an artifact object
-artifact = wandb.Artifact(
- name = "",
- type = ""
- )
-
-registry_name = ""
-collection_name = ""
-target_path = f"wandb-registry-{registry_name}/{collection_name}"
-
-# Link the artifact to a collection
-run.link_artifact(artifact = artifact, target_path = target_path)
-
-run.finish()
-```
-
-### Interactively create a collection
-
-The following steps describe how to create a collection within a registry using the W&B Registry App UI:
-
-1. Navigate to the **Registry** App in the W&B App UI.
-2. Select a registry.
-3. Click on the **Create collection** button in the upper right hand corner.
-4. Provide a name for your collection in the **Name** field.
-5. Select a type from the **Type** dropdown. Or, if the registry enables custom artifact types, provide one or more artifact types that this collection accepts.
-6. Optionally provide a description of your collection in the **Description** field.
-7. Optionally add one or more tags in the **Tags** field.
-8. Click **Link version**.
-9. From the **Project** dropdown, select the project where your artifact is stored.
-10. From the **Artifact** collection dropdown, select your artifact.
-11. From the **Version** dropdown, select the artifact version you want to link to your collection.
-12. Click on the **Create collection** button.
-
-{{< img src="/images/registry/create_collection.gif" alt="Create a new collection" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/create_registry.md b/content/en/guides/core/registry/create_registry.md
deleted file mode 100644
index 981c1ea089..0000000000
--- a/content/en/guides/core/registry/create_registry.md
+++ /dev/null
@@ -1,96 +0,0 @@
----
-menu:
- default:
- identifier: create_registry
- parent: registry
-title: Create a custom registry
-weight: 2
----
-
-A custom registry offers flexibility and control over the artifact types that you can use, allows you to restrict the registry's visibility, and more.
-
-{{% pageinfo color="info" %}}
-See the summary table in [Registry types]({{< relref "registry_types.md#summary" >}}) for a complete comparison of core and custom registries.
-{{% /pageinfo %}}
-
-
-## Create a custom registry
-
-Create a custom registry either programmatically using the Registry App or the W&B Python SDK.
-
-{{< tabpane text=true >}}
- {{% tab header="Registry App" %}}
-
-1. Navigate to the **Registry** App at https://wandb.ai/registry/.
-2. Within **Custom registry**, click on the **Create registry** button.
-3. Provide a name for your registry in the **Name** field.
-4. Optionally provide a description about the registry.
-5. Select who can view the registry from the **Registry visibility** dropdown. See [Registry visibility types]({{< relref "./configure_registry.md#registry-visibility-types" >}}) for more information on registry visibility options.
-6. Select either **All types** or **Specify types** from the **Accepted artifacts type** dropdown.
-7. (If you select **Specify types**) Add one or more artifact types that your registry accepts.
-8. Click on the **Create registry** button.
-
- {{% /tab %}}
- {{% tab header="Python SDK" %}}
-
-Use the [`wandb.Api().create_registry()`]({{< relref "/ref/python/public-api/Api/#method-apicreate_registry" >}}) method to create a custom registry programmatically. Provide a name and [visibility](#visibility-types) for the registry for the `name` and `visibility` parameters, respectively.
-
-Copy and paste the code block below. Replace the values enclosed in `<>` with your own:
-
-```python
-import wandb
-
-registry = wandb.Api().create_registry(
- name="",
- visibility="< 'restricted' | 'organization' >",
-)
-```
-
-See the [`wandb.Api().create_registry()`]({{< relref "/ref/python/public-api/Api/#method-apicreate_registry" >}}) method reference for a full list of parameters that you can provide when you create a custom registry.
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-{{% alert %}}
-An artifact type cannot be removed from a registry once it is saved in the registry's settings.
-{{% /alert %}}
-
-For example, the proceeding image shows a custom registry called `Fine_Tuned_Models` that a user is about to create. The registry is **Restricted** to only members that are manually added to the registry.
-
-{{< img src="/images/registry/create_registry.gif" alt="Creating a new registry" >}}
-
-## Visibility types
-
-The *visibility* of a registry determines who can access that registry. Restricting the visibility of a custom registry helps ensure that only specified members can access that registry.
-
-There are two type registry visibility options for a custom registry:
-
-| Visibility | Description |
-| --- | --- |
-| Restricted | Only invited organization members can access the registry.|
-| Organization | Everyone in the org can access the registry. |
-
-A team administrator or registry administrator can set the visibility of a custom registry.
-
-The user who creates a custom registry with Restricted visibility is added to the registry automatically as its registry admin.
-
-
-## Configure the visibility of a custom registry
-
-A team administrator or registry administrator can assign the visibility of a custom registry during or after the creation of a custom registry.
-
-To restrict the visibility of an existing custom registry:
-
-1. Navigate to the **Registry** App at https://wandb.ai/registry/.
-2. Select a registry.
-3. Click on the gear icon on the upper right hand corner.
-4. From the **Registry visibility** dropdown, select the desired registry visibility.
-5. if you select **Restricted visibility**:
- 1. Add members of your organization that you want to have access to this registry. Scroll to the **Registry members and roles** section and click on the **Add member** button.
- 2. Within the **Member** field, add the email or username of the member you want to add.
- 3. Click **Add new member**.
-
-{{< img src="/images/registry/change_registry_visibility.gif" alt="Changing registry visibility settings from private to public or team-restricted access" >}}
-
-See [Create a custom registry]({{< relref "./create_registry.md#create-a-custom-registry" >}}) for more information on how assign the visibility of a custom registry when a team administrator creates it.
-
diff --git a/content/en/guides/core/registry/delete_registry.md b/content/en/guides/core/registry/delete_registry.md
deleted file mode 100644
index 2b69b45b1d..0000000000
--- a/content/en/guides/core/registry/delete_registry.md
+++ /dev/null
@@ -1,50 +0,0 @@
----
-menu:
- default:
- identifier: delete_registry
- parent: registry
-title: Delete registry
-weight: 8
----
-
-This page shows how a Team admin or Registry admin can delete a custom registry. A [core registry]({{< relref "/guides/core/registry/registry_types#core-registry" >}}) cannot be deleted.
-
-- A Team admin can delete any custom registry in the organization.
-- A Registry admin can delete a custom registry that they created.
-
-Deleting a registry also deletes collections that belong to that registry, but does not delete artifacts linked to the registry. Such an artifact remains in the original project that the artifact was logged to.
-
-
-{{< tabpane text=true >}}
-{{% tab header="Python SDK" value="python" %}}
-
-Use the `wandb` API's `delete()` method to delete a registry programmatically. The following example illustrates how to:
-
-1. Fetch the registry you want to delete with `api.registry()`.
-1. Call the `delete()` method on the returned registry object to delete the registry.
-
-```python
-import wandb
-
-# Initialize the W&B API
-api = wandb.Api()
-
-# Fetch the registry you want to delete
-fetched_registry = api.registry("")
-
-# Deleting a registry
-fetched_registry.delete()
-```
-
-{{% /tab %}}
-
-{{% tab header="W&B App" value="app" %}}
-
-1. Navigate to the **Registry** App at https://wandb.ai/registry/.
-2. Select the custom registry you want to delete.
-3. Click the gear icon in the upper right corner to view the registry's settings.
-4. To delete the registry, click the trash can icon in the upper right corner of the settings page.
-5. Confirm the registry to delete by entering its name in the modal that appears, then click **Delete**.
-
-{{% /tab %}}
-{{< /tabpane >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/download_use_artifact.md b/content/en/guides/core/registry/download_use_artifact.md
deleted file mode 100644
index c3b7688550..0000000000
--- a/content/en/guides/core/registry/download_use_artifact.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-menu:
- default:
- identifier: download_use_artifact
- parent: registry
-title: Download an artifact from a registry
-weight: 6
----
-
-Use the W&B Python SDK to download an artifact linked to a registry. To download and use an artifact, you need to know the name of the registry, the name of the collection, and the alias or index of the artifact version you want to download.
-
-Once you know the properties of the artifact, you can [construct the path to the linked artifact]({{< relref "#construct-path-to-linked-artifact" >}}) and download the artifact. Alternatively, you can [copy and paste a pre-generated code snippet]({{< relref "#copy-and-paste-pre-generated-code-snippet" >}}) from the W&B App UI to download an artifact linked to a registry.
-
-
-## Construct path to linked artifact
-
-To download an artifact linked to a registry, you must know the path of that linked artifact. The path consists of the registry name, collection name, and the alias or index of the artifact version you want to access.
-
-Once you have the registry, collection, and alias or index of the artifact version, you can construct the path to the linked artifact using the proceeding string template:
-
-```python
-# Artifact name with version index specified
-f"wandb-registry-{REGISTRY}/{COLLECTION}:v{INDEX}"
-
-# Artifact name with alias specified
-f"wandb-registry-{REGISTRY}/{COLLECTION}:{ALIAS}"
-```
-
-Replace the values within the curly braces `{}` with the name of the registry, collection, and the alias or index of the artifact version you want to access.
-
-{{% alert %}}
-Specify `model` or `dataset` to link an artifact version to the core Model registry or the core Dataset registry, respectively.
-{{% /alert %}}
-
-Use the `wandb.init.use_artifact` method to access the artifact and download its contents once you have the path of the linked artifact. The proceeding code snippet shows how to use and download an artifact linked to the W&B Registry. Ensure to replace values within `<>` with your own:
-
-```python
-import wandb
-
-REGISTRY = ''
-COLLECTION = ''
-ALIAS = ''
-
-run = wandb.init(
- entity = '',
- project = ''
- )
-
-artifact_name = f"wandb-registry-{REGISTRY}/{COLLECTION}:{ALIAS}"
-# artifact_name = '' # Copy and paste Full name specified on the Registry App
-fetched_artifact = run.use_artifact(artifact_or_name = artifact_name)
-download_path = fetched_artifact.download()
-```
-
-The `.use_artifact()` method both creates a [run]({{< relref "/guides/models/track/runs/" >}}) and marks the artifact you download as the input to that run.
-Marking an artifact as the input to a run enables W&B to track the lineage of that artifact.
-
-If you do not want to create a run, you can use the `wandb.Api()` object to access the artifact:
-
-```python
-import wandb
-
-REGISTRY = ""
-COLLECTION = ""
-VERSION = ""
-
-api = wandb.Api()
-artifact_name = f"wandb-registry-{REGISTRY}/{COLLECTION}:{VERSION}"
-artifact = api.artifact(name = artifact_name)
-```
-
-
-Example: Use and download an artifact linked to the W&B Registry
-
-The proceeding code example shows how a user can download an artifact linked to a collection called `phi3-finetuned` in the **Fine-tuned Models** registry. The alias of the artifact version is set to `production`.
-
-```python
-import wandb
-
-TEAM_ENTITY = "product-team-applications"
-PROJECT_NAME = "user-stories"
-
-REGISTRY = "Fine-tuned Models"
-COLLECTION = "phi3-finetuned"
-ALIAS = 'production'
-
-# Initialize a run inside the specified team and project
-run = wandb.init(entity=TEAM_ENTITY, project = PROJECT_NAME)
-
-artifact_name = f"wandb-registry-{REGISTRY}/{COLLECTION}:{ALIAS}"
-
-# Access an artifact and mark it as input to your run for lineage tracking
-fetched_artifact = run.use_artifact(artifact_or_name = name)
-
-# Download artifact. Returns path to downloaded contents
-downloaded_path = fetched_artifact.download()
-```
-
-
-
-
-See [`use_artifact`]({{< relref "/ref/python/experiments/run.md#use_artifact" >}}) and [`Artifact.download()`]({{< relref "/ref/python/experiments/artifact.md#download" >}}) in the API Reference for parameters and return type.
-
-{{% alert title="Users with a personal entity that belong to multiple organizations" %}}
-Users with a personal entity that belong to multiple organizations must also specify either the name of their organization or use a team entity when accessing artifacts linked to a registry.
-
-```python
-import wandb
-
-REGISTRY = ""
-COLLECTION = ""
-VERSION = ""
-
-# Ensure you are using your team entity to instantiate the API
-api = wandb.Api(overrides={"entity": ""})
-artifact_name = f"wandb-registry-{REGISTRY}/{COLLECTION}:{VERSION}"
-artifact = api.artifact(name = artifact_name)
-
-# Use org display name or org entity in the path
-api = wandb.Api()
-artifact_name = f"{ORG_NAME}/wandb-registry-{REGISTRY}/{COLLECTION}:{VERSION}"
-artifact = api.artifact(name = artifact_name)
-```
-
-Where the `ORG_NAME` is the display name of your organization. Multi-tenant SaaS users can find the name of their organization in the organization's settings page at `https://wandb.ai/account-settings/`. Dedicated Cloud and Self-Managed users, contact your account administrator to confirm your organization's display name.
-{{% /alert %}}
-
-## Copy and paste pre-generated code snippet
-
-W&B creates a code snippet that you can copy and paste into your Python script, notebook, or terminal to download an artifact linked to a registry.
-
-1. Navigate to the Registry App.
-2. Select the name of the registry that contains your artifact.
-3. Select the name of the collection.
-4. From the list of artifact versions, select the version you want to access.
-5. Select the **Usage** tab.
-6. Copy the code snippet shown in the **Usage API** section.
-7. Paste the code snippet into your Python script, notebook, or terminal.
-
-{{< img src="/images/registry/find_usage_in_registry_ui.gif" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/lineage.md b/content/en/guides/core/registry/lineage.md
deleted file mode 100644
index 202d7f3dd2..0000000000
--- a/content/en/guides/core/registry/lineage.md
+++ /dev/null
@@ -1,87 +0,0 @@
----
-description: Create a lineage map in the W&B Registry.
-menu:
- default:
- parent: registry
- identifier: lineage
-title: Create and view lineage maps
-weight: 8
----
-
-Within a collection in the W&B Registry, you can view a history of the artifacts that an ML experiment uses. This history is called a _lineage graph_.
-
-{{% pageinfo color="info" %}}
-You can also view lineage graphs for artifacts you log to W&B that are not part of a collection.
-{{% /pageinfo %}}
-
-Lineage graphs can show the specific run that logs an artifact. In addition, lineage graphs can also show which run used an artifact as an input. In other words, lineage graphs can show the input and output of a run.
-
-
-For example, the proceeding image shows artifacts created and used throughout an ML experiment:
-
-{{< img src="/images/registry/registry_lineage.png" alt="Registry lineage" >}}
-
-From left to right, the image shows:
-1. Multiple runs log the `split_zoo_dataset:v4` artifact.
-2. The "rural-feather-20" run uses the `split_zoo_dataset:v4` artifact for training.
-3. The output of the "rural-feather-20" run is a model artifact called `zoo-ylbchv20:v0`.
-4. A run called "northern-lake-21" uses the model artifact `zoo-ylbchv20:v0` to evaluate the model.
-
-
-## Track the input of a run
-
-Mark an artifact as an input or dependency of a run with the `wandb.init.use_artifact` API.
-
-The proceeding code snippet shows how to use the `use_artifact`. Replace values enclosed in angle brackets (`< >`) with your values:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(project="", entity="")
-
-# Get artifact, mark it as a dependency
-artifact = run.use_artifact(artifact_or_name="", aliases="")
-```
-
-
-## Track the output of a run
-
-Use ([`wandb.init.log_artifact`]({{< relref "/ref/python/experiments/run.md#log_artifact" >}})) to declare an artifact as an output of a run.
-
-The proceeding code snippet shows how to use the `wandb.init.log_artifact` API. Ensure to replace values enclosed in angle brackets (`< >`) with your values:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(entity "", project = "",)
-artifact = wandb.Artifact(name = "", type = "")
-artifact.add_file(local_path = "", name="")
-
-# Log the artifact as an output of the run
-run.log_artifact(artifact_or_path = artifact)
-```
-
-For more information on about creating artifacts, see [Create an artifact]({{< relref "guides/core/artifacts/construct-an-artifact.md" >}}).
-
-
-## View lineage graphs in a collection
-
-View the lineage of an artifact linked to a collection in the W&B Registry.
-
-1. Navigate to the W&B Registry.
-2. Select the collection that contains the artifact.
-3. From the dropdown, click the artifact version you want to view its lineage graph.
-4. Select the "Lineage" tab.
-
-
-Once you are in an artifact's lineage graph page, you can view additional information about any node in that lineage graph.
-
-Select a run node to view that run's details, such as the run's ID, the run's name, the run's state, and more. As an example, the proceeding image shows information about the `rural-feather-20` run:
-
-{{< img src="/images/registry/lineage_expanded_node.png" alt="Expanded lineage node" >}}
-
-Select an artifact node to view that artifact's details, such as its full name, type, creation time, and associated aliases.
-
-{{< img src="/images/registry/lineage_expanded_artifact_node.png" alt="Expanded artifact node details" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/link_version.md b/content/en/guides/core/registry/link_version.md
deleted file mode 100644
index 33c7c409db..0000000000
--- a/content/en/guides/core/registry/link_version.md
+++ /dev/null
@@ -1,226 +0,0 @@
----
-menu:
- default:
- identifier: link_version
- parent: registry
-title: Link an artifact version to a registry
-weight: 5
----
-
-Link artifact versions to a collection to make them available to other members in your organization.
-
-When you link an artifact to a registry, this "publishes" that artifact to that registry. Any user that has access to that registry can access the linked artifact versions in the collection.
-
-In other words, linking an artifact to a registry collection brings that artifact version from a private, project-level scope, to a shared organization level scope.
-
-{{% alert %}}
-The term "type" refers to the artifact object's type. When you create an artifact object ([`wandb.Artifact`]({{< relref "/ref/python/experiments/artifact.md" >}})), or log an artifact ([`wandb.init.log_artifact`]({{< relref "/ref/python/experiments/run.md#log_artifact" >}})), you specify a type for the `type` parameter.
-
-{{% /alert %}}
-
-## Link an artifact to a collection
-
-Link an artifact version to a collection interactively or programmatically.
-
-{{% alert %}}
-Before you link an artifact to a registry, check the types of artifacts that collection permits. For more information about collection types, see "Collection types" within [Create a collection]({{< relref "./create_collection.md" >}}).
-{{% /alert %}}
-
-Based on your use case, follow the instructions described in the tabs below to link an artifact version.
-
-{{% alert %}}
-If an artifact version logs metrics (such as by using `run.log_artifact()`), you can view metrics for that version from its details page, and you can compare metrics across artifact versions from the artifact's page. Refer to [View linked artifacts in a registry]({{< relref "#view-linked-artifacts-in-a-registry" >}}).
-{{% /alert %}}
-
-{{< tabpane text=true >}}
- {{% tab header="Python SDK" %}}
-{{% alert %}}
-Watch a [video demonstrating linking a version](https://www.youtube.com/watch?v=2i_n1ExgO0A) (8 min).
-{{% /alert %}}
-
-Programmatically link an artifact version to a collection with [`wandb.init.Run.link_artifact()`]({{< relref "/ref/python/experiments/run.md#link_artifact" >}}).
-
-{{% alert %}}
-Before you link an artifact to a collection, ensure that the registry that the collection belongs to already exists. To check that the registry exists, navigate to the Registry app on the W&B App UI and search for the name of the registry.
-{{% /alert %}}
-
-Use the `target_path` parameter to specify the collection and registry you want to link the artifact version to. The target path consists of the prefix "wandb-registry", the name of the registry, and the name of the collection separated by a forward slashes:
-
-```text
-wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}
-```
-
-Copy and paste the code snippet below to link an artifact version to a collection within an existing registry. Replace values enclosed in `<>` with your own:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(
- entity = "",
- project = ""
-)
-
-# Create an artifact object
-# The type parameter specifies both the type of the
-# artifact object and the collection type
-artifact = wandb.Artifact(name = "", type = "")
-
-# Add the file to the artifact object.
-# Specify the path to the file on your local machine.
-artifact.add_file(local_path = "")
-
-# Specify the collection and registry to link the artifact to
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-target_path=f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
-
-# Link the artifact to the collection
-run.link_artifact(artifact = artifact, target_path = target_path)
-```
-{{% alert %}}
-If you want to link an artifact version to the Model registry or the Dataset registry, set the artifact type to `"model"` or `"dataset"`, respectively.
-{{% /alert %}}
-
- {{% /tab %}}
- {{% tab header="Registry App" %}}
-1. Navigate to the Registry App.
- {{< img src="/images/registry/navigate_to_registry_app.png" alt="Registry App navigation" >}}
-2. Hover your mouse next to the name of the collection you want to link an artifact version to.
-3. Select the meatball menu icon (three horizontal dots) next to **View details**.
-4. From the dropdown, select **Link new version**.
-5. From the sidebar that appears, select the name of a team from the **Team** dropdown.
-5. From the **Project** dropdown, select the name of the project that contains your artifact.
-6. From the **Artifact** dropdown, select the name of the artifact.
-7. From the **Version** dropdown, select the artifact version you want to link to the collection.
-
-
- {{% /tab %}}
- {{% tab header="Artifact browser" %}}
-1. Navigate to your project's artifact browser on the W&B App at: `https://wandb.ai///artifacts`
-2. Select the Artifacts icon on the left sidebar.
-3. Click on the artifact version you want to link to your registry.
-4. Within the **Version overview** section, click the **Link to registry** button.
-5. From the modal that appears on the right of the screen, select an artifact from the **Select a register model** menu dropdown.
-6. Click **Next step**.
-7. (Optional) Select an alias from the **Aliases** dropdown.
-8. Click **Link to registry**.
-
-
-
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-
-
-View a linked artifact's metadata, version data, usage, lineage information and more in the Registry App.
-
-## View linked artifacts in a registry
-
-View information about linked artifacts such as metadata, lineage, and usage information in the Registry App.
-
-1. Navigate to the Registry App.
-2. Select the name of the registry that you linked the artifact to.
-3. Select the name of the collection.
-4. If the collection's artifacts log metrics, compare metrics across versions by clicking **Show metrics**.
-4. From the list of artifact versions, select the version you want to access. Version numbers are incrementally assigned to each linked artifact version starting with `v0`.
-5. To view details about an artifact version, click the version. From the tabs in this page, you can view that version's metadata (including logged metrics), lineage, and usage information.
-
-Make note of the **Full Name** field within the **Version** tab. The full name of a linked artifact consists of the registry, collection name, and the alias or index of the artifact version.
-
-```text title="Full name of a linked artifact"
-wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{INTEGER}
-```
-
-You need the full name of a linked artifact to access the artifact version programmatically.
-
-## Troubleshooting
-
-Below are some common things to double check if you are not able to link an artifact.
-
-### Logging artifacts from a personal account
-
-Artifacts logged to W&B with a personal entity can not be linked to the registry. Make sure that you log artifacts using a team entity within your organization. Only artifacts logged within an organization's team can be linked to the organization's registry.
-
-
-{{% alert title="" %}}
-Ensure that you log an artifact with a team entity if you want to link that artifact to a registry.
-{{% /alert %}}
-
-
-#### Find your team entity
-
-W&B uses the name of your team as the team's entity. For example, if your team is called **team-awesome**, your team entity is `team-awesome`.
-
-You can confirm the name of your team by:
-
-1. Navigate to your team's W&B profile page.
-2. Copy the site's URL. It has the form of `https://wandb.ai/`. Where `` is the both the name of your team and the team's entity.
-
-#### Log from a team entity
-1. Specify the team as the entity when you initialize a run with [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}). If you do not specify the `entity` when you initialize a run, the run uses your default entity which may or may not be your team entity.
-
- ```python
- import wandb
-
- run = wandb.init(
- entity='',
- project=''
- )
- ```
-
-2. Log the artifact to the run either with run.log_artifact or by creating an Artifact object and then adding files to it with:
-
- ```python
- artifact = wandb.Artifact(name="", type="")
- ```
- To log artifacts, see [Construct artifacts]({{< relref "/guides/core/artifacts/construct-an-artifact.md" >}}).
-3. If an artifact is logged to your personal entity, you will need to re-log it to an entity within your organization.
-
-### Confirm the path of a registry in the W&B App UI
-
-There are two ways to confirm the path of a registry with the UI: create an empty collection and view the collection details or copy and paste the autogenerated code on the collection's homepage.
-
-#### Copy and paste autogenerated code
-
-1. Navigate to the Registry app at https://wandb.ai/registry/.
-2. Click the registry you want to link an artifact to.
-3. At the top of the page, you will see an autogenerated code block.
-4. Copy and paste this into your code, ensure to replace the last part of the path with the name of your collection.
-
-{{< img src="/images/registry/get_autogenerated_code.gif" alt="Auto-generated code snippet" >}}
-
-#### Create an empty collection
-
-1. Navigate to the Registry app at https://wandb.ai/registry/.
-2. Click the registry you want to link an artifact to.
-4. Click on the empty collection. If an empty collection does not exist, create a new collection.
-5. Within the code snippet that appears, identify the `target_path` field within `.link_artifact()`.
-6. (Optional) Delete the collection.
-
-{{< img src="/images/registry/check_empty_collection.gif" alt="Create an empty collection" >}}
-
-For example, after completing the steps outlined, you find the code block with the `target_path` parameter:
-
-```python
-target_path =
- "smle-registries-bug-bash/wandb-registry-Golden Datasets/raw_images"
-```
-
-Breaking this down into its components, you can see what you will need to use to create the path to link your artifact programmatically:
-
-```python
-ORG_ENTITY_NAME = "smle-registries-bug-bash"
-REGISTRY_NAME = "Golden Datasets"
-COLLECTION_NAME = "raw_images"
-```
-
-{{% alert %}}
-Ensure that you replace the name of the collection from the temporary collection with the name of the collection that you want to link your artifact to.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/_index.md b/content/en/guides/core/registry/model_registry/_index.md
deleted file mode 100644
index f6811d0154..0000000000
--- a/content/en/guides/core/registry/model_registry/_index.md
+++ /dev/null
@@ -1,81 +0,0 @@
----
-description: Model registry to manage the model lifecycle from training to production
-menu:
- default:
- identifier: model-registry
- parent: registry
-title: Model registry
-weight: 9
-url: guides/registry/model_registry
-cascade:
-- url: guides/registry/model_registry/:filename
----
-
-{{% alert %}}
-W&B will eventually stop supporting W&B Model Registry. Users are encouraged to instead use [W&B Registry]({{< relref "/guides/core/registry/" >}}) for linking and sharing their model artifacts versions. W&B Registry broadens the capabilities of the legacy W&B Model Registry. For more information about W&B Registry, see the [Registry docs]({{< relref "/guides/core/registry/" >}}).
-
-
-W&B will migrate existing model artifacts linked to the legacy Model Registry to the new W&B Registry in the near future. See [Migrating from legacy Model Registry]({{< relref "/guides/core/registry/model_registry_eol.md" >}}) for information about the migration process.
-{{% /alert %}}
-
-The W&B Model Registry houses a team's trained models where ML Practitioners can publish candidates for production to be consumed by downstream teams and stakeholders. It is used to house staged/candidate models and manage workflows associated with staging.
-
-{{< img src="/images/models/model_reg_landing_page.png" alt="Model Registry" >}}
-
-With W&B Model Registry, you can:
-
-* [Bookmark your best model versions for each machine learning task.]({{< relref "./link-model-version.md" >}})
-* [Automate]({{< relref "/guides/core/automations/" >}}) downstream processes and model CI/CD.
-* Move model versions through its ML lifecycle; from staging to production.
-* Track a model's lineage and audit the history of changes to production models.
-
-{{< img src="/images/models/models_landing_page.png" alt="Models overview" >}}
-
-## How it works
-Track and manage your staged models with a few simple steps.
-
-1. **Log a model version**: In your training script, add a few lines of code to save the model files as an artifact to W&B.
-2. **Compare performance**: Check live charts to compare the metrics and sample predictions from model training and validation. Identify which model version performed the best.
-3. **Link to registry**: Bookmark the best model version by linking it to a registered model, either programmatically in Python or interactively in the W&B UI.
-
-The following code snippet demonstrates how to log and link a model to the Model Registry:
-
-```python
-import wandb
-import random
-
-# Start a new W&B run
-run = wandb.init(project="models_quickstart")
-
-# Simulate logging model metrics
-run.log({"acc": random.random()})
-
-# Create a simulated model file
-with open("my_model.h5", "w") as f:
- f.write("Model: " + str(random.random()))
-
-# Log and link the model to the Model Registry
-run.link_model(path="./my_model.h5", registered_model_name="MNIST")
-
-run.finish()
-```
-
-4. **Connect model transitions to CI/CD workflows**: transition candidate models through workflow stages and [automate downstream actions]({{< relref "/guides/core/automations/" >}}) with webhooks.
-
-
-## How to get started
-Depending on your use case, explore the following resources to get started with W&B Models:
-
-* Check out the two-part video series:
- 1. [Logging and registering models](https://www.youtube.com/watch?si=MV7nc6v-pYwDyS-3&v=ZYipBwBeSKE&feature=youtu.be)
- 2. [Consuming models and automating downstream processes](https://www.youtube.com/watch?v=8PFCrDSeHzw) in the Model Registry.
-* Read the [models walkthrough]({{< relref "./walkthrough.md" >}}) for a step-by-step outline of the W&B Python SDK commands you could use to create, track, and use a dataset artifact.
-* Learn about:
- * [Protected models and access control]({{< relref "./access_controls.md" >}}).
- * [How to connect Registry to CI/CD processes]({{< relref "/guides/core/automations/" >}}).
- * Set up [Slack notifications]({{< relref "./notifications.md" >}}) when a new model version is linked to a registered model.
-* Review [What is an ML Model Registry?](https://wandb.ai/wandb_fc/model-registry-reports/reports/What-is-an-ML-Model-Registry---Vmlldzo1MTE5MjYx) to learn how to integrate Model Registry into your ML workflow.
-* Take the W&B [Enterprise Model Management](https://www.wandb.courses/courses/enterprise-model-management) course and learn how to:
- * Use the W&B Model Registry to manage and version your models, track lineage, and promote models through different lifecycle stages
- * Automate your model management workflows using webhooks.
- * See how the Model Registry integrates with external ML systems and tools in your model development lifecycle for model evaluation, monitoring, and deployment.
diff --git a/content/en/guides/core/registry/model_registry/access_controls.md b/content/en/guides/core/registry/model_registry/access_controls.md
deleted file mode 100644
index 2542702c60..0000000000
--- a/content/en/guides/core/registry/model_registry/access_controls.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-description: Use model registry role based access controls (RBAC) to control who can
- update protected aliases.
-menu:
- default:
- identifier: access_controls
- parent: model-registry
-title: Manage data governance and access control
-weight: 10
----
-
-Use *protected aliases* to represent key stages of your model development pipeline. Only *Model Registry Administrators* can add, modify, or remove protected aliases. Model registry admins can define and use protected aliases. W&B blocks non admin users from adding or removing protected aliases from model versions.
-
-{{% alert %}}
-Only Team admins or current registry admins can manage the list of registry admins.
-{{% /alert %}}
-
-For example, suppose you set `staging` and `production` as protected aliases. Any member of your team can add new model versions. However, only admins can add a `staging` or `production` alias.
-
-
-## Set up access control
-The following steps describe how to set up access controls for your team’s model registry.
-
-1. Navigate to the [W&B Model Registry app](https://wandb.ai/registry/model).
-2. Select the gear button on the top right of the page.
-{{< img src="/images/models/rbac_gear_button.png" alt="Registry settings gear" >}}
-3. Select the **Manage registry admins** button.
-4. Within the **Members** tab, select the users you want to grant access to add and remove protected aliases from model versions.
-{{< img src="/images/models/access_controls_admins.gif" alt="Managing registry admins" >}}
-
-
-## Add protected aliases
-1. Navigate to the [W&B Model Registry app](https://wandb.ai/registry/model).
-2. Select the gear button on the top right of the page.
-{{< img src="/images/models/rbac_gear_button.png" alt="Registry settings gear button" >}}
-3. Scroll down to the **Protected Aliases** section.
-4. Click on the plus icon (**+**) icon to add new a new alias.
-{{< img src="/images/models/access_controls_add_protected_aliases.gif" alt="Adding protected aliases" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/consume-models.md b/content/en/guides/core/registry/model_registry/consume-models.md
deleted file mode 100644
index 6ab2be8953..0000000000
--- a/content/en/guides/core/registry/model_registry/consume-models.md
+++ /dev/null
@@ -1,95 +0,0 @@
----
-description: How to download a model with W&B Python SDK
-menu:
- default:
- identifier: consume-models
- parent: model-registry
-title: Download a model version
-weight: 8
----
-
-Use the W&B Python SDK to download a model artifact that you linked to the Model Registry.
-
-{{% alert %}}
-You are responsible for providing additional Python functions, API calls to reconstruct, deserialize your model into a form that you can work with.
-
-W&B suggests that you document information on how to load models into memory with model cards. For more information, see the [Document machine learning models]({{< relref "./create-model-cards.md" >}}) page.
-{{% /alert %}}
-
-
-Replace values within `<>` with your own:
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(project="", entity="")
-
-# Access and download model. Returns path to downloaded artifact
-downloaded_model_path = run.use_model(name="")
-```
-
-Reference a model version with one of following formats listed:
-
-* `latest` - Use `latest` alias to specify the model version that is most recently linked.
-* `v#` - Use `v0`, `v1`, `v2`, and so on to fetch a specific version in the Registered Model
-* `alias` - Specify the custom alias that you and your team assigned to your model version
-
-See [`use_model`]({{< relref "/ref/python/experiments/run.md#use_model" >}}) in the API Reference guide for more information on possible parameters and return type.
-
-
-Example: Download and use a logged model
-
-For example, in the proceeding code snippet a user called the `use_model` API. They specified the name of the model artifact they want to fetch and they also provided a version/alias. They then stored the path that returned from the API to the `downloaded_model_path` variable.
-
-```python
-import wandb
-
-entity = "luka"
-project = "NLP_Experiments"
-alias = "latest" # semantic nickname or identifier for the model version
-model_artifact_name = "fine-tuned-model"
-
-# Initialize a run
-run = wandb.init()
-# Access and download model. Returns path to downloaded artifact
-
-downloaded_model_path = run.use_model(name=f"{entity/project/model_artifact_name}:{alias}")
-```
-
-
-
-{{% alert title="Planned deprecation for W&B Model Registry in 2024" %}}
-The proceeding tabs demonstrate how to consume model artifacts using the soon to be deprecated Model Registry.
-
-Use the W&B Registry to track, organize and consume model artifacts. For more information see the [Registry docs]({{< relref "/guides/core/registry/" >}}).
-{{% /alert %}}
-
-{{< tabpane text=true >}}
- {{% tab header="CLI" %}}
-Replace values within `<>` with your own:
-```python
-import wandb
-# Initialize a run
-run = wandb.init(project="", entity="")
-# Access and download model. Returns path to downloaded artifact
-downloaded_model_path = run.use_model(name="")
-```
-Reference a model version with one of following formats listed:
-
-* `latest` - Use `latest` alias to specify the model version that is most recently linked.
-* `v#` - Use `v0`, `v1`, `v2`, and so on to fetch a specific version in the Registered Model
-* `alias` - Specify the custom alias that you and your team assigned to your model version
-
-See [`use_model`]({{< relref "/ref/python/experiments/run.md#use_model" >}}) in the API Reference guide for parameters and return type.
- {{% /tab %}}
- {{% tab header="W&B App" %}}
-1. Navigate to the [Model Registry App](https://wandb.ai/registry/model).
-2. Select **View details** next to the name of the registered model that contains the model you want to download.
-3. Within the Versions section, select the View button next to the model version you want to download.
-4. Select the **Files** tab.
-5. Click on the download button next to the model file you want to download.
-{{< img src="/images/models/download_model_ui.gif" alt="Download model from UI" >}}
- {{% /tab %}}
-{{< /tabpane >}}
-
diff --git a/content/en/guides/core/registry/model_registry/create-model-cards.md b/content/en/guides/core/registry/model_registry/create-model-cards.md
deleted file mode 100644
index 0de8d46b60..0000000000
--- a/content/en/guides/core/registry/model_registry/create-model-cards.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-description: Add descriptions to model card to document your model
-menu:
- default:
- identifier: create-model-cards
- parent: model-registry
-title: Document machine learning model
-weight: 8
----
-
-Add a description to the model card of your registered model to document aspects of your machine learning model. Some topics worth documenting include:
-
-* **Summary**: A summary of what the model is. The purpose of the model. The machine learning framework the model uses, and so forth.
-* **Training data**: Describe the training data used, processing done on the training data set, where is that data stored and so forth.
-* **Architecture**: Information about the model architecture, layers, and any specific design choices.
-* **Deserialize the model**: Provide information on how someone on your team can load the model into memory.
-* **Task**: The specific type of task or problem that the machine learning model is designed to perform. It's a categorization of the model's intended capability.
-* **License**: The legal terms and permissions associated with the use of the machine learning model. It helps model users understand the legal framework under which they can utilize the model.
-* **References**: Citations or references to relevant research papers, datasets, or external resources.
-* **Deployment**: Details on how and where the model is deployed and guidance on how the model is integrated into other enterprise systems, such as a workflow orchestration platforms.
-
-## Add a description to the model card
-
-1. Navigate to the [W&B Model Registry app](https://wandb.ai/registry/model).
-2. Select **View details** next to the name of the registered model you want to create a model card for.
-2. Go to the **Model card** section.
-{{< img src="/images/models/model_card_example.png" alt="Model card example" >}}
-3. Within the **Description** field, provide information about your machine learning model. Format text within a model card with [Markdown markup language](https://www.markdownguide.org/).
-
-For example, the following images shows the model card of a **Credit-card Default Prediction** registered model.
-{{< img src="/images/models/model_card_credit_example.png" alt="Model card credit scoring" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/create-registered-model.md b/content/en/guides/core/registry/model_registry/create-registered-model.md
deleted file mode 100644
index c063151824..0000000000
--- a/content/en/guides/core/registry/model_registry/create-registered-model.md
+++ /dev/null
@@ -1,50 +0,0 @@
----
-description: Create a registered model to hold all the candidate models for your modeling
- tasks.
-menu:
- default:
- identifier: create-registered-model
- parent: model-registry
-title: Create a registered model
-weight: 4
----
-
-
-Create a [registered model]({{< relref "./model-management-concepts.md#registered-model" >}}) to hold all the candidate models for your modeling tasks. You can create a registered model interactively within the Model Registry or programmatically with the Python SDK.
-
-## Programmatically create registered a model
-Programmatically register a model with the W&B Python SDK. W&B automatically creates a registered model for you if the registered model doesn't exist.
-
-Ensure to replace other the values enclosed in `<>` with your own:
-
-```python
-import wandb
-
-run = wandb.init(entity="", project="")
-run.link_model(path="", registered_model_name="")
-run.finish()
-```
-
-The name you provide for `registered_model_name` is the name that appears in the [Model Registry App](https://wandb.ai/registry/model).
-
-## Interactively create a registered model
-Interactively create a registered model within the [Model Registry App](https://wandb.ai/registry/model).
-
-1. Navigate to the [Model Registry App](https://wandb.ai/registry/model).
-{{< img src="/images/models/create_registered_model_1.png" alt="Model Registry landing page" >}}
-2. Click the **New registered model** button located in the top right of the Model Registry page.
-{{< img src="/images/models/create_registered_model_model_reg_app.png" alt="New registered model button" >}}
-3. From the panel that appears, select the entity you want the registered model to belong to from the **Owning Entity** dropdown.
-{{< img src="/images/models/create_registered_model_3.png" alt="Model creation form" >}}
-4. Provide a name for your model in the **Name** field.
-5. From the **Type** dropdown, select the type of artifacts to link to the registered model.
-6. (Optional) Add a description about your model in the **Description** field.
-7. (Optional) Within the **Tags** field, add one or more tags.
-8. Click **Register model**.
-
-
-{{% alert %}}
-Manual linking a model to the model registry is useful for one-off models. However, it is often useful to [programmatically link model versions to the model registry]({{< relref "link-model-version#programmatically-link-a-model" >}}).
-
-For example, suppose you have a nightly job. It is tedious to manually link a model created each night. Instead, you could create a script that evaluates the model, and if the model improves in performance, link that model to the model registry with the W&B Python SDK.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/link-model-version.md b/content/en/guides/core/registry/model_registry/link-model-version.md
deleted file mode 100644
index ed6aadf7be..0000000000
--- a/content/en/guides/core/registry/model_registry/link-model-version.md
+++ /dev/null
@@ -1,98 +0,0 @@
----
-description: Link a model version to a registered model with the W&B App or programmatically
- with the Python SDK.
-menu:
- default:
- identifier: link-model-version
- parent: model-registry
-title: Link a model version
-weight: 5
----
-
-
-Link a model version to a registered model with the W&B App or programmatically with the Python SDK.
-
-## Programmatically link a model
-
-Use the [`link_model`]({{< relref "/ref/python/experiments/run.md#link_model" >}}) method to programmatically log model files to a W&B run and link it to the [W&B Model Registry]({{< relref "./" >}}).
-
-Ensure to replace other the values enclosed in `<>` with your own:
-
-```python
-import wandb
-
-run = wandb.init(entity="", project="")
-run.link_model(path="", registered_model_name="")
-run.finish()
-```
-
-W&B creates a registered model for you if the name you specify for the `registered-model-name` parameter does not already exist.
-
-For example, suppose you have an existing registered model named "Fine-Tuned-Review-Autocompletion"(`registered-model-name="Fine-Tuned-Review-Autocompletion"`) in your Model Registry. And suppose that a few model versions are linked to it: `v0`, `v1`, `v2`. If you programmatically link a new model and use the same registered model name (`registered-model-name="Fine-Tuned-Review-Autocompletion"`), W&B links this model to the existing registered model and assigns it a model version `v3`. If no registered model with this name exists, a new one registered model is created and it will have a model version `v0`.
-
-See an example ["Fine-Tuned-Review-Autocompletion" registered model here](https://wandb.ai/reviewco/registry/model?selectionPath=reviewco%2Fmodel-registry%2FFinetuned-Review-Autocompletion&view=all-models).
-
-## Interactively link a model
-Interactively link a model with the Model Registry or with the Artifact browser.
-
-{{< tabpane text=true >}}
- {{% tab header="Model Registry" %}}
-1. Navigate to the [Model Registry App](https://wandb.ai/registry/model).
-2. Hover your mouse next to the name of the registered model you want to link a new model to.
-3. Select the meatball menu icon (three horizontal dots) next to **View details**.
-4. From the dropdown, select **Link new version**.
-5. From the **Project** dropdown, select the name of the project that contains your model.
-6. From the **Model Artifact** dropdown, select the name of the model artifact.
-7. From the **Version** dropdown, select the model version you want to link to the registered model.
-
-{{< img src="/images/models/link_model_wmodel_reg.gif" alt="Linking model version to registry" >}}
- {{% /tab %}}
- {{% tab header="Artifact browser" %}}
-1. Navigate to your project's artifact browser on the W&B App at: `https://wandb.ai///artifacts`
-2. Select the Artifacts icon on the left sidebar.
-3. Click on the model version you want to link to your registry.
-4. Within the **Version overview** section, click the **Link to registry** button.
-5. From the modal that appears on the right of the screen, select a registered model from the **Select a register model** menu dropdown.
-6. Click **Next step**.
-7. (Optional) Select an alias from the **Aliases** dropdown.
-8. Click **Link to registry**.
-
-{{< img src="/images/models/manual_linking.gif" alt="Manual model linking" >}}
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-
-## View the source of linked models
-
-There are two ways to view the source of linked models: The artifact browser within the project that the model is logged to and the W&B Model Registry.
-
-A pointer connects a specific model version in the model registry to the source model artifact (located within the project the model is logged to). The source model artifact also has a pointer to the model registry.
-
-{{< tabpane text=true >}}
- {{% tab header="Model Registry" %}}
-1. Navigate to your [Model Registry App](https://wandb.ai/registry/model).
-{{< img src="/images/models/create_registered_model_1.png" alt="Create registered model" >}}
-2. Select **View details** next the name of your registered model.
-3. Within the **Versions** section, select **View** next to the model version you want to investigate.
-4. Click on the **Version** tab within the right panel.
-5. Within the **Version overview** section there is a row that contains a **Source Version** field. The **Source Version** field shows both the name of the model and the model's version.
-
-For example, the following image shows a `v0` model version called `mnist_model` (see **Source version** field `mnist_model:v0`), linked to a registered model called `MNIST-dev`.
-
-{{< img src="/images/models/view_linked_model_registry.png" alt="Linked model in registry" >}}
- {{% /tab %}}
- {{% tab header="Artifact browser" %}}
-1. Navigate to your project's artifact browser on the W&B App at: `https://wandb.ai///artifacts`
-2. Select the Artifacts icon on the left sidebar.
-3. Expand the **model** dropdown menu from the Artifacts panel.
-4. Select the name and version of the model linked to the model registry.
-5. Click on the **Version** tab within the right panel.
-6. Within the **Version overview** section there is a row that contains a **Linked To** field. The **Linked To** field shows both the name of the registered model and the version it possesses(`registered-model-name:version`).
-
-For example, in the following image, there is a registered model called `MNIST-dev` (see the **Linked To** field). A model version called `mnist_model` with a version `v0`(`mnist_model:v0`) points to the `MNIST-dev` registered model.
-
-
-{{< img src="/images/models/view_linked_model_artifacts_browser.png" alt="Model artifacts browser" >}}
- {{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/registry/model_registry/log-model-to-experiment.md b/content/en/guides/core/registry/model_registry/log-model-to-experiment.md
deleted file mode 100644
index 401019408e..0000000000
--- a/content/en/guides/core/registry/model_registry/log-model-to-experiment.md
+++ /dev/null
@@ -1,86 +0,0 @@
----
-description: Track a model, the model's dependencies, and other information relevant
- to that model with the W&B Python SDK.
-menu:
- default:
- identifier: log-model-to-experiment
- parent: model-registry
-title: Track a model
-weight: 3
----
-
-
-Track a model, the model's dependencies, and other information relevant to that model with the W&B Python SDK.
-
-Under the hood, W&B creates a lineage of [model artifact]({{< relref "./model-management-concepts.md#model-artifact" >}}) that you can view with the W&B App or programmatically with the W&B Python SDK. See the [Create model lineage map]({{< relref "./model-lineage.md" >}}) for more information.
-
-## How to log a model
-
-Use the `run.log_model` API to log a model. Provide the path where your model files are saved to the `path` parameter. The path can be a local file, directory, or [reference URI]({{< relref "/guides/core/artifacts/track-external-files.md#amazon-s3--gcs--azure-blob-storage-references" >}}) to an external bucket such as `s3://bucket/path`.
-
-Optionally provide a name for the model artifact for the `name` parameter. If `name` is not specified, W&B uses the basename of the input path prepended with the run ID.
-
-Copy and paste the proceeding code snippet. Ensure to replace values enclosed in `<>` with your own.
-
-```python
-import wandb
-
-# Initialize a W&B run
-run = wandb.init(project="", entity="")
-
-# Log the model
-run.log_model(path="", name="")
-```
-
-
-
-Example: Log a Keras model to W&B
-
-The proceeding code example shows how to log a convolutional neural network (CNN) model to W&B.
-
-```python
-import os
-import wandb
-from tensorflow import keras
-from tensorflow.keras import layers
-
-config = {"optimizer": "adam", "loss": "categorical_crossentropy"}
-
-# Initialize a W&B run
-run = wandb.init(entity="charlie", project="mnist-project", config=config)
-
-# Training algorithm
-loss = run.config["loss"]
-optimizer = run.config["optimizer"]
-metrics = ["accuracy"]
-num_classes = 10
-input_shape = (28, 28, 1)
-
-model = keras.Sequential(
- [
- layers.Input(shape=input_shape),
- layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Flatten(),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation="softmax"),
- ]
-)
-
-model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
-
-# Save model
-model_filename = "model.h5"
-local_filepath = "./"
-full_path = os.path.join(local_filepath, model_filename)
-model.save(filepath=full_path)
-
-# Log the model
-run.log_model(path=full_path, name="MNIST")
-
-# Explicitly tell W&B to end the run.
-run.finish()
-```
-
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/model-lineage.md b/content/en/guides/core/registry/model_registry/model-lineage.md
deleted file mode 100644
index 693d7f3fde..0000000000
--- a/content/en/guides/core/registry/model_registry/model-lineage.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-description: ''
-menu:
- default:
- identifier: model-lineage
- parent: model-registry
-title: Create model lineage map
-weight: 7
----
-
-This page describes creating lineage graphs in the legacy W&B Model Registry. To learn about lineage graphs in W&B Registry, refer to [Create and view lineage maps]({{< relref "../lineage.md" >}}).
-
-{{% alert %}}
-W&B will transition assets from the legacy [W&B Model Registry]({{< relref "/guides/core/registry/model_registry/" >}}) to the new [W&B Registry]({{< relref "./" >}}). This migration will be fully managed and triggered by W&B, requiring no intervention from users. The process is designed to be as seamless as possible, with minimal disruption to existing workflows. Refer to [Migrate from legacy Model Registry]({{< relref "../model_registry_eol.md" >}}).
-{{% /alert %}}
-
-
-A useful feature of logging model artifacts to W&B are lineage graphs. Lineage graphs show artifacts logged by a run as well as artifacts used by specific run.
-
-This means that, when you log a model artifact, you at a minimum have access to view the W&B run that used or produced the model artifact. If you [track a dependency]({{< relref "#track-an-artifact-dependency" >}}), you also see the inputs used by the model artifact.
-
-For example, the proceeding image shows artifacts created and used throughout an ML experiment:
-
-{{< img src="/images/models/model_lineage_example.png" alt="Model lineage graph" >}}
-
-From left to right, the image shows:
-1. The `jumping-monkey-1` W&B run created the `mnist_dataset:v0` dataset artifact.
-2. The `vague-morning-5` W&B run trained a model using the `mnist_dataset:v0` dataset artifact. The output of this W&B run was a model artifact called `mnist_model:v0`.
-3. A run called `serene-haze-6` used the model artifact (`mnist_model:v0`) to evaluate the model.
-
-
-## Track an artifact dependency
-
-Declare an dataset artifact as an input to a W&B run with the `use_artifact` API to track a dependency.
-
-The proceeding code snippet shows how to use the `use_artifact` API:
-
-```python
-# Initialize a run
-run = wandb.init(project=project, entity=entity)
-
-# Get artifact, mark it as a dependency
-artifact = run.use_artifact(artifact_or_name="name", aliases="")
-```
-
-Once you have retrieved your artifact, you can use that artifact to (for example), evaluate the performance of a model.
-
-
-
-Example: Train a model and track a dataset as the input of a model
-
-```python
-job_type = "train_model"
-
-config = {
- "optimizer": "adam",
- "batch_size": 128,
- "epochs": 5,
- "validation_split": 0.1,
-}
-
-run = wandb.init(project=project, job_type=job_type, config=config)
-
-version = "latest"
-name = "{}:{}".format("{}_dataset".format(model_use_case_id), version)
-
-artifact = run.use_artifact(name)
-
-train_table = artifact.get("train_table")
-x_train = train_table.get_column("x_train", convert_to="numpy")
-y_train = train_table.get_column("y_train", convert_to="numpy")
-
-# Store values from our config dictionary into variables for easy accessing
-num_classes = 10
-input_shape = (28, 28, 1)
-loss = "categorical_crossentropy"
-optimizer = run.config["optimizer"]
-metrics = ["accuracy"]
-batch_size = run.config["batch_size"]
-epochs = run.config["epochs"]
-validation_split = run.config["validation_split"]
-
-# Create model architecture
-model = keras.Sequential(
- [
- layers.Input(shape=input_shape),
- layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Flatten(),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation="softmax"),
- ]
-)
-model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
-
-# Generate labels for training data
-y_train = keras.utils.to_categorical(y_train, num_classes)
-
-# Create training and test set
-x_t, x_v, y_t, y_v = train_test_split(x_train, y_train, test_size=0.33)
-
-# Train the model
-model.fit(
- x=x_t,
- y=y_t,
- batch_size=batch_size,
- epochs=epochs,
- validation_data=(x_v, y_v),
- callbacks=[WandbCallback(log_weights=True, log_evaluation=True)],
-)
-
-# Save model locally
-path = "model.h5"
-model.save(path)
-
-path = "./model.h5"
-registered_model_name = "MNIST-dev"
-name = "mnist_model"
-
-run.link_model(path=path, registered_model_name=registered_model_name, name=name)
-run.finish()
-```
-
-
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/model-management-concepts.md b/content/en/guides/core/registry/model_registry/model-management-concepts.md
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--- a/content/en/guides/core/registry/model_registry/model-management-concepts.md
+++ /dev/null
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----
-description: Model Registry terms and concepts
-menu:
- default:
- identifier: model-management-concepts
- parent: model-registry
-title: Model Registry Terms and Concepts
-weight: 2
----
-
-The following terms describe key components of the W&B Model Registry: [*model version*]({{< relref "#model-version" >}}), [*model artifact*]({{< relref "#model-artifact" >}}), and [*registered model*]({{< relref "#registered-model" >}}).
-
-## Model version
-A model version represents a single model checkpoint. Model versions are a snapshot at a point in time of a model and its files within an experiment.
-
-A model version is an immutable directory of data and metadata that describes a trained model. W&B suggests that you add files to your model version that let you store (and restore) your model architecture and learned parameters at a later date.
-
-A model version belongs to one, and only one, [model artifact]({{< relref "#model-artifact" >}}). A model version can belong to zero or more, [registered models]({{< relref "#registered-model" >}}). Model versions are stored in a model artifact in the order they are logged to the model artifact. W&B automatically creates a new model version if it detects that a model you log (to the same model artifact) has different contents than a previous model version.
-
-Store files within model versions that are produced from the serialization process provided by your modeling library (for example, [PyTorch](https://pytorch.org/tutorials/beginner/saving_loading_models.html) and [Keras](https://www.tensorflow.org/guide/keras/save_and_serialize)).
-
-
-
-## Model alias
-
-Model aliases are mutable strings that allow you to uniquely identify or reference a model version in your registered model with a semantically related identifier. You can only assign an alias to one version of a registered model. This is because an alias should refer to a unique version when used programmatically. It also allows aliases to be used to capture a model's state (champion, candidate, production).
-
-It is common practice to use aliases such as `"best"`, `"latest"`, `"production"`, or `"staging"` to mark model versions with special purposes.
-
-For example, suppose you create a model and assign it a `"best"` alias. You can refer to that specific model with `run.use_model`
-
-```python
-import wandb
-run = wandb.init()
-name = f"{entity/project/model_artifact_name}:{alias}"
-run.use_model(name=name)
-```
-
-## Model tags
-Model tags are keywords or labels that belong to one or more registered models.
-
-Use model tags to organize registered models into categories and to search over those categories in the Model Registry's search bar. Model tags appear at the top of the Registered Model Card. You might choose to use them to group your registered models by ML task, owning team, or priority. The same model tag can be added to multiple registered models to allow for grouping.
-
-{{% alert %}}
-Model tags, which are labels applied to registered models for grouping and discoverability, are different from [model aliases]({{< relref "#model-alias" >}}). Model aliases are unique identifiers or nicknames that you use to fetch a model version programatically. To learn more about using tags to organize the tasks in your Model Registry, see [Organize models]({{< relref "./organize-models.md" >}}).
-{{% /alert %}}
-
-
-## Model artifact
-A model artifact is a collection of logged [model versions]({{< relref "#model-version" >}}). Model versions are stored in a model artifact in the order they are logged to the model artifact.
-
-A model artifact can contain one or more model versions. A model artifact can be empty if no model versions are logged to it.
-
-For example, suppose you create a model artifact. During model training, you periodically save your model during checkpoints. Each checkpoint corresponds to its own [model version]({{< relref "#model-version" >}}). All of the model versions created during your model training and checkpoint saving are stored in the same model artifact you created at the beginning of your training script.
-
-
-
-
-The proceeding image shows a model artifact that contains three model versions: v0, v1, and v2.
-
-{{< img src="/images/models/mr1c.png" alt="Model registry concepts" >}}
-
-View an [example model artifact here](https://wandb.ai/timssweeney/model_management_docs_official_v0/artifacts/model/mnist-zws7gt0n).
-
-## Registered model
-A registered model is a collection of pointers (links) to model versions. You can think of a registered model as a folder of "bookmarks" of candidate models for the same ML task. Each "bookmark" of a registered model is a pointer to a [model version]({{< relref "#model-version" >}}) that belongs to a [model artifact]({{< relref "#model-artifact" >}}). You can use [model tags]({{< relref "#model-tags" >}}) to group your registered models.
-
-Registered models often represent candidate models for a single modeling use case or task. For example, you might create registered model for different image classification task based on the model you use: `ImageClassifier-ResNet50`, `ImageClassifier-VGG16`, `DogBreedClassifier-MobileNetV2` and so on. Model versions are assigned version numbers in the order in which they were linked to the registered model.
-
-
-View an [example Registered Model here](https://wandb.ai/reviewco/registry/model?selectionPath=reviewco%2Fmodel-registry%2FFinetuned-Review-Autocompletion&view=versions).
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/notifications.md b/content/en/guides/core/registry/model_registry/notifications.md
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----
-description: Get Slack notifications when a new model version is linked to the model
- registry.
-menu:
- default:
- identifier: notifications
- parent: model-registry
-title: Create alerts and notifications
-weight: 9
----
-
-
-Receive Slack notifications when a new model version is linked to the model registry.
-
-
-1. Navigate to the [W&B Model Registry app](https://wandb.ai/registry/model).
-2. Select the registered model you want to receive notifications from.
-3. Click on the **Connect Slack** button.
- {{< img src="/images/models/connect_to_slack.png" alt="Connect to Slack" >}}
-4. Follow the instructions to enable W&B in your Slack workspace that appear on the OAuth page.
-
-
-Once you have configured Slack notifications for your team, you can pick and choose registered models to get notifications from.
-
-{{% alert %}}
-A toggle that reads **New model version linked to...** appears instead of a **Connect Slack** button if you have Slack notifications configured for your team.
-{{% /alert %}}
-
-The screenshot below shows a FMNIST classifier registered model that has Slack notifications.
-
-{{< img src="/images/models/conect_to_slack_fmnist.png" alt="Slack notification example" >}}
-
-A message is automatically posted to the connected Slack channel each time a new model version is linked to the FMNIST classifier registered model.
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/organize-models.md b/content/en/guides/core/registry/model_registry/organize-models.md
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index c33cb0fb49..0000000000
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----
-menu:
- default:
- identifier: organize-models
- parent: model-registry
-title: Organize models
-weight: 6
----
-
-Use model tags to organize registered models into categories and to search over those categories.
-
-1. Navigate to the [W&B Model Registry app](https://wandb.ai/registry/model).
-2. Select **View details** next to the name of the registered model you want to add a model tag to.
- {{< img src="/images/models/organize-models-model-reg-landing.png" alt="Model Registry view details" >}}
-2. Scroll to the **Model card** section.
-3. Click the plus button (**+**) next to the **Tags** field.
-{{< img src="/images/models/organize-models-seleticon.png" alt="Model tags section" >}}
-4. Type in the name for your tag or search for a pre-existing model tag.
- For example. the following image shows multiple model tags added to a registered model called **FineTuned-Review-Autocompletion**:
-
- {{< img src="/images/models/model-tags-modelregview.png" alt="Model with multiple tags" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry/walkthrough.md b/content/en/guides/core/registry/model_registry/walkthrough.md
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index 8e726c387f..0000000000
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----
-description: Learn how to use W&B for Model Management
-menu:
- default:
- identifier: walkthrough_model_registry
- parent: model-registry
-title: 'Tutorial: Use W&B for model management'
-weight: 1
----
-
-
-The following walkthrough shows you how to log a model to W&B. By the end of the walkthrough you will:
-
-* Create and train a model with the MNIST dataset and the Keras framework.
-* Log the model that you trained to a W&B project
-* Mark the dataset used as a dependency to the model you created
-* Link the model to the W&B Registry.
-* Evaluate the performance of the model you link to the registry
-* Mark a model version ready for production.
-
-{{% alert %}}
-* Copy the code snippets in the order presented in this guide.
-* Code not unique to the Model Registry are hidden in collapsible cells.
-{{% /alert %}}
-
-## Setting up
-
-Before you get started, import the Python dependencies required for this walkthrough:
-
-```python
-import wandb
-import numpy as np
-from tensorflow import keras
-from tensorflow.keras import layers
-from wandb.integration.keras import WandbMetricsLogger
-from sklearn.model_selection import train_test_split
-```
-
-Provide your W&B entity to the `entity` variable:
-
-```python
-entity = ""
-```
-
-
-### Create a dataset artifact
-
-First, create a dataset. The proceeding code snippet creates a function that downloads the MNIST dataset:
-```python
-def generate_raw_data(train_size=6000):
- eval_size = int(train_size / 6)
- (x_train, y_train), (x_eval, y_eval) = keras.datasets.mnist.load_data()
-
- x_train = x_train.astype("float32") / 255
- x_eval = x_eval.astype("float32") / 255
- x_train = np.expand_dims(x_train, -1)
- x_eval = np.expand_dims(x_eval, -1)
-
- print("Generated {} rows of training data.".format(train_size))
- print("Generated {} rows of eval data.".format(eval_size))
-
- return (x_train[:train_size], y_train[:train_size]), (
- x_eval[:eval_size],
- y_eval[:eval_size],
- )
-
-# Create dataset
-(x_train, y_train), (x_eval, y_eval) = generate_raw_data()
-```
-
-Next, upload the dataset to W&B. To do this, create an [artifact]({{< relref "/guides/core/artifacts/" >}}) object and add the dataset to that artifact.
-
-```python
-project = "model-registry-dev"
-
-model_use_case_id = "mnist"
-job_type = "build_dataset"
-
-# Initialize a W&B run
-run = wandb.init(entity=entity, project=project, job_type=job_type)
-
-# Create W&B Table for training data
-train_table = wandb.Table(data=[], columns=[])
-train_table.add_column("x_train", x_train)
-train_table.add_column("y_train", y_train)
-train_table.add_computed_columns(lambda ndx, row: {"img": wandb.Image(row["x_train"])})
-
-# Create W&B Table for eval data
-eval_table = wandb.Table(data=[], columns=[])
-eval_table.add_column("x_eval", x_eval)
-eval_table.add_column("y_eval", y_eval)
-eval_table.add_computed_columns(lambda ndx, row: {"img": wandb.Image(row["x_eval"])})
-
-# Create an artifact object
-artifact_name = "{}_dataset".format(model_use_case_id)
-artifact = wandb.Artifact(name=artifact_name, type="dataset")
-
-# Add wandb.WBValue obj to the artifact.
-artifact.add(train_table, "train_table")
-artifact.add(eval_table, "eval_table")
-
-# Persist any changes made to the artifact.
-artifact.save()
-
-# Tell W&B this run is finished.
-run.finish()
-```
-
-{{% alert %}}
-Storing files (such as datasets) to an artifact is useful in the context of logging models because you lets you track a model's dependencies.
-{{% /alert %}}
-
-
-## Train a model
-Train a model with the artifact dataset you created in the previous step.
-
-### Declare dataset artifact as an input to the run
-
-Declare the dataset artifact you created in a previous step as the input to the W&B run. This is particularly useful in the context of logging models because declaring an artifact as an input to a run lets you track the dataset (and the version of the dataset) used to train a specific model. W&B uses the information collected to create a [lineage map]({{< relref "./model-lineage.md" >}}).
-
-Use the `use_artifact` API to both declare the dataset artifact as the input of the run and to retrieve the artifact itself.
-
-```python
-job_type = "train_model"
-config = {
- "optimizer": "adam",
- "batch_size": 128,
- "epochs": 5,
- "validation_split": 0.1,
-}
-
-# Initialize a W&B run
-run = wandb.init(project=project, job_type=job_type, config=config)
-
-# Retrieve the dataset artifact
-version = "latest"
-name = "{}:{}".format("{}_dataset".format(model_use_case_id), version)
-artifact = run.use_artifact(artifact_or_name=name)
-
-# Get specific content from the dataframe
-train_table = artifact.get("train_table")
-x_train = train_table.get_column("x_train", convert_to="numpy")
-y_train = train_table.get_column("y_train", convert_to="numpy")
-```
-
-For more information about tracking the inputs and output of a model, see [Create model lineage]({{< relref "./model-lineage.md" >}}) map.
-
-### Define and train model
-
-For this walkthrough, define a 2D Convolutional Neural Network (CNN) with Keras to classify images from the MNIST dataset.
-
-
-Train CNN on MNIST data
-
-```python
-# Store values from our config dictionary into variables for easy accessing
-num_classes = 10
-input_shape = (28, 28, 1)
-loss = "categorical_crossentropy"
-optimizer = run.config["optimizer"]
-metrics = ["accuracy"]
-batch_size = run.config["batch_size"]
-epochs = run.config["epochs"]
-validation_split = run.config["validation_split"]
-
-# Create model architecture
-model = keras.Sequential(
- [
- layers.Input(shape=input_shape),
- layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Flatten(),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation="softmax"),
- ]
-)
-model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
-
-# Generate labels for training data
-y_train = keras.utils.to_categorical(y_train, num_classes)
-
-# Create training and test set
-x_t, x_v, y_t, y_v = train_test_split(x_train, y_train, test_size=0.33)
-```
-Next, train the model:
-
-```python
-# Train the model
-model.fit(
- x=x_t,
- y=y_t,
- batch_size=batch_size,
- epochs=epochs,
- validation_data=(x_v, y_v),
- callbacks=[WandbCallback(log_weights=True, log_evaluation=True)],
-)
-```
-
-Finally, save the model locally on your machine:
-
-```python
-# Save model locally
-path = "model.h5"
-model.save(path)
-```
-
-
-
-
-## Log and link a model to the Model Registry
-Use the [`link_model`]({{< relref "/ref/python/experiments/run.md#link_model" >}}) API to log model one ore more files to a W&B run and link it to the [W&B Model Registry]({{< relref "./" >}}).
-
-```python
-path = "./model.h5"
-registered_model_name = "MNIST-dev"
-
-run.link_model(path=path, registered_model_name=registered_model_name)
-run.finish()
-```
-
-W&B creates a registered model for you if the name you specify for `registered-model-name` does not already exist.
-
-See [`link_model`]({{< relref "/ref/python/experiments/run.md#link_model" >}}) in the API Reference guide for optional parameters.
-
-## Evaluate the performance of a model
-It is common practice to evaluate the performance of a one or more models.
-
-First, get the evaluation dataset artifact stored in W&B in a previous step.
-
-```python
-job_type = "evaluate_model"
-
-# Initialize a run
-run = wandb.init(project=project, entity=entity, job_type=job_type)
-
-model_use_case_id = "mnist"
-version = "latest"
-
-# Get dataset artifact, mark it as a dependency
-artifact = run.use_artifact(
- "{}:{}".format("{}_dataset".format(model_use_case_id), version)
-)
-
-# Get desired dataframe
-eval_table = artifact.get("eval_table")
-x_eval = eval_table.get_column("x_eval", convert_to="numpy")
-y_eval = eval_table.get_column("y_eval", convert_to="numpy")
-```
-
-Download the [model version]({{< relref "./model-management-concepts.md#model-version" >}}) from W&B that you want to evaluate. Use the `use_model` API to access and download your model.
-
-```python
-alias = "latest" # alias
-name = "mnist_model" # name of the model artifact
-
-# Access and download model. Returns path to downloaded artifact
-downloaded_model_path = run.use_model(name=f"{name}:{alias}")
-```
-
-Load the Keras model and compute the loss:
-
-```python
-model = keras.models.load_model(downloaded_model_path)
-
-y_eval = keras.utils.to_categorical(y_eval, 10)
-(loss, _) = model.evaluate(x_eval, y_eval)
-score = (loss, _)
-```
-
-Finally, log the loss metric to the W&B run:
-
-```python
-# # Log metrics, images, tables, or any data useful for evaluation.
-run.log(data={"loss": (loss, _)})
-```
-
-
-## Promote a model version
-Mark a model version ready for the next stage of your machine learning workflow with a [*model alias*]({{< relref "./model-management-concepts.md#model-alias" >}}). Each registered model can have one or more model aliases. A model alias can only belong to a single model version at a time.
-
-For example, suppose that after evaluating a model's performance, you are confident that the model is ready for production. To promote that model version, add the `production` alias to that specific model version.
-
-{{% alert %}}
-The `production` alias is one of the most common aliases used to mark a model as production-ready.
-{{% /alert %}}
-
-You can add an alias to a model version interactively with the W&B App UI or programmatically with the Python SDK. The following steps show how to add an alias with the W&B Model Registry App:
-
-
-1. Navigate to the [Model Registry App](https://wandb.ai/registry/model).
-2. Click **View details** next to the name of your registered model.
-3. Within the **Versions** section, click the **View** button next to the name of the model version you want to promote.
-4. Next to the **Aliases** field, click the plus icon (**+**).
-5. Type in `production` into the field that appears.
-6. Press Enter on your keyboard.
-
-
-{{< img src="/images/models/promote_model_production.gif" alt="Promote model to production" >}}
\ No newline at end of file
diff --git a/content/en/guides/core/registry/model_registry_eol.md b/content/en/guides/core/registry/model_registry_eol.md
deleted file mode 100644
index 1082fda3a6..0000000000
--- a/content/en/guides/core/registry/model_registry_eol.md
+++ /dev/null
@@ -1,168 +0,0 @@
----
-menu:
- default:
- identifier: model_registry_eol
- parent: registry
-title: Migrate from legacy Model Registry
-weight: 9
----
-
-W&B is migrating from the legacy **Model Registry** to the enhanced **W&B Registry**. This transition is designed to be seamless and fully managed by W&B. The migration process will preserve your workflows while unlocking powerful new features. For any questions or support, contact [support@wandb.com](mailto:support@wandb.com).
-
-## Reasons for the migration
-
-W&B Registry offers major improvements over the legacy Model Registry:
-
-- **Unified, organization-level experience**: Share and manage curated artifacts across your organization, regardless of teams.
-- **Improved governance**: Use access control, restricted registries, and visibility settings to manage user access.
-- **Enhanced functionality**: New features such as custom registries, better search, audit trails, and automation support help modernize your ML infrastructure.
-
-
-The following table summarizes the key differences between the legacy Model Registry and the new W&B Registry:
-
-| Feature | Legacy W&B Model Registry | W&B Registry |
-| ----- | ----- | ----- |
-| Artifact Visibility | Team-level only - access restricted to team members | Org-level visibility with fine-grained permission controls |
-| Custom Registries |Not supported | Fully supported — create registries for any artifact type |
-| Access Control | Not available | Role-based access (Admin, Member, Viewer) at the registry level |
-| Terminology |“Registered models”: pointers to model versions | “Collections”: pointers to any artifact versions |
-| Registry Scope |Only supports model versioning | Supports models, datasets, custom artifacts, and more |
-| Automations | Registry-level automations | Registry- and collection-level automations supported and copied during migration |
-| Search & Discoverability | Limited search and discoverability | Central search within W&B Registry across all registries in the organization |
-| API Compatibility | Uses `wandb.init.link_model()` and MR-specific patterns | Modern SDK APIs (`link_artifact()`, `use_artifact()`) with auto-redirection |
-| Migration | End-of-life | Automatically migrated and enhanced — data is copied, not deleted |
-
-## Preparing for the migration
-
-- **No action required**: The migration is fully automated and managed by W&B. You do not need to run scripts, update configurations, or move data manually.
-- **Stay informed**: You will receive communications (banners in the W&B App UI) 2 weeks prior to your scheduled migration.
-- **Review permissions**: After the migration, admins should check registry access to ensure alignment with your team’s needs.
-- **Use new paths in future work**: Old code continues to work, W&B recommends using the new W&B Registry paths for new projects.
-
-
-## Migration process
-
-### Temporary write operation pause
-During migration, write operations for your team’s Model Registry will be paused to ensure data consistency for up to one hour. Write operations to the newly created migrated W&B Registry will also be paused during the migration.
-
-### Data migration
-W&B will migrate the following data from the legacy Model Registry to the new W&B Registry:
-
-- Collections
-- Linked artifact versions
-- Version history
-- Aliases, tags, and descriptions
-- Automations (both collection and registry-level)
-- Permissions, including service account roles and protected aliases
-
-Within the W&B App UI, the legacy Model Registry will be replaced with the new W&B Registry. Migrated registries will have the name of your team followed by `mr-migrated`:
-
-```text
--mr-migrated
-```
-
-These registries default to **Restricted** visibility, preserving your existing privacy boundaries. Only the original members of the `` will have access to their respective registries.
-
-
-## After the migration
-
-After the migration completes:
-
-- The legacy Model Registry becomes **read-only**. You can still view and access your data, but no new writes will be allowed.
-- Data in the legacy Model Registry is **copied** to the new W&B Registry, not moved. No data is deleted.
-- Access all your data from the new W&B Registry.
-- Use the new Registry UI for versioning, governance, audit trails, and automation.
-- Continue using your old code.
- - [Existing paths and API calls will automatically redirect to the new W&B Registry.]({{< relref "#code-will-continue-to-work" >}})
- - [Artifact version paths are redirected.]({{< relref "#legacy-paths-will-redirect-to-new-wb-registry-paths" >}})
-- The legacy Model Registry will temporarily remain visible in the UI. W&B will eventually hide the legacy Model Registry.
-- Explore enhanced functionality in the Registry such as:
- - [Organization-level access]({{< relref "/guides/core/registry/create_registry/#visibility-types" >}})
- - [Role-based access control]({{< relref "/guides/core/registry/configure_registry/" >}})
- - [Registry-level lineage tracking]({{< relref "/guides/core/registry/lineage/" >}})
- - [Automations]({{< relref "/guides/core/automations/" >}})
-
-### Code will continue to work
-
-Existing API calls in your code that refer to the legacy Model Registry will automatically redirect to the new W&B Registry. The following API calls will continue to work without any changes:
-
-- `wandb.Api().artifact()`
-- `wandb.run.use_artifact()`
-- `wandb.run.link_artifact()`
-- `wandb.Artifact().link()`
-
-### Legacy paths will redirect to new W&B Registry paths
-
-W&B will automatically redirect legacy Model Registry paths to the new W&B Registry format. This means you can continue using your existing code without needing to refactor paths immediately. Note that automatic redirection only applies to collections that were created in the legacy Model Registry before migration.
-
-For example:
-- If the legacy Model Registry had collection `"my-model"` already present, the link action will redirect successfully
-- If the legacy Model Registry did not have collection `"my-model"`, it will not redirect and will lead to an error
-
-```python
-# This will redirect successfully if "my-model" existed in legacy Model Registry
-run.link_artifact(artifact, "team-name/model-registry/my-model")
-
-# This will fail if "new-model" did not exist in legacy Model Registry
-run.link_artifact(artifact, "team-name/model-registry/new-model")
-```
-
-
-
-To fetch versions from the legacy Model Registry, paths consisted of a team name, a `"model-registry"` string, collection name, and version:
-
-```python
-f"{team-name}/model-registry/{collection-name}:{version}"
-```
-
-W&B will automatically redirect these paths to the new W&B Registry format, which includes the organization name, a `"wandb-registry"` string, the team name, collection name, and version:
-
-```python
-# Redirects to new path
-f"{org-name}/wandb-registry-{team-name}/{collection-name}:{version}"
-```
-
-
-{{% alert title="Python SDK warnings" %}}
-
-A warning error may appear if you continue to use legacy Model Registry paths in your code. The warning will not break your code, but it indicates that you should update your paths to the new W&B Registry format.
-
-Whether a warning appears depends on the version of the W&B Python SDK you are using:
-
-* Users on the latest W&B SDK (`v0.21.0` and above) will see a non-breaking warning in their logs indicating that a redirect has occurred.
-* For older SDK versions, the redirect will still work silently without emitting a warning. Some metadata such as entity or project names may reflect legacy values.
-
-{{% /alert %}}
-
-
-## Frequently asked questions
-
-### How will I know when my org is being migrated?
-
-You will receive advance notice with an in-app banner or direct communication from W&B.
-
-### Will there be downtime?
-
-Write operations to the legacy Model Registry and the new W&B Registry will be paused for a approximately one hour during the migration. All other W&B services will remain available.
-
-### Will this break my code?
-
-No. All legacy Model Registry paths and Python SDK calls will automatically redirect to the new Registry.
-
-### Will my data be deleted?
-
-No. Your data will be copied to the new W&B Registry. The legacy Model Registry becomes read-only and later hidden. No data is removed or lost.
-
-### What if I’m using an older SDK?
-
-Redirects will still work, but you will not see warnings about them. For the best experience, upgrade to the latest version of the W&B SDK.
-
-### Can I rename/modify my migrated registry?
-
-Yes, renaming and other operations such as adding or removing members from a migrated registry are allowed. These registries are simply custom registries underneath, and the redirection will continue working even after migration.
-
-## Questions?
-
-For support or to discuss your migration, contact [support@wandb.com](mailto:support@wandb.com). W&B is committed to helping you transition smoothly to the new W&B Registry.
\ No newline at end of file
diff --git a/content/en/guides/core/registry/organize-with-tags.md b/content/en/guides/core/registry/organize-with-tags.md
deleted file mode 100644
index 51e7d041eb..0000000000
--- a/content/en/guides/core/registry/organize-with-tags.md
+++ /dev/null
@@ -1,320 +0,0 @@
----
-description: Use tags to organize collections or artifact versions within collections.
- You can add, remove, edit tags with the Python SDK or W&B App UI.
-menu:
- default:
- identifier: organize-with-tags
- parent: registry
-title: Organize versions with tags
-weight: 7
----
-
-Create and add tags to organize your collections or artifact versions within your registry. Add, modify, view, or remove tags to a collection or artifact version with the W&B App UI or the W&B Python SDK.
-
-{{% alert title="When to use a tag versus using an alias" %}}
-Use aliases when you need to reference a specific artifact version uniquely. For example, use an alias such as 'production' or 'latest' to ensure that `artifact_name:alias` always points to a single, specific version.
-
-Use tags when you want more flexibility for grouping or searching. Tags are ideal when multiple versions or collections can share the same label, and you don’t need the guarantee that only one version is associated with a specific identifier.
-{{% /alert %}}
-
-
-## Add a tag to a collection
-
-Use the W&B App UI or Python SDK to add a tag to a collection:
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" %}}
-
-Use the W&B App UI to add a tag to a collection:
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Click **View details** next to the name of a collection
-4. Within the collection card, click on the plus icon (**+**) next to the **Tags** field and type in the name of the tag
-5. Press **Enter** on your keyboard
-
-{{< img src="/images/registry/add_tag_collection.gif" alt="Adding tags to a Registry collection" >}}
-
-{{% /tab %}}
-{{% tab header="Python SDK" %}}
-
-```python
-import wandb
-
-COLLECTION_TYPE = ""
-ORG_NAME = ""
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-
-full_name = f"{ORG_NAME}/wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
-
-collection = wandb.Api().artifact_collection(
- type_name = COLLECTION_TYPE,
- name = full_name
- )
-
-collection.tags = ["your-tag"]
-collection.save()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-## Update tags that belong to a collection
-
-Update a tag programmatically by reassigning or by mutating the `tags` attribute. W&B recommends, and it is good Python practice, that you reassign the `tags` attribute instead of in-place mutation.
-
-For example, the proceeding code snippet shows common ways to update a list with reassignment. For brevity, we continue the code example from the [Add a tag to a collection section]({{< relref "#add-a-tag-to-a-collection" >}}):
-
-```python
-collection.tags = [*collection.tags, "new-tag", "other-tag"]
-collection.tags = collection.tags + ["new-tag", "other-tag"]
-
-collection.tags = set(collection.tags) - set(tags_to_delete)
-collection.tags = [] # deletes all tags
-```
-
-The following code snippet shows how you can use in-place mutation to update tags that belong to an artifact version:
-
-```python
-collection.tags += ["new-tag", "other-tag"]
-collection.tags.append("new-tag")
-
-collection.tags.extend(["new-tag", "other-tag"])
-collection.tags[:] = ["new-tag", "other-tag"]
-collection.tags.remove("existing-tag")
-collection.tags.pop()
-collection.tags.clear()
-```
-
-## View tags that belong to a collection
-
-Use the W&B App UI to view tags added to a collection:
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Click **View details** next to the name of a collection
-
-If a collection has one or more tags, you can view those tags within the collection card next to the **Tags** field.
-
-{{< img src="/images/registry/tag_collection_selected.png" alt="Registry collection with selected tags" >}}
-
-Tags added to a collection also appear next to the name of that collection.
-
-For example, in the proceeding image, a tag called "tag1" was added to the "zoo-dataset-tensors" collection.
-
-{{< img src="/images/registry/tag_collection.png" alt="Tag management" >}}
-
-
-## Remove a tag from a collection
-
-Use the W&B App UI to remove a tag from a collection:
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Click **View details** next to the name of a collection
-4. Within the collection card, hover your mouse over the name of the tag you want to remove
-5. Click on the cancel button (**X** icon)
-
-## Add a tag to an artifact version
-
-Add a tag to an artifact version linked to a collection with the W&B App UI or with the Python SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" %}}
-1. Navigate to the W&B Registry at https://wandb.ai/registry
-2. Click on a registry card
-3. Click **View details** next to the name of the collection you want to add a tag to
-4. Scroll down to **Versions**
-5. Click **View** next to an artifact version
-6. Within the **Version** tab, click on the plus icon (**+**) next to the **Tags** field and type in the name of the tag
-7. Press **Enter** on your keyboard
-
-{{< img src="/images/registry/add_tag_linked_artifact_version.gif" alt="Adding tags to artifact versions" >}}
-
-{{% /tab %}}
-{{% tab header="Python SDK" %}}
-Fetch the artifact version you want to add or update a tag to. Once you have the artifact version, you can access the artifact object's `tag` attribute to add or modify tags to that artifact. Pass in one or more tags as list to the artifacts `tag` attribute.
-
-Like other artifacts, you can fetch an artifact from W&B without creating a run or you can create a run and fetch the artifact within that run. In either case, ensure to call the artifact object's `save` method to update the artifact on the W&B servers.
-
-Copy and paste an appropriate code cells below to add or modify an artifact version's tag. Replace the values in `<>` with your own.
-
-
-The proceeding code snippet shows how to fetch an artifact and add a tag without creating a new run:
-```python title="Add a tag to an artifact version without creating a new run"
-import wandb
-
-ARTIFACT_TYPE = ""
-ORG_NAME = ""
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-VERSION = ""
-
-artifact_name = f"{ORG_NAME}/wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{VERSION}"
-
-artifact = wandb.Api().artifact(name = artifact_name, type = ARTIFACT_TYPE)
-artifact.tags = ["tag2"] # Provide one or more tags in a list
-artifact.save()
-```
-
-
-The proceeding code snippet shows how to fetch an artifact and add a tag by creating a new run:
-
-```python title="Add a tag to an artifact version during a run"
-import wandb
-
-ORG_NAME = ""
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-VERSION = ""
-
-run = wandb.init(entity = "", project="")
-
-artifact_name = f"{ORG_NAME}/wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{VERSION}"
-
-artifact = run.use_artifact(artifact_or_name = artifact_name)
-artifact.tags = ["tag2"] # Provide one or more tags in a list
-artifact.save()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-## Update tags that belong to an artifact version
-
-
-Update a tag programmatically by reassigning or by mutating the `tags` attribute. W&B recommends, and it is good Python practice, that you reassign the `tags` attribute instead of in-place mutation.
-
-For example, the proceeding code snippet shows common ways to update a list with reassignment. For brevity, we continue the code example from the [Add a tag to an artifact version section]({{< relref "#add-a-tag-to-an-artifact-version" >}}):
-
-```python
-artifact.tags = [*artifact.tags, "new-tag", "other-tag"]
-artifact.tags = artifact.tags + ["new-tag", "other-tag"]
-
-artifact.tags = set(artifact.tags) - set(tags_to_delete)
-artifact.tags = [] # deletes all tags
-```
-
-The following code snippet shows how you can use in-place mutation to update tags that belong to an artifact version:
-
-```python
-artifact.tags += ["new-tag", "other-tag"]
-artifact.tags.append("new-tag")
-
-artifact.tags.extend(["new-tag", "other-tag"])
-artifact.tags[:] = ["new-tag", "other-tag"]
-artifact.tags.remove("existing-tag")
-artifact.tags.pop()
-artifact.tags.clear()
-```
-
-
-## View tags that belong to an artifact version
-
-View tags that belong to an artifact version that is linked to a registry with the W&B App UI or with the Python SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" %}}
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Click **View details** next to the name of the collection you want to add a tag to
-4. Scroll down to **Versions** section
-
-If an artifact version has one or more tags, you can view those tags within the **Tags** column.
-
-{{< img src="/images/registry/tag_artifact_version.png" alt="Artifact version with tags" >}}
-
-{{% /tab %}}
-{{% tab header="Python SDK" %}}
-
-Fetch the artifact version to view its tags. Once you have the artifact version, you can view tags that belong to that artifact by viewing the artifact object's `tag` attribute.
-
-Like other artifacts, you can fetch an artifact from W&B without creating a run or you can create a run and fetch the artifact within that run.
-
-Copy and paste an appropriate code cells below to add or modify an artifact version's tag. Replace the values in `<>` with your own.
-
-The proceeding code snippet shows how to fetch and view an artifact version's tags without creating a new run:
-
-```python title="Add a tag to an artifact version without creating a new run"
-import wandb
-
-ARTIFACT_TYPE = ""
-ORG_NAME = ""
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-VERSION = ""
-
-artifact_name = f"{ORG_NAME}/wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{VERSION}"
-
-artifact = wandb.Api().artifact(name = artifact_name, type = artifact_type)
-print(artifact.tags)
-```
-
-
-The proceeding code snippet shows how to fetch and view artifact version's tags by creating a new run:
-
-```python title="Add a tag to an artifact version during a run"
-import wandb
-
-ORG_NAME = ""
-REGISTRY_NAME = ""
-COLLECTION_NAME = ""
-VERSION = ""
-
-run = wandb.init(entity = "", project="")
-
-artifact_name = f"{ORG_NAME}/wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{VERSION}"
-
-artifact = run.use_artifact(artifact_or_name = artifact_name)
-print(artifact.tags)
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-## Remove a tag from an artifact version
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Click **View details** next to the name of the collection you want to add a tag to
-4. Scroll down to **Versions**
-5. Click **View** next to an artifact version
-6. Within the **Version** tab, hover your mouse over the name of the tag
-7. Click on the cancel button (**X** icon)
-
-## Search existing tags
-
-Use the W&B App UI to search existing tags in collections and artifact versions:
-
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry).
-2. Click on a registry card
-3. Within the search bar, type in the name of a tag.
-
-{{< img src="/images/registry/search_tags.gif" alt="Tag-based search" >}}
-
-
-## Find artifact versions with a specific tag
-
-Use the W&B Python SDK to find artifact versions that have a set of tags:
-
-```python
-import wandb
-
-api = wandb.Api()
-tagged_artifact_versions = api.artifacts(
- type_name = "",
- name = "",
- tags = ["", ""]
-)
-
-for artifact_version in tagged_artifact_versions:
- print(artifact_version.tags)
-```
\ No newline at end of file
diff --git a/content/en/guides/core/registry/registry_cards.md b/content/en/guides/core/registry/registry_cards.md
deleted file mode 100644
index 890baf2e81..0000000000
--- a/content/en/guides/core/registry/registry_cards.md
+++ /dev/null
@@ -1,78 +0,0 @@
----
-menu:
- default:
- identifier: registry_cards
- parent: registry
-title: Annotate collections
-weight: 8
----
-
-Add human-friendly text to your collections to help users understand the purpose of the collection and the artifacts it contains.
-
-
-Depending on the collection, you might want to include information about the training data, model architecture, task, license, references, and deployment. The proceeding lists some topics worth documenting in a collection:
-
-
-
-W&B recommends including at minimum these details:
-* **Summary**: The purpose of the collection. The machine learning framework used for the machine learning experiment.
-* **License**: The legal terms and permissions associated with the use of the machine learning model. It helps model users understand the legal framework under which they can utilize the model. Common licenses include Apache 2.0, MIT, and GPL.
-* **References**: Citations or references to relevant research papers, datasets, or external resources.
-
-If your collection contains training data, consider including these additional details:
-* **Training data**: Describe the training data used
-* **Processing**: Processing done on the training data set.
-* **Data storage**: Where is that data stored and how to access it.
-
-
-If your collection contains a machine learning model, consider including these additional details:
-* **Architecture**: Information about the model architecture, layers, and any specific design choices.
-* **Task**: The specific type of task or problem that the machine that the collection model is designed to perform. It's a categorization of the model's intended capability.
-* **Deserialize the model**: Provide information on how someone on your team can load the model into memory.
-* **Deployment**: Details on how and where the model is deployed and guidance on how the model is integrated into other enterprise systems, such as a workflow orchestration platforms.
-
-
-## Add a description to a collection
-
-Interactively or programmatically add a description to a collection with the W&B Registry UI or Python SDK.
-
-{{< tabpane text=true >}}
- {{% tab header="W&B Registry UI" %}}
-1. Navigate to the [W&B Registry App](https://wandb.ai/registry/).
-2. Click on a collection.
-3. Select **View details** next to the name of the collection.
-4. Within the **Description** field, provide information about your collection. Format text within with [Markdown markup language](https://www.markdownguide.org/).
-
- {{% /tab %}}
- {{% tab header="Python SDK" %}}
-
-Use the [`wandb.Api().artifact_collection()`]({{< relref "/ref/python/public-api/api.md#artifact_collection" >}}) method to access a collection's description. Use the returned object's `description` property to add, or update, a description to the collection.
-
-Specify the collection's type for the `type_name` parameter and the collection's full name for the `name` parameter. A collection's name consists of the prefix “wandb-registry”, the name of the registry, and the name of the collection separated by a forward slashes:
-
-```text
-wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}
-```
-
-Copy and paste the proceeding code snippet into your Python script or notebook. Replace values enclosed in angle brackets (`<>`) with your own.
-
-```python
-import wandb
-
-api = wandb.Api()
-
-collection = api.artifact_collection(
- type_name = "",
- name = ""
- )
-
-
-collection.description = "This is a description."
-collection.save()
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-For example, the proceeding image shows a collection that documents a model's architecture, intended use, performance information and more.
-
-{{< img src="/images/registry/registry_card.png" alt="Collection card" >}}
diff --git a/content/en/guides/core/registry/registry_types.md b/content/en/guides/core/registry/registry_types.md
deleted file mode 100644
index fcfb417eb8..0000000000
--- a/content/en/guides/core/registry/registry_types.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-menu:
- default:
- identifier: registry_types
- parent: registry
-title: Registry types
-weight: 1
----
-
-W&B supports two types of registries: [Core registries]({{< relref "#core-registry" >}}) and [Custom registries]({{< relref "#custom-registry" >}}).
-
-## Core registry
-A core registry is a template for specific use cases: **Models** and **Datasets**.
-
-By default, the **Models** registry is configured to accept `"model"` artifact types and the **Dataset** registry is configured to accept `"dataset"` artifact types. An admin can add additional accepted artifact types.
-
-
-
-{{< img src="/images/registry/core_registry_example.png" alt="Core registry" >}}
-
-The preceding image shows the **Models** and the **Dataset** core registry along with a custom registry called **Fine_Tuned_Models** in the W&B Registry App UI.
-
-A core registry has [organization visibility]({{< relref "./configure_registry.md#registry-visibility-types" >}}). A registry admin can not change the visibility of a core registry.
-
-## Custom registry
-Custom registries are not restricted to `"model"` artifact types or `"dataset"` artifact types.
-
-You can create a custom registry for each step in your machine learning pipeline, from initial data collection to final model deployment.
-
-For example, you might create a registry called "Benchmark_Datasets" for organizing curated datasets to evaluate the performance of trained models. Within this registry, you might have a collection called "User_Query_Insurance_Answer_Test_Data" that contains a set of user questions and corresponding expert-validated answers that the model has never seen during training.
-
-{{< img src="/images/registry/custom_registry_example.png" alt="Custom registry example" >}}
-
-A custom registry can have either [organization or restricted visibility]({{< relref "./configure_registry.md#registry-visibility-types" >}}). A registry admin can change the visibility of a custom registry from organization to restricted. However, the registry admin can not change a custom registry's visibility from restricted to organizational visibility.
-
-For information on how to create a custom registry, see [Create a custom registry]({{< relref "./create_collection.md" >}}).
-
-
-## Summary
-The proceeding table summarizes the differences between core and custom registries:
-
-| | Core | Custom|
-| -------------- | ----- | ----- |
-| Visibility | Organizational visibility only. Visibility can not be altered. | Either organization or restricted. Visibility can be altered from organization to restricted visibility.|
-| Metadata | Preconfigured and not editable by users. | Users can edit. |
-| Artifact types | Preconfigured and accepted artifact types cannot be removed. Users can add additional accepted artifact types. | Admin can define accepted types. |
-| Customization | Can add additional types to the existing list.| Edit registry name, description, visibility, and accepted artifact types.|
\ No newline at end of file
diff --git a/content/en/guides/core/registry/search_registry.md b/content/en/guides/core/registry/search_registry.md
deleted file mode 100644
index 2206328aa1..0000000000
--- a/content/en/guides/core/registry/search_registry.md
+++ /dev/null
@@ -1,151 +0,0 @@
----
-menu:
- default:
- identifier: search_registry
- parent: registry
-title: Find registry items
-weight: 7
----
-
-Use the [global search bar in the W&B Registry App]({{< relref "./search_registry.md#search-for-registry-items" >}}) to find a registry, collection, artifact version tag, collection tag, or alias. You can use MongoDB-style queries to [filter registries, collections, and artifact versions]({{< relref "./search_registry.md#query-registry-items-with-mongodb-style-queries" >}}) based on specific criteria using the W&B Python SDK.
-
-
-Only items that you have permission to view appear in the search results.
-
-## Search for registry items
-
-To search for a registry item:
-
-1. Navigate to the W&B Registry App.
-2. Specify the search term in the search bar at the top of the page. Press Enter to search.
-
-Search results appear below the search bar if the term you specify matches an existing registry, collection name, artifact version tag, collection tag, or alias.
-
-{{< img src="/images/registry/search_registry.gif" alt="Searching within a Registry" >}}
-
-## Query registry items with MongoDB-style queries
-
-Use the [`wandb.Api().registries()`]({{< relref "/ref/python/public-api/api.md#registries" >}}) and [query predicates](https://www.mongodb.com/docs/manual/reference/glossary/#std-term-query-predicate) to filter registries, collections, and artifact versions based on one or more [MongoDB-style queries](https://www.mongodb.com/docs/compass/current/query/filter/).
-
-The following table lists query names you can use based on the type of item you want to filter:
-
-| | query name |
-| ----- | ----- |
-| registries | `name`, `description`, `created_at`, `updated_at` |
-| collections | `name`, `tag`, `description`, `created_at`, `updated_at` |
-| versions | `tag`, `alias`, `created_at`, `updated_at`, `metadata` |
-
-The proceeding code examples demonstrate some common search scenarios.
-
-To use the `wandb.Api().registries()` method, first import the W&B Python SDK ([`wandb`]({{< relref "/ref/python/_index.md" >}})) library:
-```python
-import wandb
-
-# (Optional) Create an instance of the wandb.Api() class for readability
-api = wandb.Api()
-```
-
-Filter all registries that contain the string `model`:
-
-```python
-# Filter all registries that contain the string `model`
-registry_filters = {
- "name": {"$regex": "model"}
-}
-
-# Returns an iterable of all registries that match the filters
-registries = api.registries(filter=registry_filters)
-```
-
-Filter all collections, independent of registry, that contains the string `yolo` in the collection name:
-
-```python
-# Filter all collections, independent of registry, that
-# contains the string `yolo` in the collection name
-collection_filters = {
- "name": {"$regex": "yolo"}
-}
-
-# Returns an iterable of all collections that match the filters
-collections = api.registries().collections(filter=collection_filters)
-```
-
-Filter all collections, independent of registry, that contains the string `yolo` in the collection name and possesses `cnn` as a tag:
-
-```python
-# Filter all collections, independent of registry, that contains the
-# string `yolo` in the collection name and possesses `cnn` as a tag
-collection_filters = {
- "name": {"$regex": "yolo"},
- "tag": "cnn"
-}
-
-# Returns an iterable of all collections that match the filters
-collections = api.registries().collections(filter=collection_filters)
-```
-
-Find all artifact versions that contains the string `model` and has either the tag `image-classification` or an `latest` alias:
-
-```python
-# Find all artifact versions that contains the string `model` and
-# has either the tag `image-classification` or an `latest` alias
-registry_filters = {
- "name": {"$regex": "model"}
-}
-
-# Use logical $or operator to filter artifact versions
-version_filters = {
- "$or": [
- {"tag": "image-classification"},
- {"alias": "production"}
- ]
-}
-
-# Returns an iterable of all artifact versions that match the filters
-artifacts = api.registries(filter=registry_filters).collections().versions(filter=version_filters)
-```
-
-See the MongoDB documentation for more information on [logical query operators](https://www.mongodb.com/docs/manual/reference/operator/query-logical/).
-
-Each item in the `artifacts` iterable in the previous code snippet is an instance of the `Artifact` class. This means that you can access each artifact's attributes, such as `name`, `collection`, `aliases`, `tags`, `created_at`, and more:
-
-```python
-for art in artifacts:
- print(f"artifact name: {art.name}")
- print(f"collection artifact belongs to: { art.collection.name}")
- print(f"artifact aliases: {art.aliases}")
- print(f"tags attached to artifact: {art.tags}")
- print(f"artifact created at: {art.created_at}\n")
-```
-For a complete list of an artifact object's attributes, see the [Artifacts Class]({{< relref "/ref/python/experiments/artifact/_index.md" >}}) in the API Reference docs.
-
-
-Filter all artifact versions, independent of registry or collection, created between 2024-01-08 and 2025-03-04 at 13:10 UTC:
-
-```python
-# Find all artifact versions created between 2024-01-08 and 2025-03-04 at 13:10 UTC.
-
-artifact_filters = {
- "alias": "latest",
- "created_at" : {"$gte": "2024-01-08", "$lte": "2025-03-04 13:10:00"},
-}
-
-# Returns an iterable of all artifact versions that match the filters
-artifacts = api.registries().collections().versions(filter=artifact_filters)
-```
-
-Specify the date and time in the format `YYYY-MM-DD HH:MM:SS`. You can omit the hours, minutes, and seconds if you want to filter by date only.
-
-See the MongoDB documentation for more information on [query comparisons](https://www.mongodb.com/docs/manual/reference/operator/query-comparison/).
-
-## Prioritize registries with stars
-
-Add a "star" to label important or frequently used registries. Starred registries appear in the **My starred registries** section on the Registry landing page.
-
-To add a star to a registry:
-
-1. Navigate to the W&B Registry.
-2. Click the star icon next to the name of the registry.
-
-
-
diff --git a/content/en/guides/core/reports/_index.md b/content/en/guides/core/reports/_index.md
deleted file mode 100644
index 86cfac1b8b..0000000000
--- a/content/en/guides/core/reports/_index.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-description: Project management and collaboration tools for machine learning projects
-menu:
- default:
- identifier: reports
- parent: core
-title: Reports
-weight: 3
-url: guides/reports
-cascade:
-- url: guides/reports/:filename
----
-
-
-{{< cta-button productLink="https://wandb.ai/stacey/deep-drive/reports/The-View-from-the-Driver-s-Seat--Vmlldzo1MTg5NQ?utm_source=fully_connected&utm_medium=blog&utm_campaign=view+from+the+drivers+seat" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Report_API_Quickstart.ipynb" >}}
-
-Use W&B Reports to:
-- Organize Runs.
-- Embed and automate visualizations.
-- Describe your findings.
-- Share updates with collaborators, either as a LaTeX zip file a PDF.
-
-
-
-The following image shows a section of a report created from metrics that were logged to W&B over the course of training.
-
-{{< img src="/images/reports/safe-lite-benchmark-with-comments.png" alt="W&B report with benchmark results" max-width="90%" >}}
-
-View the report where the above image was taken from [here](https://wandb.ai/stacey/saferlife/reports/SafeLife-Benchmark-Experiments--Vmlldzo0NjE4MzM).
-
-## How it works
-Create a collaborative report with a few clicks.
-
-1. Navigate to your W&B project workspace in the W&B App.
-2. Click the **Create report** button in the upper right corner of your workspace.
-
-{{< img src="/images/reports/create_a_report_button.png" alt="Create report button" max-width="90%">}}
-
-3. A modal titled **Create Report** will appear. Select the charts and panels you want to add to your report. (You can add or remove charts and panels later).
-4. Click **Create report**.
-5. Edit the report to your desired state.
-6. Click **Publish to project**.
-7. Click the **Share** button to share your report with collaborators.
-
-See the [Create a report]({{< relref "./create-a-report.md" >}}) page for more information on how to create reports interactively an programmatically with the W&B Python SDK.
-
-## How to get started
-Depending on your use case, explore the following resources to get started with W&B Reports:
-
-* Check out our [video demonstration](https://www.youtube.com/watch?v=2xeJIv_K_eI) to get an overview of W&B Reports.
-* Explore the [Reports gallery]({{< relref "./reports-gallery.md" >}}) for examples of live reports.
-* Try the [Programmatic Workspaces]({{< relref "/tutorials/workspaces.md" >}}) tutorial to learn how to create and customize your workspace.
-* Read curated Reports in [W&B Fully Connected](https://wandb.me/fc).
-
-## Recommended practices and tips
-
-For best practices and tips for Experiments and logging, see [Best Practices: Reports](https://wandb.ai/wandb/pytorch-lightning-e2e/reports/W-B-Best-Practices-Guide--VmlldzozNTU1ODY1#reports).
\ No newline at end of file
diff --git a/content/en/guides/core/reports/clone-and-export-reports.md b/content/en/guides/core/reports/clone-and-export-reports.md
deleted file mode 100644
index 14bf2afad5..0000000000
--- a/content/en/guides/core/reports/clone-and-export-reports.md
+++ /dev/null
@@ -1,63 +0,0 @@
----
-description: Export a W&B Report as a PDF or LaTeX.
-menu:
- default:
- identifier: clone-and-export-reports
- parent: reports
-title: Clone and export reports
-weight: 40
----
-
-{{% alert %}}
-W&B Report and Workspace API is in Public Preview.
-{{% /alert %}}
-
-## Export reports
-
-Export a report as a PDF or LaTeX. Within your report, select the kebab icon to expand the dropdown menu. Choose **Download and** select either PDF or LaTeX output format.
-
-## Cloning reports
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" value="app" %}}
-Within your report, select the kebab icon to expand the dropdown menu. Choose the **Clone this report** button. Pick a destination for your cloned report in the modal. Choose **Clone report**.
-
-{{< img src="/images/reports/clone_reports.gif" alt="Cloning reports" >}}
-
-Clone a report to reuse a project's template and format. Cloned projects are visible to your team if you clone a project within the team's account. Projects cloned within an individual's account are only visible to that user.
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api"%}}
-
-Load a Report from a URL to use it as a template.
-
-```python
-report = wr.Report(
- project=PROJECT, title="Quickstart Report", description="That was easy!"
-) # Create
-report.save() # Save
-new_report = wr.Report.from_url(report.url) # Load
-```
-
-Edit the content within `new_report.blocks`.
-
-```python
-pg = wr.PanelGrid(
- runsets=[
- wr.Runset(ENTITY, PROJECT, "First Run Set"),
- wr.Runset(ENTITY, PROJECT, "Elephants Only!", query="elephant"),
- ],
- panels=[
- wr.LinePlot(x="Step", y=["val_acc"], smoothing_factor=0.8),
- wr.BarPlot(metrics=["acc"]),
- wr.MediaBrowser(media_keys="img", num_columns=1),
- wr.RunComparer(diff_only="split", layout={"w": 24, "h": 9}),
- ],
-)
-new_report.blocks = (
- report.blocks[:1] + [wr.H1("Panel Grid Example"), pg] + report.blocks[1:]
-)
-new_report.save()
-```
-{{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/reports/collaborate-on-reports.md b/content/en/guides/core/reports/collaborate-on-reports.md
deleted file mode 100644
index 77de631c29..0000000000
--- a/content/en/guides/core/reports/collaborate-on-reports.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-description: Collaborate and share W&B Reports with peers, co-workers, and your team.
-menu:
- default:
- identifier: collaborate-on-reports
- parent: reports
-title: Collaborate on reports
-weight: 30
----
-
-This page describes various ways to collaborate on reports with your team.
-
-## Share a report
-When viewing a report, click **Share**, then:
-- To share a link to the report with an email address or a username, click **Invite**. Enter an email address or username, select **Can view** or **Can edit**, then click **Invite**. If you share by email, the email address does not need to be a member of your organization or team.
-- To generate a sharing link instead, click **Share**. Adjust the permissions for the link, then click **Copy report link**. Share the link with the member.
-
-When viewing the report, click a panel to open it in full-screen mode. If you copy the URL from the browser and share it with another user, when they access the link the panel will open directly in full-screen mode.
-
-## Edit a report
-When any team member clicks the **Edit** button to begin editing the report, a draft is automatically saved. Select **Save to report** to publish your changes.
-
-If an edit conflict occurs, such as when two team members edit the report at once, a warning notification helps you to resolve any conflicts.
-
-{{< img src="/images/reports/share-report.gif" alt="Report sharing modal for a report in a 'Public' project" max-width="90%">}}
-
-## Comment on reports
-Click **Comment** to leave a comment on a report.
-
-To comment directly on a panel, hover over the panel, then click the comment button, which looks like a speech bubble.
-
-{{< img src="/images/reports/demo_comment_on_panels_in_reports.gif" alt="Adding a comment to a panel" >}}
-
-## Star a report
-If your team has a large number of reports, click **Star** at the top of a report to add it to your favorites. When viewing your team's list of reports, click the star in a report's row to add it to your favorites. Starred reports appear at the top of the list.
-
-From the list of reports, you can see how many members have starred each report to gauge its relative popularity.
\ No newline at end of file
diff --git a/content/en/guides/core/reports/create-a-report.md b/content/en/guides/core/reports/create-a-report.md
deleted file mode 100644
index 9e7cc2278d..0000000000
--- a/content/en/guides/core/reports/create-a-report.md
+++ /dev/null
@@ -1,66 +0,0 @@
----
-description: Create a W&B Report with the W&B App or programmatically.
-menu:
- default:
- identifier: create-a-report
- parent: reports
-title: Create a report
-weight: 10
----
-
-{{% alert %}}
-W&B Report and Workspace API is in Public Preview.
-{{% /alert %}}
-
-Select a tab below to learn how to create a report in the W&B App or programmatically with the W&B Report and Workspace API.
-
-See this [Google Colab](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Report_API_Quickstart.ipynb) for an example on how to programmatically create a report.
-
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" value="app" %}}
-1. Navigate to your project workspace in the W&B App.
-2. Click **Create report** in the upper right corner of your workspace.
-
- {{< img src="/images/reports/create_a_report_button.png" alt="Create report button" >}}
-
-3. A modal will appear. Select the charts you would like to start with. You can add or delete charts later from the report interface.
-
- {{< img src="/images/reports/create_a_report_modal.png" alt="Create report modal" >}}
-
-4. Select the **Filter run sets** option to prevent new runs from being added to your report. You can toggle this option on or off. Once you click **Create report,** a draft report will be available in the report tab to continue working on.
-{{% /tab %}}
-
-{{% tab header="Report tab" value="reporttab"%}}
-1. Navigate to your project workspace in the W&B App.
-2. Select to the **Reports** tab (clipboard image) in your project.
-3. Select the **Create Report** button on the report page.
-
- {{< img src="/images/reports/create_report_button.png" alt="Create report button" >}}
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api"%}}
-Create a report programmatically:
-
-1. Install W&B SDK (`wandb`) and Report and Workspace API (`wandb-workspaces`):
- ```bash
- pip install wandb wandb-workspaces
- ```
-2. Next, import workspaces
- ```python
- import wandb
- import wandb_workspaces.reports.v2 as wr
- ```
-3. Create a report with `wandb_workspaces.reports.v2.Report`. Create a report instance with the Report Class Public API ([`wandb.apis.reports`]({{< relref "/ref/python/public-api/api.md#reports" >}})). Specify a name for the project.
- ```python
- report = wr.Report(project="report_standard")
- ```
-
-4. Save the report. Reports are not uploaded to the W&B server until you call the .`save()` method:
- ```python
- report.save()
- ```
-
-For information on how to edit a report interactively with the App UI or programmatically, see [Edit a report]({{< relref "/guides/core/reports/edit-a-report" >}}).
-{{% /tab %}}
-{{< /tabpane >}}
diff --git a/content/en/guides/core/reports/cross-project-reports.md b/content/en/guides/core/reports/cross-project-reports.md
deleted file mode 100644
index 5a9cc7dd2a..0000000000
--- a/content/en/guides/core/reports/cross-project-reports.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-description: Compare runs from two different projects with cross-project reports.
-menu:
- default:
- identifier: cross-project-reports
- parent: reports
-title: Compare runs across projects
-weight: 60
----
-{{% alert %}}
-Watch a [video demonstrating comparing runs across projects](https://www.youtube.com/watch?v=uD4if_nGrs4) (2 min).
-{{% /alert %}}
-
-
-Compare runs from two different projects with cross-project reports. Use the project selector in the run set table to pick a project.
-
-{{< img src="/images/reports/howto_pick_a_different_project_to_draw_runs_from.gif" alt="Compare runs across different projects" >}}
-
-The visualizations in the section pull columns from the first active runset. Make sure that the first run set checked in the section has that column available if you do not see the metric you are looking for in the line plot.
-
-This feature supports history data on time series lines, but we don't support pulling different summary metrics from different projects. In other words, you can not create a scatter plot from columns that are only logged in another project.
-
-If you need to compare runs from two projects and the columns are not working, add a tag to the runs in one project and then move those runs to the other project. You can still filter only the runs from each project, but the report includes all the columns for both sets of runs.
-
-## View-only report links
-
-Share a view-only link to a report that is in a private project or team project.
-
-{{< img src="/images/reports/magic-links.gif" alt="View-only report links" >}}
-
-View-only report links add a secret access token to the URL, so anyone who opens the link can view the page. Anyone can use the magic link to view the report without logging in first. For customers on [W&B Local]({{< relref "/guides/hosting/" >}}) private cloud installations, these links remain behind your firewall, so only members of your team with access to your private instance _and_ access to the view-only link can view the report.
-
-In **view-only mode**, someone who is not logged in can see the charts and mouse over to see tooltips of values, zoom in and out on charts, and scroll through columns in the table. When in view mode, they cannot create new charts or new table queries to explore the data. View-only visitors to the report link won't be able to click a run to get to the run page. Also, the view-only visitors would not be able to see the share modal but instead would see a tooltip on hover which says: `Sharing not available for view only access`.
-
-{{% alert color="info" %}}
-The magic links are only available for “Private” and “Team” projects. For “Public” (anyone can view) or “Open” (anyone can view and contribute runs) projects, the links can't turn on/off because this project is public implying that it is already available to anyone with the link.
-{{% /alert %}}
-
-## Send a graph to a report
-
-Send a graph from your workspace to a report to keep track of your progress. Click the dropdown menu on the chart or panel you'd like to copy to a report and click **Add to report** to select the destination report.
\ No newline at end of file
diff --git a/content/en/guides/core/reports/edit-a-report.md b/content/en/guides/core/reports/edit-a-report.md
deleted file mode 100644
index f607d3aa11..0000000000
--- a/content/en/guides/core/reports/edit-a-report.md
+++ /dev/null
@@ -1,722 +0,0 @@
----
-description: Edit a report interactively with the App UI or programmatically with
- the W&B SDK.
-menu:
- default:
- identifier: edit-a-report
- parent: reports
-title: Edit a report
-weight: 20
----
-
-{{% alert %}}
-W&B Report and Workspace API is in Public Preview.
-{{% /alert %}}
-
-Edit a report interactively with the App UI or programmatically with the W&B SDK.
-
-Reports consist of _blocks_. Blocks make up the body of a report. Within these blocks you can add text, images, embedded visualizations, plots from experiments and run, and panels grids.
-
-_Panel grids_ are a specific type of block that hold panels and _run sets_. Run sets are a collection of runs logged to a project in W&B. Panels are visualizations of run set data.
-
-
-{{% alert %}}
-Check out the [Programmatic workspaces tutorial]({{< relref "/tutorials/workspaces.md" >}}) for a step by step example on how create and customize a saved workspace view.
-{{% /alert %}}
-
-{{% alert %}}
-Verify that you have the W&B Report and Workspace API `wandb-workspaces` installed in addition to the W&B Python SDK if you want to programmatically edit a report:
-
-```pip
-pip install wandb wandb-workspaces
-```
-{{% /alert %}}
-
-## Add plots
-
-Each panel grid has a set of run sets and a set of panels. The run sets at the bottom of the section control what data shows up on the panels in the grid. Create a new panel grid if you want to add charts that pull data from a different set of runs.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" value="app" %}}
-
-Enter a forward slash (`/`) in the report to display a dropdown menu. Select **Add panel** to add a panel. You can add any panel that is supported by W&B, including a line plot, scatter plot or parallel coordinates chart.
-
-{{< img src="/images/reports/demo_report_add_panel_grid.gif" alt="Add charts to a report" >}}
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api"%}}
-Add plots to a report programmatically with the SDK. Pass a list of one or more plot or chart objects to the `panels` parameter in the `PanelGrid` Public API Class. Create a plot or chart object with its associated Python Class.
-
-
-The proceeding examples demonstrates how to create a line plot and scatter plot.
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(
- project="report-editing",
- title="An amazing title",
- description="A descriptive description.",
-)
-
-blocks = [
- wr.PanelGrid(
- panels=[
- wr.LinePlot(x="time", y="velocity"),
- wr.ScatterPlot(x="time", y="acceleration"),
- ]
- )
-]
-
-report.blocks = blocks
-report.save()
-```
-
-For more information about available plots and charts you can add to a report programmatically, see `wr.panels`.
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-## Add run sets
-
-Add run sets from projects interactively with the App UI or the W&B SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="W&B App" value="app" %}}
-
-Enter a forward slash (`/`) in the report to display a dropdown menu. From the dropdown, choose **Panel Grid**. This will automatically import the run set from the project the report was created from.
-
-If you import a panel into a report, run names are inherited from the project. In the report, you can optionally [rename a run]({{< relref "/guides/models/track/runs/#rename-a-run" >}}) to give the reader more context. The run is renamed only in the individual panel. If you clone the panel in the same report, the run is also renamed in the cloned panel.
-
-1. In the report, click the pencil icon to open the report editor.
-1. In the run set, find the run to rename. Hover over the report name, click the three vertical dots. Select one of the following choices, then submit the form.
-
- - **Rename run for project**: rename the run across the entire project. To generate a new random name, leave the field blank.
- - **Rename run for panel grid** rename the run only in the report, preserving the existing name in other contexts. Generating a new random name is not supported.
-
-1. Click **Publish report**.
-
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api"%}}
-
-Add run sets from projects with the `wr.Runset()` and `wr.PanelGrid` Classes. The proceeding procedure describes how to add a runset:
-
-1. Create a `wr.Runset()` object instance. Provide the name of the project that contains the run sets for the project parameter and the entity that owns the project for the entity parameter.
-2. Create a `wr.PanelGrid()` object instance. Pass a list of one or more runset objects to the `run sets` parameter.
-3. Store one or more `wr.PanelGrid()` object instances in a list.
-4. Update the report instance blocks attribute with the list of panel grid instances.
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(
- project="report-editing",
- title="An amazing title",
- description="A descriptive description.",
-)
-
-panel_grids = wr.PanelGrid(
- runsets=[wr.RunSet(project="", entity="")]
-)
-
-report.blocks = [panel_grids]
-report.save()
-```
-
-You can optionally add runsets and panels with one call to the SDK:
-
-```python
-import wandb
-
-report = wr.Report(
- project="report-editing",
- title="An amazing title",
- description="A descriptive description.",
-)
-
-panel_grids = wr.PanelGrid(
- panels=[
- wr.LinePlot(
- title="line title",
- x="x",
- y=["y"],
- range_x=[0, 100],
- range_y=[0, 100],
- log_x=True,
- log_y=True,
- title_x="x axis title",
- title_y="y axis title",
- ignore_outliers=True,
- groupby="hyperparam1",
- groupby_aggfunc="mean",
- groupby_rangefunc="minmax",
- smoothing_factor=0.5,
- smoothing_type="gaussian",
- smoothing_show_original=True,
- max_runs_to_show=10,
- plot_type="stacked-area",
- font_size="large",
- legend_position="west",
- ),
- wr.ScatterPlot(
- title="scatter title",
- x="y",
- y="y",
- # z='x',
- range_x=[0, 0.0005],
- range_y=[0, 0.0005],
- # range_z=[0,1],
- log_x=False,
- log_y=False,
- # log_z=True,
- running_ymin=True,
- running_ymean=True,
- running_ymax=True,
- font_size="small",
- regression=True,
- ),
- ],
- runsets=[wr.RunSet(project="", entity="")],
-)
-
-
-report.blocks = [panel_grids]
-report.save()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-## Freeze a run set
-
-A report automatically updates run sets to show the latest data from the project. You can preserve the run set in a report by *freezing* that run set. When you freeze a run set, you preserve the state of the run set in a report at a point in time.
-
-To freeze a run set when viewing a report, click the snowflake icon in its panel grid near the **Filter** button.
-
-{{< img src="/images/reports/freeze_runset.png" alt="Freeze runset button" >}}
-
-## Group a run set programmatically
-
-Group runs in a run set programmatically with the [Workspace and Reports API]({{< relref "/ref/wandb_workspaces/reports" >}}).
-
-You can group runs in a run set by config values, run metadata or summary metrics. The following table lists the available grouping methods along with the available keys for that grouping method:
-
-| Grouping Method | Description |Available keys |
-| ---|------| --- |
-| Config values| Group runs by config values | Values specified in config parameter in `wandb.init(config=)` |
-| Run metadata| Group runs by run metadata | `State`, `Name`, `JobType` |
-| Summary metrics| Group runs by summary metrics | Values you log to a run with `wandb.Run.log()` |
-
-
-
-### Group runs by config values
-
-Group runs by config values to compare runs with similar configurations. Config values are parameters you specify in your run configuration `(wandb.init(config=))`. To group runs by config values, use the `config.` syntax, where `` is the name of the config value you want to group by.
-
-For example, the following code snippet first initializes a run with a config value for `group`, then groups runs in a report based on the `group` config value. Replace values for `` and `` with your W&B entity and project names.
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-entity = ""
-project = ""
-
-for group in ["control", "experiment_a", "experiment_b"]:
- for i in range(3):
- with wandb.init(entity=entity, project=project, group=group, config={"group": group, "run": i}, name=f"{group}_run_{i}") as run:
- # Simulate some training
- for step in range(100):
- run.log({
- "acc": 0.5 + (step / 100) * 0.3 + (i * 0.05),
- "loss": 1.0 - (step / 100) * 0.5
- })
-```
-
-Within your Python script or notebook, you can then group runs by the `config.group` value:
-
-```python
-runset = wr.Runset(
- project=project,
- entity=entity,
- groupby=["config.group"] # Group by the "group" config value
-)
-```
-
-Continuing from the previous example, you can create a report with the grouped run set:
-
-```python
-report = wr.Report(
- entity=entity,
- project=project,
- title="Grouped Runs Example",
-)
-
-report.blocks = [
- wr.PanelGrid(
- runsets=[runset],
- )
- ]
-
-report.save()
-```
-
-### Group runs by run metadata
-
-Group runs by a run's name (`Name`), state (`State`), or job type (`JobType`).
-
-Continuing from the previous example, you can group your runs by their name with the following code snippet:
-
-```python
-runset = wr.Runset(
- project=project,
- entity=entity,
- groupby=["Name"] # Group by run names
-)
-```
-
-{{% alert %}}
-The name of the run is the name you specify in the `wandb.init(name=)` parameter. If you do not specify a name, W&B generates a random name for the run.
-
-You can find the name of the run in the **Overview** page of a run in the W&B App or programmatically with `Api.runs().run.name`.
-{{% /alert %}}
-
-### Group runs by summary metrics
-
-The following examples demonstrate how to group runs by summary metrics. Summary metrics are the values you log to a run with `wandb.Run.log()`. After you log a run, you can find the names of your summary metrics in the W&B App under the **Summary** section of a run's **Overview** page.
-
-The syntax for grouping runs by summary metrics is `summary.`, where `` is the name of the summary metric you want to group by.
-
-For example, suppose you log a summary metric called `acc`:
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-entity = ""
-project = ""
-
-for group in ["control", "experiment_a", "experiment_b"]:
- for i in range(3):
- with wandb.init(entity=entity, project=project, group=group, config={"group": group, "run": i}, name=f"{group}_run_{i}") as run:
- # Simulate some training
- for step in range(100):
- run.log({
- "acc": 0.5 + (step / 100) * 0.3 + (i * 0.05),
- "loss": 1.0 - (step / 100) * 0.5
- })
-
-```
-
-You can then group runs by the `summary.acc` summary metric:
-
-```python
-runset = wr.Runset(
- project=project,
- entity=entity,
- groupby=["summary.acc"] # Group by summary values
-)
-```
-
-## Filter a run set programmatically
-
-Programmatically filter run sets and add them to a report with the [Workspace and Reports API]({{< relref "/ref/wandb_workspaces/reports" >}}).
-
-The general syntax for a filter expression is:
-
-```text
-Filter('key') operation
-```
-
-Where `key` is the name of the filter, `operation` is a comparison operator (e.g., `>`, `<`, `==`, `in`, `not in`, `or`, and `and`), and `` is the value to compare against. `Filter` is a placeholder for the type of filter you want to apply. The following table lists the available filters and their descriptions:
-
-| Filter | Description | Available keys |
-| ---|---| --- |
-|`Config('key')` | Filter by config values | Values specified in `config` parameter in `wandb.init(config=)`. |
-|`SummaryMetric('key')` | Filter by summary metrics | Values you log to a run with `wandb.Run.log()`. |
-|`Tags('key')` | Filter by tags | Tag values that you add to your run (programmatically or with the W&B App). |
-|`Metric('key')` | Filter by run properties | `tags`, `state`, `displayName`, `jobType` |
-
-Once you have defined your filters, you can create a report and pass the filtered run sets to `wr.PanelGrid(runsets=)`. See the **Report and Workspace API** tabs throughout this page for more information on how to add various elements to a report programmatically.
-
-The following examples demonstrate how to filter run sets in a report. Replace values enclosed in `<>` with your own values.
-
-### Config filters
-
-Filter a runset by one or more config values. Config values are parameters you specify in your run configuration (`wandb.init(config=)`).
-
-For example, the following code snippet first initializes a run with a config value for `learning_rate` and `batch_size`, then filters runs in a report based on the `learning_rate` config value.
-
-```python
-import wandb
-
-config = {
- "learning_rate": 0.01,
- "batch_size": 32,
-}
-
-with wandb.init(project="", entity="", config=config) as run:
- # Your training code here
- pass
-```
-
-Within your Python script or notebook, you can then programmatically filter runs that have a learning rate greater than `0.01`.
-
-```python
-import wandb_workspaces.reports.v2 as wr
-
-runset = wr.Runset(
- entity="",
- project="",
- filters="Config('learning_rate') > 0.01"
-)
-```
-
-You can also filter by multiple config values with the `and` operator:
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Config('learning_rate') > 0.01 and Config('batch_size') == 32"
-)
-```
-
-Continuing from the previous example, you can create a report with the filtered runset as follows:
-
-```python
-report = wr.Report(
- entity="",
- project="",
- title="My Report"
-)
-
-report.blocks = [
- wr.PanelGrid(
- runsets=[runset],
- panels=[
- wr.LinePlot(
- x="Step",
- y=["accuracy"],
- )
- ]
- )
-]
-
-report.save()
-```
-
-### Metric filters
-
-Filter run sets based on a run's: tag (`tags`), run state (`state`), run name (`displayName`), or job type (`jobType`).
-
-{{% alert %}}
-`Metric` filters posses a different syntax. Pass a list of values as a list.
-
-```text
-Metric('key') operation []
-```
-{{% /alert %}}
-
-For example, consider the following Python snippet that creates three runs and assigns each of them a name:
-
-```python
-import wandb
-
-with wandb.init(project="", entity="") as run:
- for i in range(3):
- run.name = f"run{i+1}"
- # Your training code here
- pass
-```
-
-When you create your report, you can filter runs by their display name. For example, to filter runs with names `run1`, `run2`, and `run3`, you can use the following code:
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Metric('displayName') in ['run1', 'run2', 'run3']"
-)
-```
-
-{{% alert %}}
-You can find the name of the run in the **Overview** page of a run in the W&B App or programmatically with `Api.runs().run.name`.
-{{% /alert %}}
-
-The following examples demonstrate how to filter a runset by the run's state (`finished`, `crashed`, or `running`):
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Metric('state') in ['finished']"
-)
-```
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Metric('state') not in ['crashed']"
-)
-```
-
-
-### SummaryMetric filters
-
-The following examples demonstrate how to filter a run set by summary metrics. Summary metrics are the values you log to a run with `wandb.Run.log()`. After you log a run, you can find the names of your summary metrics in the W&B App under the **Summary** section of a run's **Overview** page.
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="SummaryMetric('accuracy') > 0.9"
-)
-```
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Metric('state') in ['finished'] and SummaryMetric('train/train_loss') < 0.5"
-)
-```
-
-### Tags filters
-
-The following code snippet shows how to filter a runs set by its tags. Tags are values you add to a run (programmatically or with the W&B App).
-
-```python
-runset = wr.Runset(
- entity="",
- project="",
- filters="Tags('training') == 'training'"
-)
-```
-
-## Add code blocks
-
-Add code blocks to your report interactively with the App UI or with the W&B SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="App UI" value="app" %}}
-
-Enter a forward slash (`/`) in the report to display a dropdown menu. From the dropdown choose **Code**.
-
-Select the name of the programming language on the right hand of the code block. This will expand a dropdown. From the dropdown, select your programming language syntax. You can choose from Javascript, Python, CSS, JSON, HTML, Markdown, and YAML.
-
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api" %}}
-
-Use the `wr.CodeBlock` Class to create a code block programmatically. Provide the name of the language and the code you want to display for the language and code parameters, respectively.
-
-For example the proceeding example demonstrates a list in YAML file:
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(project="report-editing")
-
-report.blocks = [
- wr.CodeBlock(
- code=["this:", "- is", "- a", "cool:", "- yaml", "- file"], language="yaml"
- )
-]
-
-report.save()
-```
-
-This will render a code block similar to:
-
-```yaml
-this:
-- is
-- a
-cool:
-- yaml
-- file
-```
-
-The proceeding example demonstrates a Python code block:
-
-```python
-report = wr.Report(project="report-editing")
-
-
-report.blocks = [wr.CodeBlock(code=["Hello, World!"], language="python")]
-
-report.save()
-```
-
-This will render a code block similar to:
-
-```md
-Hello, World!
-```
-
-{{% /tab %}}
-
-{{% /tabpane %}}
-
-## Add markdown
-
-Add markdown to your report interactively with the App UI or with the W&B SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="App UI" value="app" %}}
-
-Enter a forward slash (`/`) in the report to display a dropdown menu. From the dropdown choose **Markdown**.
-
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api" %}}
-
-Use the `wandb.apis.reports.MarkdownBlock` Class to create a markdown block programmatically. Pass a string to the `text` parameter:
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(project="report-editing")
-
-report.blocks = [
- wr.MarkdownBlock(text="Markdown cell with *italics* and **bold** and $e=mc^2$")
-]
-```
-
-This will render a markdown block similar to:
-
-{{< img src="/images/reports/markdown.png" alt="Rendered markdown block" >}}
-
-{{% /tab %}}
-
-{{% /tabpane %}}
-
-
-## Add HTML elements
-
-Add HTML elements to your report interactively with the App UI or with the W&B SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="App UI" value="app" %}}
-
-Enter a forward slash (`/`) in the report to display a dropdown menu. From the dropdown select a type of text block. For example, to create an H2 heading block, select the `Heading 2` option.
-
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api" %}}
-
-Pass a list of one or more HTML elements to `wandb.apis.reports.blocks` attribute. The proceeding example demonstrates how to create an H1, H2, and an unordered list:
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(project="report-editing")
-
-report.blocks = [
- wr.H1(text="How Programmatic Reports work"),
- wr.H2(text="Heading 2"),
- wr.UnorderedList(items=["Bullet 1", "Bullet 2"]),
-]
-
-report.save()
-```
-
-This will render a HTML elements to the following:
-
-
-{{< img src="/images/reports/render_html.png" alt="Rendered HTML elements" >}}
-
-{{% /tab %}}
-
-{{% /tabpane %}}
-
-## Embed rich media links
-
-Embed rich media within the report with the App UI or with the W&B SDK.
-
-{{< tabpane text=true >}}
-{{% tab header="App UI" value="app" %}}
-
-Copy and past URLs into reports to embed rich media within the report. The following animations demonstrate how to copy and paste URLs from Twitter, YouTube, and SoundCloud.
-
-### Twitter
-
-Copy and paste a Tweet link URL into a report to view the Tweet within the report.
-
-{{< img src="/images/reports/twitter.gif" alt="Embedding Twitter content" >}}
-
-### Youtube
-
-Copy and paste a YouTube video URL link to embed a video in the report.
-
-{{< img src="/images/reports/youtube.gif" alt="Embedding YouTube videos" >}}
-
-### SoundCloud
-
-Copy and paste a SoundCloud link to embed an audio file into a report.
-
-{{< img src="/images/reports/soundcloud.gif" alt="Embedding SoundCloud audio" >}}
-
-{{% /tab %}}
-
-{{% tab header="Report and Workspace API" value="python_wr_api" %}}
-
-Pass a list of one or more embedded media objects to the `wandb.apis.reports.blocks` attribute. The proceeding example demonstrates how to embed video and Twitter media into a report:
-
-```python
-import wandb
-import wandb_workspaces.reports.v2 as wr
-
-report = wr.Report(project="report-editing")
-
-report.blocks = [
- wr.Video(url="https://www.youtube.com/embed/6riDJMI-Y8U"),
- wr.Twitter(
- embed_html='
\n'
- ),
-]
-report.save()
-```
-
-{{% /tab %}}
-
-{{% /tabpane %}}
-
-## Duplicate and delete panel grids
-
-If you have a layout that you would like to reuse, you can select a panel grid and copy-paste it to duplicate it in the same report or even paste it into a different report.
-
-Highlight a whole panel grid section by selecting the drag handle in the upper right corner. Click and drag to highlight and select a region in a report such as panel grids, text, and headings.
-
-{{< img src="/images/reports/demo_copy_and_paste_a_panel_grid_section.gif" alt="Copying panel grids" >}}
-
-Select a panel grid and press `delete` on your keyboard to delete a panel grid.
-
-{{< img src="/images/reports/delete_panel_grid.gif" alt="Deleting panel grids" >}}
-
-## Collapse headers to organize Reports
-
-Collapse headers in a Report to hide content within a text block. When the report is loaded, only headers that are expanded will show content. Collapsing headers in reports can help organize your content and prevent excessive data loading. The proceeding gif demonstrates the process.
-
-{{< img src="/images/reports/collapse_headers.gif" alt="Collapsing headers in a report." >}}
-
-## Visualize relationships across multiple dimensions
-
-To effectively visualize relationships across multiple dimensions, use a color gradient to represent one of the variables. This enhances clarity and makes patterns easier to interpret.
-
-1. Choose a variable to represent with a color gradient (e.g., penalty scores, learning rates, etc.). This allows for a clearer understanding of how penalty (color) interacts with reward/side effects (y-axis) over training time (x-axis).
-2. Highlight key trends. Hovering over a specific group of runs highlights them in the visualization.
-
diff --git a/content/en/guides/core/reports/embed-reports.md b/content/en/guides/core/reports/embed-reports.md
deleted file mode 100644
index d1e00ca180..0000000000
--- a/content/en/guides/core/reports/embed-reports.md
+++ /dev/null
@@ -1,53 +0,0 @@
----
-description: Embed W&B reports directly into Notion or with an HTML IFrame element.
-menu:
- default:
- identifier: embed-reports
- parent: reports
-title: Embed a report
-weight: 50
----
-
-## HTML iframe element
-
-Select the **Share** button on the upper right hand corner within a report. A modal window will appear. Within the modal window, select **Copy embed code**. The copied code will render within an Inline Frame (IFrame) HTML element. Paste the copied code into an iframe HTML element of your choice.
-
-{{% alert %}}
-Only **public** reports are viewable when embedded.
-{{% /alert %}}
-
-{{< img src="/images/reports/get_embed_url.gif" alt="Getting embed code" >}}
-
-## Confluence
-
-The proceeding animation demonstrates how to insert the direct link to the report within an IFrame cell in Confluence.
-
-{{< img src="//images/reports/embed_iframe_confluence.gif" alt="Embedding in Confluence" >}}
-
-## Notion
-
-The proceeding animation demonstrates how to insert a report into a Notion document using an Embed block in Notion and the report's embedded code.
-
-{{< img src="//images/reports/embed_iframe_notion.gif" alt="Embedding in Notion" >}}
-
-## Gradio
-
-You can use the `gr.HTML` element to embed W&B Reports within Gradio Apps and use them within Hugging Face Spaces.
-
-```python
-import gradio as gr
-
-
-def wandb_report(url):
- iframe = f'
-
-## Related resources
-
-* [Examples](https://github.com/wandb/examples): Try the code with notebook and script examples for each integration.
-* [Video Tutorials](https://www.youtube.com/playlist?list=PLD80i8An1OEGajeVo15ohAQYF1Ttle0lk): Learn to use W&B with YouTube video tutorials
diff --git a/content/en/guides/integrations/accelerate.md b/content/en/guides/integrations/accelerate.md
deleted file mode 100644
index 4e5760bde7..0000000000
--- a/content/en/guides/integrations/accelerate.md
+++ /dev/null
@@ -1,88 +0,0 @@
----
-description: Training and inference at scale made simple, efficient and adaptable
-menu:
- default:
- identifier: accelerate
- parent: integrations
-title: Hugging Face Accelerate
-weight: 140
----
-
-Hugging Face Accelerate is a library that enables the same PyTorch code to run across any distributed configuration, to simplify model training and inference at scale.
-
-Accelerate includes a W&B Tracker which we show how to use below. You can also read more about [Accelerate Trackers in Hugging Face](https://huggingface.co/docs/accelerate/main/en/usage_guides/tracking).
-
-## Start logging with Accelerate
-
-To get started with Accelerate and W&B you can follow the pseudocode below:
-
-```python
-from accelerate import Accelerator
-
-# Tell the Accelerator object to log with wandb
-accelerator = Accelerator(log_with="wandb")
-
-# Initialise your wandb run, passing wandb parameters and any config information
-accelerator.init_trackers(
- project_name="my_project",
- config={"dropout": 0.1, "learning_rate": 1e-2}
- init_kwargs={"wandb": {"entity": "my-wandb-team"}}
- )
-
-...
-
-# Log to wandb by calling `accelerator.log`, `step` is optional
-accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=global_step)
-
-
-# Make sure that the wandb tracker finishes correctly
-accelerator.end_training()
-```
-
-Explaining more, you need to:
-1. Pass `log_with="wandb"` when initialising the Accelerator class
-2. Call the [`init_trackers`](https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.init_trackers) method and pass it:
-- a project name via `project_name`
-- any parameters you want to pass to [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) via a nested dict to `init_kwargs`
-- any other experiment config information you want to log to your wandb run, via `config`
-3. Use the `.log` method to log to Weigths & Biases; the `step` argument is optional
-4. Call `.end_training` when finished training
-
-## Access the W&B tracker
-
-To access the W&B tracker, use the `Accelerator.get_tracker()` method. Pass in the string corresponding to a tracker’s `.name` attribute, which returns the tracker on the `main` process.
-
-```python
-wandb_tracker = accelerator.get_tracker("wandb")
-
-```
-From there you can interact with wandb’s run object like normal:
-
-```python
-wandb_tracker.log_artifact(some_artifact_to_log)
-```
-
-{{% alert color="secondary" %}}
-Trackers built in Accelerate will automatically execute on the correct process, so if a tracker is only meant to be ran on the main process it will do so automatically.
-
-If you want to truly remove Accelerate’s wrapping entirely, you can achieve the same outcome with:
-
-```python
-wandb_tracker = accelerator.get_tracker("wandb", unwrap=True)
-with accelerator.on_main_process:
- wandb_tracker.log_artifact(some_artifact_to_log)
-```
-{{% /alert %}}
-
-## Accelerate Articles
-Below is an Accelerate article you may enjoy
-
-
-
-HuggingFace Accelerate Super Charged With W&B
-
-* In this article, we'll look at what HuggingFace Accelerate has to offer and how simple it is to perform distributed training and evaluation, while logging results to W&B.
-
-Read the [Hugging Face Accelerate Super Charged with W&B report](https://wandb.ai/gladiator/HF%20Accelerate%20+%20W&B/reports/Hugging-Face-Accelerate-Super-Charged-with-Weights-Biases--VmlldzoyNzk3MDUx?utm_source=docs&utm_medium=docs&utm_campaign=accelerate-docs).
-
-
\ No newline at end of file
diff --git a/content/en/guides/integrations/add-wandb-to-any-library.md b/content/en/guides/integrations/add-wandb-to-any-library.md
deleted file mode 100644
index 3522936ab4..0000000000
--- a/content/en/guides/integrations/add-wandb-to-any-library.md
+++ /dev/null
@@ -1,430 +0,0 @@
----
-menu:
- default:
- identifier: add-wandb-to-any-library
- parent: integrations
-title: Add wandb to any library
-weight: 10
----
-
-## Add wandb to any library
-
-This guide provides best practices on how to integrate W&B into your Python library to get powerful Experiment Tracking, GPU and System Monitoring, Model Checkpointing, and more for your own library.
-
-{{% alert %}}
-If you are still learning how to use W&B, we recommend exploring the other W&B Guides in these docs, such as [Experiment Tracking]({{< relref "/guides/models/track" >}}), before reading further.
-{{% /alert %}}
-
-Below we cover best tips and best practices when the codebase you are working on is more complicated than a single Python training script or Jupyter notebook. The topics covered are:
-
-* Setup requirements
-* User Login
-* Starting a wandb Run
-* Defining a Run Config
-* Logging to W&B
-* Distributed Training
-* Model Checkpointing and More
-* Hyper-parameter tuning
-* Advanced Integrations
-
-### Setup requirements
-
-Before you get started, decide whether or not to require W&B in your library’s dependencies:
-
-#### Require W&B on installation
-
-Add the W&B Python library (`wandb`) to your dependencies file, for example, in your `requirements.txt` file:
-
-```python
-torch==1.8.0
-...
-wandb==0.13.*
-```
-
-#### Make W&B optional on installation
-
-There are two ways to make the W&B SDK (`wandb`) optional:
-
-A. Raise an error when a user tries to use `wandb` functionality without installing it manually and show an appropriate error message:
-
-```python
-try:
- import wandb
-except ImportError:
- raise ImportError(
- "You are trying to use wandb which is not currently installed."
- "Please install it using pip install wandb"
- )
-```
-
-B. Add `wandb` as an optional dependency to your `pyproject.toml` file, if you are building a Python package:
-
-```toml
-[project]
-name = "my_awesome_lib"
-version = "0.1.0"
-dependencies = [
- "torch",
- "sklearn"
-]
-
-[project.optional-dependencies]
-dev = [
- "wandb"
-]
-```
-
-### User login
-
-#### Create an API key
-
-An API key authenticates a client or machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-#### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python-notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-If a user is using wandb for the first time without following any of the steps mentioned above, they will automatically be prompted to log in when your script calls `wandb.init`.
-
-### Start a run
-
-A W&B Run is a unit of computation logged by W&B. Typically, you associate a single W&B Run per training experiment.
-
-Initialize W&B and start a Run within your code with:
-
-```python
-run = wandb.init()
-```
-
-Optionally, you can provide a name for their project, or let the user set it themselves with parameters such as `wandb_project` in your code along with the username or team name, such as `wandb_entity`, for the entity parameter:
-
-```python
-run = wandb.init(project=wandb_project, entity=wandb_entity)
-```
-
-You must call `run.finish()` to finish the run. If this works with your integration's design, use the run as a context manager:
-
-```python
-# When this block exits, it calls run.finish() automatically.
-# If it exits due to an exception, it uses run.finish(exit_code=1) which
-# marks the run as failed.
-with wandb.init() as run:
- ...
-```
-
-
-#### When to call `wandb.init`?
-
-Your library should create W&B Run as early as possible because any output in your console, including error messages, is logged as part of the W&B Run. This makes debugging easier.
-
-#### Use `wandb` as an optional dependency
-
-If you want to make `wandb` optional when your users use your library, you can either:
-
-* Define a `wandb` flag such as:
-
-{{< tabpane text=true >}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-trainer = my_trainer(..., use_wandb=True)
-```
-{{% /tab %}}
-
-{{% tab header="Bash" value="bash" %}}
-
-```bash
-python train.py ... --use-wandb
-```
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-* Or, set `wandb` to be `disabled` in `wandb.init`:
-
-{{< tabpane text=true >}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-wandb.init(mode="disabled")
-```
-{{% /tab %}}
-
-{{% tab header="Bash" value="bash" %}}
-
-```bash
-export WANDB_MODE=disabled
-```
-
-or
-
-```bash
-wandb disabled
-```
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-* Or, set `wandb` to be offline - note this will still run `wandb`, it just won't try and communicate back to W&B over the internet:
-
-{{< tabpane text=true >}}
-
-{{% tab header="Environment Variable" value="environment" %}}
-
-```bash
-export WANDB_MODE=offline
-```
-
-or
-
-```python
-os.environ['WANDB_MODE'] = 'offline'
-```
-{{% /tab %}}
-
-{{% tab header="Bash" value="bash" %}}
-
-```bash
-wandb offline
-```
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-### Define a run config
-With a `wandb` run config, you can provide metadata about your model, dataset, and so on when you create a W&B Run. You can use this information to compare different experiments and quickly understand the main differences.
-
-{{< img src="/images/integrations/integrations_add_any_lib_runs_page.png" alt="W&B Runs table" >}}
-
-Typical config parameters you can log include:
-
-* Model name, version, architecture parameters, etc.
-* Dataset name, version, number of train/val examples, etc.
-* Training parameters such as learning rate, batch size, optimizer, etc.
-
-The following code snippet shows how to log a config:
-
-```python
-config = {"batch_size": 32, ...}
-wandb.init(..., config=config)
-```
-
-#### Update the run config
-Use `wandb.Run.config.update` to update the config. Updating your configuration dictionary is useful when parameters are obtained after the dictionary was defined. For example, you might want to add a model’s parameters after the model is instantiated.
-
-```python
-run.config.update({"model_parameters": 3500})
-```
-
-For more information on how to define a config file, see [Configure experiments]({{< relref "/guides/models/track/config" >}}).
-
-### Log to W&B
-
-#### Log metrics
-
-Create a dictionary where the key value is the name of the metric. Pass this dictionary object to [`run.log`]({{< relref "/guides/models/track/log" >}}):
-
-```python
-for epoch in range(NUM_EPOCHS):
- for input, ground_truth in data:
- prediction = model(input)
- loss = loss_fn(prediction, ground_truth)
- metrics = { "loss": loss }
- run.log(metrics)
-```
-
-If you have a lot of metrics, you can have them automatically grouped in the UI by using prefixes in the metric name, such as `train/...` and `val/...`. This will create separate sections in your W&B Workspace for your training and validation metrics, or other metric types you'd like to separate:
-
-```python
-metrics = {
- "train/loss": 0.4,
- "train/learning_rate": 0.4,
- "val/loss": 0.5,
- "val/accuracy": 0.7
-}
-run.log(metrics)
-```
-
-{{< img src="/images/integrations/integrations_add_any_lib_log.png" alt="W&B Workspace" >}}
-
-See the [`wandb.Run.log()` reference]({{< relref "/guides/models/track/log" >}}).
-
-#### Prevent x-axis misalignments
-
-If you perform multiple calls to `run.log` for the same training step, the wandb SDK increments an internal step counter for each call to `run.log`. This counter may not align with the training step in your training loop.
-
-To avoid this situation, define your x-axis step explicitly with `run.define_metric`, one time, immediately after you call `wandb.init`:
-
-```python
-with wandb.init(...) as run:
- run.define_metric("*", step_metric="global_step")
-```
-
-The glob pattern, `*`, means that every metric will use `global_step` as the x-axis in your charts. If you only want certain metrics to be logged against `global_step`, you can specify them instead:
-
-```python
-run.define_metric("train/loss", step_metric="global_step")
-```
-
-Now, log your metrics, your `step` metric, and your `global_step` each time you call `run.log`:
-
-```python
-for step, (input, ground_truth) in enumerate(data):
- ...
- run.log({"global_step": step, "train/loss": 0.1})
- run.log({"global_step": step, "eval/loss": 0.2})
-```
-
-If you do not have access to the independent step variable, for example "global_step" is not available during your validation loop, the previously logged value for "global_step" is automatically used by wandb. In this case, ensure you log an initial value for the metric so it has been defined when it’s needed.
-
-#### Log images, tables, audio, and more
-
-In addition to metrics, you can log plots, histograms, tables, text, and media such as images, videos, audios, 3D, and more.
-
-Some considerations when logging data include:
-
-* How often should the metric be logged? Should it be optional?
-* What type of data could be helpful in visualizing?
- * For images, you can log sample predictions, segmentation masks, etc., to see the evolution over time.
- * For text, you can log tables of sample predictions for later exploration.
-
-See the [logging guide]({{< relref "/guides/models/track/log" >}}) for media, objects, plots, and more.
-
-### Distributed training
-
-For frameworks supporting distributed environments, you can adapt any of the following workflows:
-
-* Detect which is the "main" process and only use `wandb` there. Any required data coming from other processes must be routed to the main process first. (This workflow is encouraged).
-* Call `wandb` in every process and auto-group them by giving them all the same unique `group` name.
-
-See [Log Distributed Training Experiments]({{< relref "/guides/models/track/log/distributed-training.md" >}}) for more details.
-
-### Log model checkpoints and more
-
-If your framework uses or produces models or datasets, you can log them for full traceability and have wandb automatically monitor your entire pipeline through W&B Artifacts.
-
-{{< img src="/images/integrations/integrations_add_any_lib_dag.png" alt="Stored Datasets and Model Checkpoints in W&B" >}}
-
-When using Artifacts, it might be useful but not necessary to let your users define:
-
-* The ability to log model checkpoints or datasets (in case you want to make it optional).
-* The path/reference of the artifact being used as input, if any. For example, `user/project/artifact`.
-* The frequency for logging Artifacts.
-
-#### Log model checkpoints
-
-You can log Model Checkpoints to W&B. It is useful to leverage the unique `wandb` Run ID to name output Model Checkpoints to differentiate them between Runs. You can also add useful metadata. In addition, you can also add aliases to each model as shown below:
-
-```python
-metadata = {"eval/accuracy": 0.8, "train/steps": 800}
-
-artifact = wandb.Artifact(
- name=f"model-{run.id}",
- metadata=metadata,
- type="model"
- )
-artifact.add_dir("output_model") # local directory where the model weights are stored
-
-aliases = ["best", "epoch_10"]
-run.log_artifact(artifact, aliases=aliases)
-```
-
-For information on how to create a custom alias, see [Create a Custom Alias]({{< relref "/guides/core/artifacts/create-a-custom-alias/" >}}).
-
-You can log output Artifacts at any frequency (for example, every epoch, every 500 steps, and so on) and they are automatically versioned.
-
-#### Log and track pre-trained models or datasets
-
-You can log artifacts that are used as inputs to your training such as pre-trained models or datasets. The following snippet demonstrates how to log an Artifact and add it as an input to the ongoing Run as shown in the graph above.
-
-```python
-artifact_input_data = wandb.Artifact(name="flowers", type="dataset")
-artifact_input_data.add_file("flowers.npy")
-run.use_artifact(artifact_input_data)
-```
-
-#### Download an artifact
-
-You re-use an Artifact (dataset, model, etc.) and `wandb` will download a copy locally (and cache it):
-
-```python
-artifact = run.use_artifact("user/project/artifact:latest")
-local_path = artifact.download("./tmp")
-```
-
-Artifacts can be found in the Artifacts section of W&B and can be referenced with aliases generated automatically (`latest`, `v2`, `v3`) or manually when logging (`best_accuracy`, etc.).
-
-To download an Artifact without creating a `wandb` run (through `wandb.init`), for example in distributed environments or for simple inference, you can instead reference the artifact with the [wandb API]({{< relref "/ref/python/public-api/index.md" >}}):
-
-```python
-artifact = wandb.Api().artifact("user/project/artifact:latest")
-local_path = artifact.download()
-```
-
-For more information, see [Download and Use Artifacts]({{< relref "/guides/core/artifacts/download-and-use-an-artifact" >}}).
-
-### Tune hyper-parameters
-
-If your library would like to leverage W&B hyper-parameter tuning, [W&B Sweeps]({{< relref "/guides/models/sweeps/" >}}) can also be added to your library.
-
-### Advanced integrations
-
-You can also see what an advanced W&B integrations look like in the following integrations. Note most integrations will not be as complex as these:
-
-* [Hugging Face Transformers `WandbCallback`](https://github.com/huggingface/transformers/blob/49629e7ba8ef68476e08b671d6fc71288c2f16f1/src/transformers/integrations.py#L639)
-* [PyTorch Lightning `WandbLogger`](https://github.com/Lightning-AI/lightning/blob/18f7f2d3958fb60fcb17b4cb69594530e83c217f/src/pytorch_lightning/loggers/wandb.py#L53)
\ No newline at end of file
diff --git a/content/en/guides/integrations/autotrain.md b/content/en/guides/integrations/autotrain.md
deleted file mode 100644
index 15580da8a7..0000000000
--- a/content/en/guides/integrations/autotrain.md
+++ /dev/null
@@ -1,142 +0,0 @@
----
-menu:
- default:
- identifier: autotrain
- parent: integrations
-title: Hugging Face AutoTrain
-weight: 130
----
-[Hugging Face AutoTrain](https://huggingface.co/docs/autotrain/index) is a no-code tool for training state-of-the-art models for Natural Language Processing (NLP) tasks, for Computer Vision (CV) tasks, and for Speech tasks and even for Tabular tasks.
-
-[W&B](https://wandb.com/) is directly integrated into Hugging Face AutoTrain, providing experiment tracking and config management. It's as easy as using a single parameter in the CLI command for your experiments.
-
-{{< img src="/images/integrations/hf-autotrain-1.png" alt="Experiment metrics logging" >}}
-
-## Install prerequisites
-
-Install `autotrain-advanced` and `wandb`.
-
-{{< tabpane text=true >}}
-
-{{% tab header="Command Line" value="script" %}}
-
-```shell
-pip install --upgrade autotrain-advanced wandb
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-```notebook
-!pip install --upgrade autotrain-advanced wandb
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-To demonstrate these changes, this page fine-tines an LLM on a math dataset to achieve SoTA result in `pass@1` on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math).
-
-## Prepare the dataset
-
-Hugging Face AutoTrain expects your CSV custom dataset to have a specific format to work properly.
-
-- Your training file must contain a `text` column, which the training uses. For best results, the `text` column's data must conform to the `### Human: Question?### Assistant: Answer.` format. Review a great example in [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
-
- However, the [MetaMathQA dataset](https://huggingface.co/datasets/meta-math/MetaMathQA) includes the columns `query`, `response`, and `type`. First, pre-process this dataset. Remove the `type` column and combine the content of the `query` and `response` columns into a new `text` column in the `### Human: Query?### Assistant: Response.` format. Training uses the resulting dataset, [`rishiraj/guanaco-style-metamath`](https://huggingface.co/datasets/rishiraj/guanaco-style-metamath).
-
-## Train using `autotrain`
-
-You can start training using the `autotrain` advanced from the command line or a notebook. Use the `--log` argument, or use `--log wandb` to log your results to a [W&B Run]({{< relref "/guides/models/track/runs/" >}}).
-
-{{< tabpane text=true >}}
-
-{{% tab header="Command Line" value="script" %}}
-
-```shell
-autotrain llm \
- --train \
- --model HuggingFaceH4/zephyr-7b-alpha \
- --project-name zephyr-math \
- --log wandb \
- --data-path data/ \
- --text-column text \
- --lr 2e-5 \
- --batch-size 4 \
- --epochs 3 \
- --block-size 1024 \
- --warmup-ratio 0.03 \
- --lora-r 16 \
- --lora-alpha 32 \
- --lora-dropout 0.05 \
- --weight-decay 0.0 \
- --gradient-accumulation 4 \
- --logging_steps 10 \
- --fp16 \
- --use-peft \
- --use-int4 \
- --merge-adapter \
- --push-to-hub \
- --token \
- --repo-id
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-```notebook
-# Set hyperparameters
-learning_rate = 2e-5
-num_epochs = 3
-batch_size = 4
-block_size = 1024
-trainer = "sft"
-warmup_ratio = 0.03
-weight_decay = 0.
-gradient_accumulation = 4
-lora_r = 16
-lora_alpha = 32
-lora_dropout = 0.05
-logging_steps = 10
-
-# Run training
-!autotrain llm \
- --train \
- --model "HuggingFaceH4/zephyr-7b-alpha" \
- --project-name "zephyr-math" \
- --log "wandb" \
- --data-path data/ \
- --text-column text \
- --lr str(learning_rate) \
- --batch-size str(batch_size) \
- --epochs str(num_epochs) \
- --block-size str(block_size) \
- --warmup-ratio str(warmup_ratio) \
- --lora-r str(lora_r) \
- --lora-alpha str(lora_alpha) \
- --lora-dropout str(lora_dropout) \
- --weight-decay str(weight_decay) \
- --gradient-accumulation str(gradient_accumulation) \
- --logging-steps str(logging_steps) \
- --fp16 \
- --use-peft \
- --use-int4 \
- --merge-adapter \
- --push-to-hub \
- --token str(hf_token) \
- --repo-id "rishiraj/zephyr-math"
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-
-{{< img src="/images/integrations/hf-autotrain-2.gif" alt="Experiment config saving" >}}
-
-## More Resources
-
-* [AutoTrain Advanced now supports Experiment Tracking](https://huggingface.co/blog/rishiraj/log-autotrain) by [Rishiraj Acharya](https://huggingface.co/rishiraj).
-* [Hugging Face AutoTrain Docs](https://huggingface.co/docs/autotrain/index)
\ No newline at end of file
diff --git a/content/en/guides/integrations/azure-openai-fine-tuning.md b/content/en/guides/integrations/azure-openai-fine-tuning.md
deleted file mode 100644
index fcb6a5b8dc..0000000000
--- a/content/en/guides/integrations/azure-openai-fine-tuning.md
+++ /dev/null
@@ -1,49 +0,0 @@
----
-description: How to Fine-Tune Azure OpenAI models using W&B.
-menu:
- default:
- identifier: azure-openai-fine-tuning
- parent: integrations
-title: Azure OpenAI Fine-Tuning
-weight: 20
----
-
-## Introduction
-Fine-tuning GPT-3.5 or GPT-4 models on Microsoft Azure using W&B tracks, analyzes, and improves model performance by automatically capturing metrics and facilitating systematic evaluation through W&B's experiment tracking and evaluation tools.
-
-{{< img src="/images/integrations/aoai_ft_plot.png" alt="Azure OpenAI fine-tuning metrics" >}}
-
-## Prerequisites
-- Set up Azure OpenAI service according to [official Azure documentation](https://wandb.me/aoai-wb-int).
-- Configure a W&B account with an API key.
-
-## Workflow overview
-
-### 1. Fine-tuning setup
-- Prepare training data according to Azure OpenAI requirements.
-- Configure the fine-tuning job in Azure OpenAI.
-- W&B automatically tracks the fine-tuning process, logging metrics and hyperparameters.
-
-### 2. Experiment tracking
-During fine-tuning, W&B captures:
-- Training and validation metrics
-- Model hyperparameters
-- Resource utilization
-- Training artifacts
-
-### 3. Model evaluation
-After fine-tuning, use [W&B Weave](https://weave-docs.wandb.ai) to:
-- Evaluate model outputs against reference datasets
-- Compare performance across different fine-tuning runs
-- Analyze model behavior on specific test cases
-- Make data-driven decisions for model selection
-
-## Real-world example
-* Explore the [medical note generation demo](https://wandb.me/aoai-ft-colab) to see how this integration facilitates:
- - Systematic tracking of fine-tuning experiments
- - Model evaluation using domain-specific metrics
-* Go through an [interactive demo of fine-tuning a notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/azure/azure_gpt_medical_notes.ipynb)
-
-## Additional resources
-- [Azure OpenAI W&B Integration Guide](https://wandb.me/aoai-wb-int)
-- [Azure OpenAI Fine-tuning Documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython&pivots=programming-language-python)
\ No newline at end of file
diff --git a/content/en/guides/integrations/catalyst.md b/content/en/guides/integrations/catalyst.md
deleted file mode 100644
index 838b7284ff..0000000000
--- a/content/en/guides/integrations/catalyst.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-description: How to integrate W&B for Catalyst, a Pytorch framework.
-menu:
- default:
- identifier: catalyst
- parent: integrations
-title: Catalyst
-weight: 30
----
-
-[Catalyst](https://github.com/catalyst-team/catalyst) is a PyTorch framework for deep learning R&D that focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new.
-
-Catalyst includes a W&B integration for logging parameters, metrics, images, and other artifacts.
-
-Check out their [documentation of the integration](https://catalyst-team.github.io/catalyst/api/loggers.html#catalyst.loggers.wandb.WandbLogger), which includes examples using Python and Hydra.
-
-## Interactive Example
-
-Run an [example colab](https://colab.research.google.com/drive/1PD0LnXiADCtt4mu7bzv7VfQkFXVrPxJq?usp=sharing) to see Catalyst and W&B integration in action.
\ No newline at end of file
diff --git a/content/en/guides/integrations/cohere-fine-tuning.md b/content/en/guides/integrations/cohere-fine-tuning.md
deleted file mode 100644
index f39e8ac1a6..0000000000
--- a/content/en/guides/integrations/cohere-fine-tuning.md
+++ /dev/null
@@ -1,63 +0,0 @@
----
-description: How to Fine-Tune Cohere models using W&B.
-menu:
- default:
- identifier: cohere-fine-tuning
- parent: integrations
-title: Cohere fine-tuning
-weight: 40
----
-With W&B you can log your Cohere model's fine-tuning metrics and configuration to analyze and understand the performance of your models and share the results with your colleagues.
-
-This [guide from Cohere](https://docs.cohere.com/page/convfinqa-finetuning-wandb) has a full example of how to kick off a fine-tuning run and you can find the [Cohere API docs here](https://docs.cohere.com/reference/createfinetunedmodel#request.body.settings.wandb)
-
-## Log your Cohere fine-tuning results
-
-To add Cohere fine-tuning logging to your W&B workspace:
-
-1. Create a `WandbConfig` with your W&B API key, W&B `entity` and `project` name. You can find your W&B API key at https://wandb.ai/authorize
-
-2. Pass this config to the `FinetunedModel` object along with your model name, dataset and hyperparameters to kick off your fine-tuning run.
-
-
- ```python
- from cohere.finetuning import WandbConfig, FinetunedModel
-
- # create a config with your W&B details
- wandb_ft_config = WandbConfig(
- api_key="",
- entity="my-entity", # must be a valid enitity associated with the provided API key
- project="cohere-ft",
- )
-
- ... # set up your datasets and hyperparameters
-
- # start a fine-tuning run on cohere
- cmd_r_finetune = co.finetuning.create_finetuned_model(
- request=FinetunedModel(
- name="command-r-ft",
- settings=Settings(
- base_model=...
- dataset_id=...
- hyperparameters=...
- wandb=wandb_ft_config # pass your W&B config here
- ),
- ),
- )
- ```
-
-3. View your model's fine-tuning training and validation metrics and hyperparameters in the W&B project that you created.
-
- {{< img src="/images/integrations/cohere_ft.png" alt="Cohere fine-tuning dashboard" >}}
-
-
-## Organize runs
-
-Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate and any other hyper-parameter.
-
-In addition, you can rename your runs, add notes or create tags to group them.
-
-
-## Resources
-
-* [Cohere Fine-tuning Example](https://github.com/cohere-ai/notebooks/blob/kkt_ft_cookbooks/notebooks/finetuning/convfinqa_finetuning_wandb.ipynb)
\ No newline at end of file
diff --git a/content/en/guides/integrations/composer.md b/content/en/guides/integrations/composer.md
deleted file mode 100644
index d6ac673896..0000000000
--- a/content/en/guides/integrations/composer.md
+++ /dev/null
@@ -1,101 +0,0 @@
----
-description: State of the art algorithms to train your neural networks
-menu:
- default:
- identifier: composer
- parent: integrations
-title: MosaicML Composer
-weight: 230
----
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/master/colabs/mosaicml/MosaicML_Composer_and_wandb.ipynb" >}}
-
-[Composer](https://github.com/mosaicml/composer) is a library for training neural networks better, faster, and cheaper. It contains many state-of-the-art methods for accelerating neural network training and improving generalization, along with an optional [Trainer](https://docs.mosaicml.com/projects/composer/en/stable/trainer/using_the_trainer.html) API that makes _composing_ many different enhancements easy.
-
-W&B provides a lightweight wrapper for logging your ML experiments. But you don't need to combine the two yourself: W&B is incorporated directly into the Composer library via the [WandBLogger](https://docs.mosaicml.com/projects/composer/en/stable/trainer/file_uploading.html#weights-biases-artifacts).
-
-## Start logging to W&B
-
-```python
-from composer import Trainer
-from composer.loggers import WandBLogger
-
-trainer = Trainer(..., logger=WandBLogger())
-```
-
-{{< img src="/images/integrations/n6P7K4M.gif" alt="Interactive dashboards" >}}
-
-## Use Composer's `WandBLogger`
-
-The Composer library uses [WandBLogger](https://docs.mosaicml.com/projects/composer/en/stable/trainer/file_uploading.html#weights-biases-artifacts) class in the `Trainer` to log metrics to W&B. It is as simple as instantiating the logger and passing it to the `Trainer`.
-
-```python
-wandb_logger = WandBLogger(project="gpt-5", log_artifacts=True)
-trainer = Trainer(logger=wandb_logger)
-```
-
-## Logger arguments
-
-Below the parameters for `WandbLogger`, see the [Composer documentation](https://docs.mosaicml.com/projects/composer/en/stable/api_reference/generated/composer.loggers.WandBLogger.html) for a full list and description.
-
-| Parameter | Description |
-| ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `project` | W&B Project name (str, optional)
-| `group` | W&B group name (str, optional)
-| `name` | W&B Run name. If not specified, the State.run_name is used (str, optional)
-| `entity` | W&B entity name, such as your username or W&B Team name (str, optional)
-| `tags` | W&B tags (List[str], optional)
-| `log_artifacts` | Whether to log checkpoints to wandb, default: `false` (bool, optional)|
-| `rank_zero_only` | Whether to log only on the rank-zero process. When logging artifacts, it is highly recommended to log on all ranks. Artifacts from ranks ≥1 are not stored, which may discard pertinent information. For example, when using Deepspeed ZeRO, it would be impossible to restore from checkpoints without artifacts from all ranks, default: `True` (bool, optional)
-| `init_kwargs` | Params to pass to `wandb.init()` such as your wandb `config` etc. See the [`wandb.init()` parameters]({{< relref "/ref/python/functions/init.md" >}}) for parameters that `wandb.init()` accepts.
-
-A typical usage would be:
-
-```
-init_kwargs = {"notes":"Testing higher learning rate in this experiment",
- "config":{"arch":"Llama",
- "use_mixed_precision":True
- }
- }
-
-wandb_logger = WandBLogger(log_artifacts=True, init_kwargs=init_kwargs)
-```
-
-## Log prediction samples
-
-You can use [Composer's Callbacks](https://docs.mosaicml.com/projects/composer/en/stable/trainer/callbacks.html) system to control when you log to W&B via the `WandBLogger`, in this example a sample of the validation images and predictions is logged:
-
-```python
-import wandb
-from composer import Callback, State, Logger
-
-class LogPredictions(Callback):
- def __init__(self, num_samples=100, seed=1234):
- super().__init__()
- self.num_samples = num_samples
- self.data = []
-
- def eval_batch_end(self, state: State, logger: Logger):
- """Compute predictions per batch and stores them on self.data"""
-
- if state.timer.epoch == state.max_duration: #on last val epoch
- if len(self.data) < self.num_samples:
- n = self.num_samples
- x, y = state.batch_pair
- outputs = state.outputs.argmax(-1)
- data = [[wandb.Image(x_i), y_i, y_pred] for x_i, y_i, y_pred in list(zip(x[:n], y[:n], outputs[:n]))]
- self.data += data
-
- def eval_end(self, state: State, logger: Logger):
- with wandb.init() as run:
- "Create a wandb.Table and logs it"
- columns = ['image', 'ground truth', 'prediction']
- table = wandb.Table(columns=columns, data=self.data[:self.num_samples])
- run.log({'sample_table':table}, step=int(state.timer.batch))
-...
-
-trainer = Trainer(
- ...
- loggers=[WandBLogger()],
- callbacks=[LogPredictions()]
-)
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/databricks.md b/content/en/guides/integrations/databricks.md
deleted file mode 100644
index 6ff51d751e..0000000000
--- a/content/en/guides/integrations/databricks.md
+++ /dev/null
@@ -1,65 +0,0 @@
----
-description: How to integrate W&B with Databricks.
-menu:
- default:
- identifier: databricks
- parent: integrations
-title: Databricks
-weight: 50
----
-
-W&B integrates with [Databricks](https://www.databricks.com/) by customizing the W&B Jupyter notebook experience in the Databricks environment.
-
-## Configure Databricks
-
-1. Install wandb in the cluster
-
- Navigate to your cluster configuration, choose your cluster, click **Libraries**. Click **Install New**, choose **PyPI**, and add the package `wandb`.
-
-2. Set up authentication
-
- To authenticate your W&B account you can add a Databricks secret which your notebooks can query.
-
- ```bash
- # install databricks cli
- pip install databricks-cli
-
- # Generate a token from databricks UI
- databricks configure --token
-
- # Create a scope with one of the two commands (depending if you have security features enabled on databricks):
- # with security add-on
- databricks secrets create-scope --scope wandb
- # without security add-on
- databricks secrets create-scope --scope wandb --initial-manage-principal users
-
- # Add your api_key from: https://app.wandb.ai/authorize
- databricks secrets put --scope wandb --key api_key
- ```
-
-## Examples
-
-### Simple example
-
-```python
-import os
-import wandb
-
-api_key = dbutils.secrets.get("wandb", "api_key")
-wandb.login(key=api_key)
-
-with wandb.init() as run:
- run.log({"foo": 1})
-```
-
-### Sweeps
-
-Setup required (temporary) for notebooks attempting to use wandb.sweep() or wandb.agent():
-
-```python
-import os
-
-# These will not be necessary in the future
-os.environ["WANDB_ENTITY"] = "my-entity"
-os.environ["WANDB_PROJECT"] = "my-project-that-exists"
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/deepchecks.md b/content/en/guides/integrations/deepchecks.md
deleted file mode 100644
index fac9bcc7bd..0000000000
--- a/content/en/guides/integrations/deepchecks.md
+++ /dev/null
@@ -1,59 +0,0 @@
----
-description: How to integrate W&B with DeepChecks.
-menu:
- default:
- identifier: deepchecks
- parent: integrations
-title: DeepChecks
-weight: 60
----
-{{< cta-button colabLink="https://colab.research.google.com/github/deepchecks/deepchecks/blob/0.5.0-1-g5380093/docs/source/examples/guides/export_outputs_to_wandb.ipynb" >}}
-
-DeepChecks helps you validate your machine learning models and data, such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models, all with minimal effort.
-
-[Read more about DeepChecks and the wandb integration ->](https://docs.deepchecks.com/stable/general/usage/exporting_results/auto_examples/plot_exports_output_to_wandb.html)
-
-## Getting Started
-
-To use DeepChecks with W&B you will first need to sign up for a [W&B account](https://wandb.ai/site). With the W&B integration in DeepChecks you can quickly get started like so:
-
-```python
-import wandb
-
-wandb.login()
-
-# import your check from deepchecks
-from deepchecks.checks import ModelErrorAnalysis
-
-# run your check
-result = ModelErrorAnalysis()
-
-# push that result to wandb
-result.to_wandb()
-```
-
-You can also log an entire DeepChecks test suite to W&B.
-
-```python
-import wandb
-
-wandb.login()
-
-# import your full_suite tests from deepchecks
-from deepchecks.suites import full_suite
-
-# create and run a DeepChecks test suite
-suite_result = full_suite().run(...)
-
-# push thes results to wandb
-# here you can pass any wandb.init configs and arguments you need
-suite_result.to_wandb(project="my-suite-project", config={"suite-name": "full-suite"})
-```
-
-## Example
-
-[This Report](https://wandb.ai/cayush/deepchecks/reports/Validate-your-Data-and-Models-with-Deepchecks-and-W-B--VmlldzoxNjY0ODc5) shows off the power of using DeepChecks and W&B.
-
-{{< img src="/images/integrations/deepchecks_example.png" alt="Deepchecks data validation results" >}}
-
-Any questions or issues about this W&B integration? Open an issue in the [DeepChecks github repository](https://github.com/deepchecks/deepchecks) and we'll catch it and get you an answer.
\ No newline at end of file
diff --git a/content/en/guides/integrations/deepchem.md b/content/en/guides/integrations/deepchem.md
deleted file mode 100644
index 120cb3ee2d..0000000000
--- a/content/en/guides/integrations/deepchem.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-description: How to integrate W&B with DeepChem library.
-menu:
- default:
- identifier: deepchem
- parent: integrations
-title: DeepChem
-weight: 70
----
-The [DeepChem library](https://github.com/deepchem/deepchem) provides open source tools that democratize the use of deep-learning in drug discovery, materials science, chemistry, and biology. This W&B integration adds simple and easy-to-use experiment tracking and model checkpointing while training models using DeepChem.
-
-## DeepChem logging in 3 lines of code
-
-```python
-logger = WandbLogger(…)
-model = TorchModel(…, wandb_logger=logger)
-model.fit(…)
-```
-
-{{< img src="/images/integrations/cd.png" alt="DeepChem molecular analysis" >}}
-
-## Report and Google Colab
-
-Explore the Using [W&B with DeepChem: Molecular Graph Convolutional Networks](https://wandb.ai/kshen/deepchem_graphconv/reports/Using-W-B-with-DeepChem-Molecular-Graph-Convolutional-Networks--Vmlldzo4MzU5MDc?galleryTag=) article for an example charts generated using the W&B DeepChem integration.
-
-To dive straight into working code, check out this [Google Colab](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/deepchem/W%26B_x_DeepChem.ipynb).
-
-## Track experiments
-
-Set up W&B for DeepChem models of type [KerasModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#keras-models) or [TorchModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#pytorch-models).
-
-### Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python-notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-### Log your training and evaluation data to W&B
-
-Training loss and evaluation metrics can be automatically logged to W&B. Optional evaluation can be enabled using the DeepChem [ValidationCallback](https://github.com/deepchem/deepchem/blob/master/deepchem/models/callbacks.py), the `WandbLogger` will detect ValidationCallback callback and log the metrics generated.
-
-{{< tabpane text=true >}}
-
-{{% tab header="TorchModel" value="torch" %}}
-
-```python
-from deepchem.models import TorchModel, ValidationCallback
-
-vc = ValidationCallback(…) # optional
-model = TorchModel(…, wandb_logger=logger)
-model.fit(…, callbacks=[vc])
-logger.finish()
-```
-
-{{% /tab %}}
-
-{{% tab header="KerasModel" value="keras" %}}
-
-```python
-from deepchem.models import KerasModel, ValidationCallback
-
-vc = ValidationCallback(…) # optional
-model = KerasModel(…, wandb_logger=logger)
-model.fit(…, callbacks=[vc])
-logger.finish()
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
diff --git a/content/en/guides/integrations/diffusers.md b/content/en/guides/integrations/diffusers.md
deleted file mode 100644
index b3060aa437..0000000000
--- a/content/en/guides/integrations/diffusers.md
+++ /dev/null
@@ -1,300 +0,0 @@
----
-menu:
- default:
- identifier: diffusers
- parent: integrations
-title: Hugging Face Diffusers
-weight: 120
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/diffusers/lcm-diffusers.ipynb" >}}
-
-[Hugging Face Diffusers](https://huggingface.co/docs/diffusers) is the go-to library for state-of-the-art pre-trained diffusion models for generating images, audio, and even 3D structures of molecules. The W&B integration adds rich, flexible experiment tracking, media visualization, pipeline architecture, and configuration management to interactive centralized dashboards without compromising that ease of use.
-
-## Next-level logging in just two lines
-
-Log all the prompts, negative prompts, generated media, and configs associated with your experiment by simply including 2 lines of code. Here are the 2 lines of code to begin logging:
-
-```python
-# import the autolog function
-from wandb.integration.diffusers import autolog
-
-# call the autolog before calling the pipeline
-autolog(init=dict(project="diffusers_logging"))
-```
-
-| {{< img src="/images/integrations/diffusers-autolog-4.gif" alt="Experiment results logging" >}} |
-|:--:|
-| **An example of how the results of your experiment are logged.** |
-
-## Get started
-
-1. Install `diffusers`, `transformers`, `accelerate`, and `wandb`.
-
- - Command line:
-
- ```shell
- pip install --upgrade diffusers transformers accelerate wandb
- ```
-
- - Notebook:
-
- ```bash
- !pip install --upgrade diffusers transformers accelerate wandb
- ```
-
-
-2. Use `autolog` to initialize a W&B Run and automatically track the inputs and the outputs from [all supported pipeline calls](https://github.com/wandb/wandb/blob/main/wandb/integration/diffusers/autologger.py#L12-L72).
-
- You can call the `autolog()` function with the `init` parameter, which accepts a dictionary of parameters required by [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}).
-
- When you call `autolog()`, it initializes a W&B Run and automatically tracks the inputs and the outputs from [all supported pipeline calls](https://github.com/wandb/wandb/blob/main/wandb/integration/diffusers/autologger.py#L12-L72).
-
- - Each pipeline call is tracked into its own [table]({{< relref "/guides/models/tables/" >}}) in the workspace, and the configs associated with the pipeline call is appended to the list of workflows in the configs for that run.
- - The prompts, negative prompts, and the generated media are logged in a [`wandb.Table`]({{< relref "/guides/models/tables/" >}}).
- - All other configs associated with the experiment including seed and the pipeline architecture are stored in the config section for the run.
- - The generated media for each pipeline call are also logged in [media panels]({{< relref "/guides/models/track/log/media" >}}) in the run.
-
- {{% alert %}}
- You can find a [list of supported pipeline calls](https://github.com/wandb/wandb/blob/main/wandb/integration/diffusers/autologger.py#L12-L72). In case, you want to request a new feature of this integration or report a bug associated with it, open an issue on the [W&B GitHub issues page](https://github.com/wandb/wandb/issues).
- {{% /alert %}}
-
-## Examples
-
-### Autologging
-
-Here is a brief end-to-end example of the autolog in action:
-
-{{< tabpane text=true >}}
-{{% tab header="Script" value="script" %}}
-```python
-import torch
-from diffusers import DiffusionPipeline
-
-# import the autolog function
-from wandb.integration.diffusers import autolog
-
-# call the autolog before calling the pipeline
-autolog(init=dict(project="diffusers_logging"))
-
-# Initialize the diffusion pipeline
-pipeline = DiffusionPipeline.from_pretrained(
- "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
-).to("cuda")
-
-# Define the prompts, negative prompts, and seed.
-prompt = ["a photograph of an astronaut riding a horse", "a photograph of a dragon"]
-negative_prompt = ["ugly, deformed", "ugly, deformed"]
-generator = torch.Generator(device="cpu").manual_seed(10)
-
-# call the pipeline to generate the images
-images = pipeline(
- prompt,
- negative_prompt=negative_prompt,
- num_images_per_prompt=2,
- generator=generator,
-)
-```
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook"%}}
-```python
-import torch
-from diffusers import DiffusionPipeline
-
-import wandb
-
-# import the autolog function
-from wandb.integration.diffusers import autolog
-
-run = wandb.init()
-
-# call the autolog before calling the pipeline
-autolog(init=dict(project="diffusers_logging"))
-
-# Initialize the diffusion pipeline
-pipeline = DiffusionPipeline.from_pretrained(
- "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
-).to("cuda")
-
-# Define the prompts, negative prompts, and seed.
-prompt = ["a photograph of an astronaut riding a horse", "a photograph of a dragon"]
-negative_prompt = ["ugly, deformed", "ugly, deformed"]
-generator = torch.Generator(device="cpu").manual_seed(10)
-
-# call the pipeline to generate the images
-images = pipeline(
- prompt,
- negative_prompt=negative_prompt,
- num_images_per_prompt=2,
- generator=generator,
-)
-
-# Finish the experiment
-run.finish()
-```
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-- The results of a single experiment:
-
- {{< img src="/images/integrations/diffusers-autolog-2.gif" alt="Experiment results logging" >}}
-
-- The results of multiple experiments:
-
- {{< img src="/images/integrations/diffusers-autolog-1.gif" alt="Experiment results logging" >}}
-
-- The config of an experiment:
-
- {{< img src="/images/integrations/diffusers-autolog-3.gif" alt="Experiment config logging" >}}
-
-{{% alert %}}
-You need to explicitly call [`wandb.Run.finish()`]({{< relref "/ref/python/functions/finish.md" >}}) when executing the code in IPython notebook environments after calling the pipeline. This is not necessary when executing python scripts.
-{{% /alert %}}
-
-### Tracking multi-pipeline workflows
-
-This section demonstrates the autolog with a typical [Stable Diffusion XL + Refiner](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#base-to-refiner-model) workflow, in which the latents generated by the [`StableDiffusionXLPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl) is refined by the corresponding refiner.
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/diffusers/sdxl-diffusers.ipynb" >}}
-
-{{< tabpane text=true >}}
-
-{{% tab header="Python Script" value="script" %}}
-
-```python
-import torch
-from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline
-from wandb.integration.diffusers import autolog
-
-# initialize the SDXL base pipeline
-base_pipeline = StableDiffusionXLPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-base-1.0",
- torch_dtype=torch.float16,
- variant="fp16",
- use_safetensors=True,
-)
-base_pipeline.enable_model_cpu_offload()
-
-# initialize the SDXL refiner pipeline
-refiner_pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-refiner-1.0",
- text_encoder_2=base_pipeline.text_encoder_2,
- vae=base_pipeline.vae,
- torch_dtype=torch.float16,
- use_safetensors=True,
- variant="fp16",
-)
-refiner_pipeline.enable_model_cpu_offload()
-
-prompt = "a photo of an astronaut riding a horse on mars"
-negative_prompt = "static, frame, painting, illustration, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, deformed toes standing still, posing"
-
-# Make the experiment reproducible by controlling randomness.
-# The seed would be automatically logged to WandB.
-seed = 42
-generator_base = torch.Generator(device="cuda").manual_seed(seed)
-generator_refiner = torch.Generator(device="cuda").manual_seed(seed)
-
-# Call WandB Autolog for Diffusers. This would automatically log
-# the prompts, generated images, pipeline architecture and all
-# associated experiment configs to W&B, thus making your
-# image generation experiments easy to reproduce, share and analyze.
-autolog(init=dict(project="sdxl"))
-
-# Call the base pipeline to generate the latents
-image = base_pipeline(
- prompt=prompt,
- negative_prompt=negative_prompt,
- output_type="latent",
- generator=generator_base,
-).images[0]
-
-# Call the refiner pipeline to generate the refined image
-image = refiner_pipeline(
- prompt=prompt,
- negative_prompt=negative_prompt,
- image=image[None, :],
- generator=generator_refiner,
-).images[0]
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-```python
-import torch
-from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline
-
-import wandb
-from wandb.integration.diffusers import autolog
-
-run = wandb.init()
-
-# initialize the SDXL base pipeline
-base_pipeline = StableDiffusionXLPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-base-1.0",
- torch_dtype=torch.float16,
- variant="fp16",
- use_safetensors=True,
-)
-base_pipeline.enable_model_cpu_offload()
-
-# initialize the SDXL refiner pipeline
-refiner_pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-refiner-1.0",
- text_encoder_2=base_pipeline.text_encoder_2,
- vae=base_pipeline.vae,
- torch_dtype=torch.float16,
- use_safetensors=True,
- variant="fp16",
-)
-refiner_pipeline.enable_model_cpu_offload()
-
-prompt = "a photo of an astronaut riding a horse on mars"
-negative_prompt = "static, frame, painting, illustration, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, deformed toes standing still, posing"
-
-# Make the experiment reproducible by controlling randomness.
-# The seed would be automatically logged to WandB.
-seed = 42
-generator_base = torch.Generator(device="cuda").manual_seed(seed)
-generator_refiner = torch.Generator(device="cuda").manual_seed(seed)
-
-# Call WandB Autolog for Diffusers. This would automatically log
-# the prompts, generated images, pipeline architecture and all
-# associated experiment configs to W&B, thus making your
-# image generation experiments easy to reproduce, share and analyze.
-autolog(init=dict(project="sdxl"))
-
-# Call the base pipeline to generate the latents
-image = base_pipeline(
- prompt=prompt,
- negative_prompt=negative_prompt,
- output_type="latent",
- generator=generator_base,
-).images[0]
-
-# Call the refiner pipeline to generate the refined image
-image = refiner_pipeline(
- prompt=prompt,
- negative_prompt=negative_prompt,
- image=image[None, :],
- generator=generator_refiner,
-).images[0]
-
-# Finish the experiment
-run.finish()
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-- Example of a Stable Diffisuion XL + Refiner experiment:
- {{< img src="/images/integrations/diffusers-autolog-6.gif" alt="Stable Diffusion XL experiment tracking" >}}
-
-## More resources
-
-* [A Guide to Prompt Engineering for Stable Diffusion](https://wandb.ai/geekyrakshit/diffusers-prompt-engineering/reports/A-Guide-to-Prompt-Engineering-for-Stable-Diffusion--Vmlldzo1NzY4NzQ3)
-* [PIXART-α: A Diffusion Transformer Model for Text-to-Image Generation](https://wandb.ai/geekyrakshit/pixart-alpha/reports/PIXART-A-Diffusion-Transformer-Model-for-Text-to-Image-Generation--Vmlldzo2MTE1NzM3)
diff --git a/content/en/guides/integrations/docker.md b/content/en/guides/integrations/docker.md
deleted file mode 100644
index 2d662987f3..0000000000
--- a/content/en/guides/integrations/docker.md
+++ /dev/null
@@ -1,29 +0,0 @@
----
-description: How to integrate W&B with Docker.
-menu:
- default:
- identifier: docker
- parent: integrations
-title: Docker
-weight: 80
----
-
-## Docker Integration
-
-W&B can store a pointer to the Docker image that your code ran in, giving you the ability to restore a previous experiment to the exact environment it was run in. The wandb library looks for the **WANDB_DOCKER** environment variable to persist this state. We provide a few helpers that automatically set this state.
-
-### Local Development
-
-`wandb docker` is a command that starts a docker container, passes in wandb environment variables, mounts your code, and ensures wandb is installed. By default the command uses a docker image with TensorFlow, PyTorch, Keras, and Jupyter installed. You can use the same command to start your own docker image: `wandb docker my/image:latest`. The command mounts the current directory into the "/app" directory of the container, you can change this with the "--dir" flag.
-
-### Production
-
-The `wandb docker-run` command is provided for production workloads. It's meant to be a drop in replacement for `nvidia-docker`. It's a simple wrapper to the `docker run` command that adds your credentials and the **WANDB_DOCKER** environment variable to the call. If you do not pass the "--runtime" flag and `nvidia-docker` is available on the machine, this also ensures the runtime is set to nvidia.
-
-### Kubernetes
-
-If you run your training workloads in Kubernetes and the k8s API is exposed to your pod \(which is the case by default\). wandb will query the API for the digest of the docker image and automatically set the **WANDB_DOCKER** environment variable.
-
-## Restoring
-
-If a run was instrumented with the **WANDB_DOCKER** environment variable, calling `wandb restore username/project:run_id` will checkout a new branch restoring your code then launch the exact docker image used for training pre-populated with the original command.
\ No newline at end of file
diff --git a/content/en/guides/integrations/dspy.md b/content/en/guides/integrations/dspy.md
deleted file mode 100644
index d31bf161ea..0000000000
--- a/content/en/guides/integrations/dspy.md
+++ /dev/null
@@ -1,199 +0,0 @@
----
-description: Track and optimize DSPy programs with W&B.
-menu:
- default:
- identifier: dspy
- parent: integrations
-title: DSPy
-weight: 80
----
-
-
-Use W&B with DSPy to track and optimize your language model programs. W&B complements the [Weave DSPy integration](https://weave-docs.wandb.ai/guides/integrations/dspy) by providing:
-
-- Evaluation metrics tracking over time
-- W&B Tables for program signature evolution
-- Integration with DSPy optimizers like MIPROv2
-
-For comprehensive observability when optimizing DSPy modules, enable the integration in both W&B and Weave.
-
-{{< alert title="Note" color="info" >}}
-As of `wandb==0.21.2` and `weave==0.52.5`, Weave initializes automatically when used with W&B:
-
-- If `weave` is imported and then `wandb.init()` is called (script case)
-- If `wandb.init()` was called and then `weave` is imported later (notebook/Jupyter case)
-
-No explicit `weave.init(...)` call is required.
-{{< /alert >}}
-
-## Install and authenticate
-
-Install the required libraries and authenticate with W&B:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" %}}
-
-1. Install the required libraries:
-
- ```shell
- pip install wandb weave dspy
- ```
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) and log in:
-
- ```bash
- export WANDB_API_KEY=
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" %}}
-1. Install the required libraries:
-
- ```bash
- pip install wandb weave dspy
- ```
-1. In your code, log in to W&B:
-
- ```python
- import wandb
- wandb.login()
- ```
-{{% /tab %}}
-
-{{% tab header="Notebook" %}}
-Install and import the required libraries, then log in to W&B:
-```notebook
-!pip install wandb weave dspy
-
-import wandb
-wandb.login()
-```
-{{% /tab %}}
-{{< /tabpane >}}
-
-New to W&B? See our [quickstart guide]({{< relref "/guides/quickstart.md" >}}).
-
-
-## Track program optimization (experimental) {#track-program-optimization}
-
-
-For DSPy optimizers that use `dspy.Evaluate` (such as MIPROv2), use the `WandbDSPyCallback` to log evaluation metrics over time and track program signature evolution in W&B Tables.
-
-```python
-import dspy
-from dspy.datasets import MATH
-
-import weave
-import wandb
-from wandb.integration.dspy import WandbDSPyCallback
-
-# Initialize W&B (importing weave is sufficient; no explicit weave.init needed)
-project_name = "dspy-optimization"
-with wandb.init(project=project_name) as run:
- # Add W&B callback to DSPy
- dspy.settings.callbacks.append(
- WandbDSPyCallback(run=run)
- )
-
- # Configure language models
- teacher_lm = dspy.LM('openai/gpt-4o', max_tokens=2000, cache=True)
- student_lm = dspy.LM('openai/gpt-4o-mini', max_tokens=2000)
- dspy.configure(lm=student_lm)
-
- # Load dataset and define program
- dataset = MATH(subset='algebra')
- program = dspy.ChainOfThought("question -> answer")
-
- # Configure and run optimizer
- optimizer = dspy.MIPROv2(
- metric=dataset.metric,
- auto="light",
- num_threads=24,
- teacher_settings=dict(lm=teacher_lm),
- prompt_model=student_lm
- )
-
- optimized_program = optimizer.compile(
- program,
- trainset=dataset.train,
- max_bootstrapped_demos=2,
- max_labeled_demos=2
- )
-```
-
-After running this code, you receive both a W&B Run URL and a Weave URL. W&B displays evaluation metrics over time, along with Tables that show the evolution of program signatures. The run's **Overview** tab includes links to Weave traces for detailed inspection.
-
-If a `run` object is not passed to `WandbDSPyCallback`, the global `run` object is used.
-
-{{< img src="/images/integrations/dspy_run_page.png" alt="DSPy optimization run in W&B" >}}
-
-For comprehensive details about Weave tracing, evaluation, and optimization with DSPy, see the [Weave DSPy integration guide](https://weave-docs.wandb.ai/guides/integrations/dspy).
-
-## Log predictions to W&B Tables
-
-Enable detailed prediction logging to inspect individual examples during optimization. The callback creates a W&B Tables for each evaluation step, which can help you to analyze specific successes and failures.
-
-```python
-from wandb.integration.dspy import WandbDSPyCallback
-
-# Enable prediction logging (enabled by default)
-callback = WandbDSPyCallback(log_results=True)
-dspy.settings.callbacks.append(callback)
-
-# Run your optimization
-optimized_program = optimizer.compile(program, trainset=train_data)
-
-# Disable prediction logging if needed
-# callback = WandbDSPyCallback(log_results=False)
-```
-
-### Access prediction data
-
-After optimization, find your prediction data in W&B:
-
-1. Navigate to your run's **Overview** page.
-2. Look for Table panels named with a pattern like `predictions_0`, `predictions_1`, and so forth.
-3. Filter by `is_correct` to analyze failures.
-4. Compare tables across runs in the project workspace.
-
-Each table includes columns for:
-- `example`: Input data
-- `prediction`: Model output
-- `is_correct`: Evaluation result
-
-Learn more in the [W&B Tables guide](../models/tables/visualize-tables.md) and the [Tables tutorial](../../tutorials/tables.md).
-
-## Save and version DSPy programs
-
-To reproduce and version your best DSpy programs, save them as W&B Artifacts. Choose between saving the complete program or only the state.
-
-```python
-from wandb.integration.dspy import WandbDSPyCallback
-
-# Create callback instance
-callback = WandbDSPyCallback()
-dspy.settings.callbacks.append(callback)
-
-# Run optimization
-optimized_program = optimizer.compile(program, trainset=train_data)
-
-# Save options:
-
-# 1. Complete program (recommended) - includes architecture and state
-callback.log_best_model(optimized_program, save_program=True)
-
-# 2. State only as JSON - lighter weight, human-readable
-callback.log_best_model(optimized_program, save_program=False, filetype="json")
-
-# 3. State only as pickle - preserves Python objects
-callback.log_best_model(optimized_program, save_program=False, filetype="pkl")
-
-# Add custom aliases for versioning
-callback.log_best_model(
- optimized_program,
- save_program=True,
- aliases=["best", "production", "v2.0"]
-)
-```
diff --git a/content/en/guides/integrations/farama-gymnasium.md b/content/en/guides/integrations/farama-gymnasium.md
deleted file mode 100644
index a47b6a07e3..0000000000
--- a/content/en/guides/integrations/farama-gymnasium.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-description: How to integrate W&B with Farama Gymnasium.
-menu:
- default:
- identifier: farama-gymnasium
- parent: integrations
-title: Farama Gymnasium
-weight: 90
----
-
-If you're using [Farama Gymnasium](https://gymnasium.farama.org/#) we will automatically log videos of your environment generated by `gymnasium.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`]({{< relref "/ref/python/functions/init.md" >}}) to `True`.
-
-Our gymnasium integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/c5fe3d56b155655980611d32ef09df35cd336872/wandb/integration/gym/__init__.py#LL69C67-L69C67) being logged from `gymnasium` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video]({{< relref "/guides/models/track/log/media.md" >}}).
-
-Check out this [report](https://wandb.ai/raph-test/cleanrltest/reports/Mario-Bros-but-with-AI-Gymnasium-and-CleanRL---Vmlldzo0NTcxNTcw) to learn more on how to use Gymnasium with the CleanRL library.
-
-{{< img src="/images/integrations/gymnasium.png" alt="Gymnasium RL environment" >}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/fastai/_index.md b/content/en/guides/integrations/fastai/_index.md
deleted file mode 100644
index f1b4adc8e8..0000000000
--- a/content/en/guides/integrations/fastai/_index.md
+++ /dev/null
@@ -1,278 +0,0 @@
----
-menu:
- default:
- identifier: README
- parent: integrations
-title: fastai
-cascade:
-- url: guides/integrations/fastai/:filename
-weight: 100
----
-If you're using **fastai** to train your models, W&B has an easy integration using the `WandbCallback`. Explore the details in[ interactive docs with examples →](https://app.wandb.ai/borisd13/demo_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA)
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python-notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Add the `WandbCallback` to the `learner` or `fit` method
-
-```python
-import wandb
-from fastai.callback.wandb import *
-
-# start logging a wandb run
-wandb.init(project="my_project")
-
-# To log only during one training phase
-learn.fit(..., cbs=WandbCallback())
-
-# To log continuously for all training phases
-learn = learner(..., cbs=WandbCallback())
-```
-
-{{% alert %}}
-If you use version 1 of Fastai, refer to the [Fastai v1 docs]({{< relref "v1.md" >}}).
-{{% /alert %}}
-
-## WandbCallback Arguments
-
-`WandbCallback` accepts the following arguments:
-
-| Args | Description |
-| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| log | Whether to log the model's: `gradients` , `parameters`, `all` or `None` (default). Losses & metrics are always logged. |
-| log_preds | whether we want to log prediction samples (default to `True`). |
-| log_preds_every_epoch | whether to log predictions every epoch or at the end (default to `False`) |
-| log_model | whether we want to log our model (default to False). This also requires `SaveModelCallback` |
-| model_name | The name of the `file` to save, overrides `SaveModelCallback` |
-| log_dataset |
False (default)
True will log folder referenced by learn.dls.path.
a path can be defined explicitly to reference which folder to log.
Note: subfolder "models" is always ignored.
|
-| dataset_name | name of logged dataset (default to `folder name`). |
-| valid_dl | `DataLoaders` containing items used for prediction samples (default to random items from `learn.dls.valid`. |
-| n_preds | number of logged predictions (default to 36). |
-| seed | used for defining random samples. |
-
-For custom workflows, you can manually log your datasets and models:
-
-* `log_dataset(path, name=None, metadata={})`
-* `log_model(path, name=None, metadata={})`
-
-_Note: any subfolder "models" will be ignored._
-
-## Distributed Training
-
-`fastai` supports distributed training by using the context manager `distrib_ctx`. W&B supports this automatically and enables you to track your Multi-GPU experiments out of the box.
-
-Review this minimal example:
-
-{{< tabpane text=true >}}
-{{% tab header="Script" value="script" %}}
-
-```python
-import wandb
-from fastai.vision.all import *
-from fastai.distributed import *
-from fastai.callback.wandb import WandbCallback
-
-wandb.require(experiment="service")
-path = rank0_first(lambda: untar_data(URLs.PETS) / "images")
-
-def train():
- dls = ImageDataLoaders.from_name_func(
- path,
- get_image_files(path),
- valid_pct=0.2,
- label_func=lambda x: x[0].isupper(),
- item_tfms=Resize(224),
- )
- wandb.init("fastai_ddp", entity="capecape")
- cb = WandbCallback()
- learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
- with learn.distrib_ctx(sync_bn=False):
- learn.fit(1)
-
-if __name__ == "__main__":
- train()
-```
-
-Then, in your terminal you will execute:
-
-```shell
-$ torchrun --nproc_per_node 2 train.py
-```
-
-in this case, the machine has 2 GPUs.
-
-{{% /tab %}}
-{{% tab header="Python notebook" value="notebook" %}}
-
-You can now run distributed training directly inside a notebook.
-
-```python
-import wandb
-from fastai.vision.all import *
-
-from accelerate import notebook_launcher
-from fastai.distributed import *
-from fastai.callback.wandb import WandbCallback
-
-wandb.require(experiment="service")
-path = untar_data(URLs.PETS) / "images"
-
-def train():
- dls = ImageDataLoaders.from_name_func(
- path,
- get_image_files(path),
- valid_pct=0.2,
- label_func=lambda x: x[0].isupper(),
- item_tfms=Resize(224),
- )
- wandb.init("fastai_ddp", entity="capecape")
- cb = WandbCallback()
- learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
- with learn.distrib_ctx(in_notebook=True, sync_bn=False):
- learn.fit(1)
-
-notebook_launcher(train, num_processes=2)
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-### Log only on the main process
-
-In the examples above, `wandb` launches one run per process. At the end of the training, you will end up with two runs. This can sometimes be confusing, and you may want to log only on the main process. To do so, you will have to detect in which process you are manually and avoid creating runs (calling `wandb.init` in all other processes)
-
-{{< tabpane text=true >}}
-{{% tab header="Script" value="script" %}}
-
-```python
-import wandb
-from fastai.vision.all import *
-from fastai.distributed import *
-from fastai.callback.wandb import WandbCallback
-
-wandb.require(experiment="service")
-path = rank0_first(lambda: untar_data(URLs.PETS) / "images")
-
-def train():
- cb = []
- dls = ImageDataLoaders.from_name_func(
- path,
- get_image_files(path),
- valid_pct=0.2,
- label_func=lambda x: x[0].isupper(),
- item_tfms=Resize(224),
- )
- if rank_distrib() == 0:
- run = wandb.init("fastai_ddp", entity="capecape")
- cb = WandbCallback()
- learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
- with learn.distrib_ctx(sync_bn=False):
- learn.fit(1)
-
-if __name__ == "__main__":
- train()
-```
-in your terminal call:
-
-```
-$ torchrun --nproc_per_node 2 train.py
-```
-
-{{% /tab %}}
-{{% tab header="Python notebook" value="notebook" %}}
-
-```python
-import wandb
-from fastai.vision.all import *
-
-from accelerate import notebook_launcher
-from fastai.distributed import *
-from fastai.callback.wandb import WandbCallback
-
-wandb.require(experiment="service")
-path = untar_data(URLs.PETS) / "images"
-
-def train():
- cb = []
- dls = ImageDataLoaders.from_name_func(
- path,
- get_image_files(path),
- valid_pct=0.2,
- label_func=lambda x: x[0].isupper(),
- item_tfms=Resize(224),
- )
- if rank_distrib() == 0:
- run = wandb.init("fastai_ddp", entity="capecape")
- cb = WandbCallback()
- learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
- with learn.distrib_ctx(in_notebook=True, sync_bn=False):
- learn.fit(1)
-
-notebook_launcher(train, num_processes=2)
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Examples
-
-* [Visualize, track, and compare Fastai models](https://app.wandb.ai/borisd13/demo_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA): A thoroughly documented walkthrough.
-* [Image Segmentation on CamVid](https://bit.ly/fastai-wandb): A sample use case of the integration.
\ No newline at end of file
diff --git a/content/en/guides/integrations/fastai/v1.md b/content/en/guides/integrations/fastai/v1.md
deleted file mode 100644
index 540d41d47d..0000000000
--- a/content/en/guides/integrations/fastai/v1.md
+++ /dev/null
@@ -1,71 +0,0 @@
----
-menu:
- default:
- identifier: v1
- parent: fastai
-title: fastai v1
----
-
-{{% alert %}}
-This documentation is for fastai v1.
-If you use the current version of fastai, you should refer to [fastai page]({{< relref "../" >}}).
-{{% /alert %}}
-
-For scripts using fastai v1, we have a callback that can automatically log model topology, losses, metrics, weights, gradients, sample predictions and best trained model.
-
-```python
-import wandb
-from wandb.fastai import WandbCallback
-
-wandb.init()
-
-learn = cnn_learner(data, model, callback_fns=WandbCallback)
-learn.fit(epochs)
-```
-
-Requested logged data is configurable through the callback constructor.
-
-```python
-from functools import partial
-
-learn = cnn_learner(
- data, model, callback_fns=partial(WandbCallback, input_type="images")
-)
-```
-
-It is also possible to use WandbCallback only when starting training. In this case it must be instantiated.
-
-```python
-learn.fit(epochs, callbacks=WandbCallback(learn))
-```
-
-Custom parameters can also be given at that stage.
-
-```python
-learn.fit(epochs, callbacks=WandbCallback(learn, input_type="images"))
-```
-
-## Example Code
-
-We've created a few examples for you to see how the integration works:
-
-**Fastai v1**
-
-* [Classify Simpsons characters](https://github.com/borisdayma/simpsons-fastai)[: ](https://app.wandb.ai/jxmorris12/huggingface-demo/reports/A-Step-by-Step-Guide-to-Tracking-Hugging-Face-Model-Performance--VmlldzoxMDE2MTU)A simple demo to track and compare Fastai models
-* [Semantic Segmentation with Fastai](https://github.com/borisdayma/semantic-segmentation): Optimize neural networks on self-driving cars
-
-## Options
-
-`WandbCallback()` class supports a number of options:
-
-| Keyword argument | Default | Description |
-| ---------------- | --------- | -------------------------------------------------------------------------------------------------------- |
-| learn | N/A | the fast.ai learner to hook. |
-| save_model | True | save the model if it's improved at each step. It will also load best model at the end of training. |
-| mode | auto | `min`, `max`, or `auto`: How to compare the training metric specified in `monitor` between steps. |
-| monitor | None | training metric used to measure performance for saving the best model. None defaults to validation loss. |
-| log | gradients | `gradients`, `parameters`, `all`, or None. Losses & metrics are always logged. |
-| input_type | None | `images` or `None`. Used to display sample predictions. |
-| validation_data | None | data used for sample predictions if `input_type` is set. |
-| predictions | 36 | number of predictions to make if `input_type` is set and `validation_data` is `None`. |
-| seed | 12345 | initialize random generator for sample predictions if `input_type` is set and `validation_data` is `None`. |
\ No newline at end of file
diff --git a/content/en/guides/integrations/huggingface.md b/content/en/guides/integrations/huggingface.md
deleted file mode 100644
index 944c5a4c49..0000000000
--- a/content/en/guides/integrations/huggingface.md
+++ /dev/null
@@ -1,578 +0,0 @@
----
-menu:
- default:
- identifier: huggingface
- parent: integrations
-title: Hugging Face Transformers
-weight: 110
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_&_Biases.ipynb" >}}
-
-The [Hugging Face Transformers](https://huggingface.co/transformers/) library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. The [W&B integration](https://huggingface.co/transformers/main_classes/callback.html#transformers.integrations.WandbCallback) adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use.
-
-## Next-level logging in few lines
-
-```python
-os.environ["WANDB_PROJECT"] = "" # name your W&B project
-os.environ["WANDB_LOG_MODEL"] = "checkpoint" # log all model checkpoints
-
-from transformers import TrainingArguments, Trainer
-
-args = TrainingArguments(..., report_to="wandb") # turn on W&B logging
-trainer = Trainer(..., args=args)
-```
-{{< img src="/images/integrations/huggingface_gif.gif" alt="HuggingFace dashboard" >}}
-
-{{% alert %}}
-If you'd rather dive straight into working code, check out this [Google Colab](https://wandb.me/hf).
-{{% /alert %}}
-
-## Get started: track experiments
-
-### Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-If you are using W&B for the first time you might want to check out our [quickstart]({{< relref "/guides/quickstart.md" >}})
-
-
-### Name the project
-
-A W&B Project is where all of the charts, data, and models logged from related runs are stored. Naming your project helps you organize your work and keep all the information about a single project in one place.
-
-To add a run to a project simply set the `WANDB_PROJECT` environment variable to the name of your project. The `WandbCallback` will pick up this project name environment variable and use it when setting up your run.
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-```bash
-WANDB_PROJECT=amazon_sentiment_analysis
-```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-import os
-os.environ["WANDB_PROJECT"]="amazon_sentiment_analysis"
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-%env WANDB_PROJECT=amazon_sentiment_analysis
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-{{% alert %}}
-Make sure you set the project name _before_ you initialize the `Trainer`.
-{{% /alert %}}
-
-If a project name is not specified the project name defaults to `huggingface`.
-
-### Log your training runs to W&B
-
-This is **the most important step** when defining your `Trainer` training arguments, either inside your code or from the command line, is to set `report_to` to `"wandb"` in order enable logging with W&B.
-
-The `logging_steps` argument in `TrainingArguments` will control how often training metrics are pushed to W&B during training. You can also give a name to the training run in W&B using the `run_name` argument.
-
-That's it. Now your models will log losses, evaluation metrics, model topology, and gradients to W&B while they train.
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-```bash
-python run_glue.py \ # run your Python script
- --report_to wandb \ # enable logging to W&B
- --run_name bert-base-high-lr \ # name of the W&B run (optional)
- # other command line arguments here
-```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-from transformers import TrainingArguments, Trainer
-
-args = TrainingArguments(
- # other args and kwargs here
- report_to="wandb", # enable logging to W&B
- run_name="bert-base-high-lr", # name of the W&B run (optional)
- logging_steps=1, # how often to log to W&B
-)
-
-trainer = Trainer(
- # other args and kwargs here
- args=args, # your training args
-)
-
-trainer.train() # start training and logging to W&B
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-{{% alert %}}
-Using TensorFlow? Just swap the PyTorch `Trainer` for the TensorFlow `TFTrainer`.
-{{% /alert %}}
-
-### Turn on model checkpointing
-
-
-Using [Artifacts]({{< relref "/guides/core/artifacts/" >}}), you can store up to 100GB of models and datasets for free and then use the W&B [Registry]({{< relref "/guides/core/registry/" >}}). Using Registry, you can register models to explore and evaluate them, prepare them for staging, or deploy them in your production environment.
-
-To log your Hugging Face model checkpoints to Artifacts, set the `WANDB_LOG_MODEL` environment variable to _one_ of:
-
-- **`checkpoint`**: Upload a checkpoint every `args.save_steps` from the [`TrainingArguments`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments).
-- **`end`**: Upload the model at the end of training, if `load_best_model_at_end` is also set.
-- **`false`**: Do not upload the model.
-
-
-{{< tabpane text=true >}}
-
-{{% tab header="Command Line" value="cli" %}}
-
-```bash
-WANDB_LOG_MODEL="checkpoint"
-```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-import os
-
-os.environ["WANDB_LOG_MODEL"] = "checkpoint"
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-%env WANDB_LOG_MODEL="checkpoint"
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-Any Transformers `Trainer` you initialize from now on will upload models to your W&B project. The model checkpoints you log will be viewable through the [Artifacts]({{< relref "/guides/core/artifacts/" >}}) UI, and include the full model lineage (see an example model checkpoint in the UI [here](https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage?_gl=1*yyql5q*_ga*MTQxOTYyNzExOS4xNjg0NDYyNzk1*_ga_JH1SJHJQXJ*MTY5MjMwNzI2Mi4yNjkuMS4xNjkyMzA5NjM2LjM3LjAuMA..)).
-
-
-{{% alert %}}
-By default, your model will be saved to W&B Artifacts as `model-{run_id}` when `WANDB_LOG_MODEL` is set to `end` or `checkpoint-{run_id}` when `WANDB_LOG_MODEL` is set to `checkpoint`.
-However, If you pass a [`run_name`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments.run_name) in your `TrainingArguments`, the model will be saved as `model-{run_name}` or `checkpoint-{run_name}`.
-{{% /alert %}}
-
-#### W&B Registry
-Once you have logged your checkpoints to Artifacts, you can then register your best model checkpoints and centralize them across your team with [Registry]({{< relref "/guides/core/registry/" >}}). Using Registry, you can organize your best models by task, manage the lifecycles of models, track and audit the entire ML lifecyle, and [automate]({{< relref "/guides/core/automations/" >}}) downstream actions.
-
-To link a model Artifact, refer to [Registry]({{< relref "/guides/core/registry/" >}}).
-
-### Visualise evaluation outputs during training
-
-Visualing your model outputs during training or evaluation is often essential to really understand how your model is training.
-
-By using the callbacks system in the Transformers Trainer, you can log additional helpful data to W&B such as your models' text generation outputs or other predictions to W&B Tables.
-
-See the [Custom logging section]({{< relref "#custom-logging-log-and-view-evaluation-samples-during-training" >}}) below for a full guide on how to log evaluation outputs while training to log to a W&B Table like this:
-
-
-{{< img src="/images/integrations/huggingface_eval_tables.png" alt="Shows a W&B Table with evaluation outputs" >}}
-
-### Finish your W&B Run (Notebook only)
-
-If your training is encapsulated in a Python script, the W&B run will end when your script finishes.
-
-If you are using a Jupyter or Google Colab notebook, you'll need to tell us when you're done with training by calling `run.finish()`.
-
-```python
-run = wandb.init()
-trainer.train() # start training and logging to W&B
-
-# post-training analysis, testing, other logged code
-
-run.finish()
-```
-
-### Visualize your results
-
-Once you have logged your training results you can explore your results dynamically in the [W&B Dashboard]({{< relref "/guides/models/track/workspaces.md" >}}). It's easy to compare across dozens of runs at once, zoom in on interesting findings, and coax insights out of complex data with flexible, interactive visualizations.
-
-## Advanced features and FAQs
-
-### How do I save the best model?
-If you pass `TrainingArguments` with `load_best_model_at_end=True` to your `Trainer`, W&B saves the best performing model checkpoint to Artifacts.
-
-If you save your model checkpoints as Artifacts, you can promote them to the [Registry]({{< relref "/guides/core/registry/" >}}). In Registry, you can:
-- Organize your best model versions by ML task.
-- Centralize models and share them with your team.
-- Stage models for production or bookmark them for further evaluation.
-- Trigger downstream CI/CD processes.
-
-### How do I load a saved model?
-
-If you saved your model to W&B Artifacts with `WANDB_LOG_MODEL`, you can download your model weights for additional training or to run inference. You just load them back into the same Hugging Face architecture that you used before.
-
-```python
-# Create a new run
-with wandb.init(project="amazon_sentiment_analysis") as run:
- # Pass the name and version of Artifact
- my_model_name = "model-bert-base-high-lr:latest"
- my_model_artifact = run.use_artifact(my_model_name)
-
- # Download model weights to a folder and return the path
- model_dir = my_model_artifact.download()
-
- # Load your Hugging Face model from that folder
- # using the same model class
- model = AutoModelForSequenceClassification.from_pretrained(
- model_dir, num_labels=num_labels
- )
-
- # Do additional training, or run inference
-```
-
-### How do I resume training from a checkpoint?
-If you had set `WANDB_LOG_MODEL='checkpoint'` you can also resume training by you can using the `model_dir` as the `model_name_or_path` argument in your `TrainingArguments` and pass `resume_from_checkpoint=True` to `Trainer`.
-
-```python
-last_run_id = "xxxxxxxx" # fetch the run_id from your wandb workspace
-
-# resume the wandb run from the run_id
-with wandb.init(
- project=os.environ["WANDB_PROJECT"],
- id=last_run_id,
- resume="must",
-) as run:
- # Connect an Artifact to the run
- my_checkpoint_name = f"checkpoint-{last_run_id}:latest"
- my_checkpoint_artifact = run.use_artifact(my_model_name)
-
- # Download checkpoint to a folder and return the path
- checkpoint_dir = my_checkpoint_artifact.download()
-
- # reinitialize your model and trainer
- model = AutoModelForSequenceClassification.from_pretrained(
- "", num_labels=num_labels
- )
- # your awesome training arguments here.
- training_args = TrainingArguments()
-
- trainer = Trainer(model=model, args=training_args)
-
- # make sure use the checkpoint dir to resume training from the checkpoint
- trainer.train(resume_from_checkpoint=checkpoint_dir)
-```
-
-### How do I log and view evaluation samples during training
-
-Logging to W&B via the Transformers `Trainer` is taken care of by the [`WandbCallback`](https://huggingface.co/transformers/main_classes/callback.html#transformers.integrations.WandbCallback) in the Transformers library. If you need to customize your Hugging Face logging you can modify this callback by subclassing `WandbCallback` and adding additional functionality that leverages additional methods from the Trainer class.
-
-Below is the general pattern to add this new callback to the HF Trainer, and further down is a code-complete example to log evaluation outputs to a W&B Table:
-
-
-```python
-# Instantiate the Trainer as normal
-trainer = Trainer()
-
-# Instantiate the new logging callback, passing it the Trainer object
-evals_callback = WandbEvalsCallback(trainer, tokenizer, ...)
-
-# Add the callback to the Trainer
-trainer.add_callback(evals_callback)
-
-# Begin Trainer training as normal
-trainer.train()
-```
-
-#### View evaluation samples during training
-
-The following section shows how to customize the `WandbCallback` to run model predictions and log evaluation samples to a W&B Table during training. We will every `eval_steps` using the `on_evaluate` method of the Trainer callback.
-
-Here, we wrote a `decode_predictions` function to decode the predictions and labels from the model output using the tokenizer.
-
-Then, we create a pandas DataFrame from the predictions and labels and add an `epoch` column to the DataFrame.
-
-Finally, we create a `wandb.Table` from the DataFrame and log it to wandb.
-Additionally, we can control the frequency of logging by logging the predictions every `freq` epochs.
-
-**Note**: Unlike the regular `WandbCallback` this custom callback needs to be added to the trainer **after** the `Trainer` is instantiated and not during initialization of the `Trainer`.
-This is because the `Trainer` instance is passed to the callback during initialization.
-
-```python
-from transformers.integrations import WandbCallback
-import pandas as pd
-
-
-def decode_predictions(tokenizer, predictions):
- labels = tokenizer.batch_decode(predictions.label_ids)
- logits = predictions.predictions.argmax(axis=-1)
- prediction_text = tokenizer.batch_decode(logits)
- return {"labels": labels, "predictions": prediction_text}
-
-
-class WandbPredictionProgressCallback(WandbCallback):
- """Custom WandbCallback to log model predictions during training.
-
- This callback logs model predictions and labels to a wandb.Table at each
- logging step during training. It allows to visualize the
- model predictions as the training progresses.
-
- Attributes:
- trainer (Trainer): The Hugging Face Trainer instance.
- tokenizer (AutoTokenizer): The tokenizer associated with the model.
- sample_dataset (Dataset): A subset of the validation dataset
- for generating predictions.
- num_samples (int, optional): Number of samples to select from
- the validation dataset for generating predictions. Defaults to 100.
- freq (int, optional): Frequency of logging. Defaults to 2.
- """
-
- def __init__(self, trainer, tokenizer, val_dataset, num_samples=100, freq=2):
- """Initializes the WandbPredictionProgressCallback instance.
-
- Args:
- trainer (Trainer): The Hugging Face Trainer instance.
- tokenizer (AutoTokenizer): The tokenizer associated
- with the model.
- val_dataset (Dataset): The validation dataset.
- num_samples (int, optional): Number of samples to select from
- the validation dataset for generating predictions.
- Defaults to 100.
- freq (int, optional): Frequency of logging. Defaults to 2.
- """
- super().__init__()
- self.trainer = trainer
- self.tokenizer = tokenizer
- self.sample_dataset = val_dataset.select(range(num_samples))
- self.freq = freq
-
- def on_evaluate(self, args, state, control, **kwargs):
- super().on_evaluate(args, state, control, **kwargs)
- # control the frequency of logging by logging the predictions
- # every `freq` epochs
- if state.epoch % self.freq == 0:
- # generate predictions
- predictions = self.trainer.predict(self.sample_dataset)
- # decode predictions and labels
- predictions = decode_predictions(self.tokenizer, predictions)
- # add predictions to a wandb.Table
- predictions_df = pd.DataFrame(predictions)
- predictions_df["epoch"] = state.epoch
- records_table = self._wandb.Table(dataframe=predictions_df)
- # log the table to wandb
- self._wandb.log({"sample_predictions": records_table})
-
-
-# First, instantiate the Trainer
-trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=lm_datasets["train"],
- eval_dataset=lm_datasets["validation"],
-)
-
-# Instantiate the WandbPredictionProgressCallback
-progress_callback = WandbPredictionProgressCallback(
- trainer=trainer,
- tokenizer=tokenizer,
- val_dataset=lm_dataset["validation"],
- num_samples=10,
- freq=2,
-)
-
-# Add the callback to the trainer
-trainer.add_callback(progress_callback)
-```
-
-For a more detailed example please refer to this [colab](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Custom_Progress_Callback.ipynb)
-
-
-### What additional W&B settings are available?
-
-Further configuration of what is logged with `Trainer` is possible by setting environment variables. A full list of W&B environment variables [can be found here]({{< relref "/guides/hosting/env-vars.md" >}}).
-
-| Environment Variable | Usage |
-| -------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `WANDB_PROJECT` | Give your project a name (`huggingface` by default) |
-| `WANDB_LOG_MODEL` |
Log the model checkpoint as a W&B Artifact (`false` by default)
false (default): No model checkpointing
checkpoint: A checkpoint will be uploaded every args.save_steps (set in the Trainer's TrainingArguments).
end: The final model checkpoint will be uploaded at the end of training.
|
-| `WANDB_WATCH` |
Set whether you'd like to log your models gradients, parameters or neither
false (default): No gradient or parameter logging
gradients: Log histograms of the gradients
all: Log histograms of gradients and parameters
|
-| `WANDB_DISABLED` | Set to `true` to turn off logging entirely (`false` by default) |
-| `WANDB_QUIET`. | Set to `true` to limit statements logged to standard output to critical statements only (`false` by default) |
-| `WANDB_SILENT` | Set to `true` to silence the output printed by wandb (`false` by default) |
-
-{{< tabpane text=true >}}
-
-{{% tab header="Command Line" value="cli" %}}
-
-```bash
-WANDB_WATCH=all
-WANDB_SILENT=true
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-```notebook
-%env WANDB_WATCH=all
-%env WANDB_SILENT=true
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-
-### How do I customize `wandb.init`?
-
-The `WandbCallback` that `Trainer` uses will call `wandb.init` under the hood when `Trainer` is initialized. You can alternatively set up your runs manually by calling `wandb.init` before the`Trainer` is initialized. This gives you full control over your W&B run configuration.
-
-An example of what you might want to pass to `init` is below. For `wandb.init()` details, see the [`wandb.init()` reference]({{< relref "/ref/python/functions/init.md" >}}).
-
-```python
-wandb.init(
- project="amazon_sentiment_analysis",
- name="bert-base-high-lr",
- tags=["baseline", "high-lr"],
- group="bert",
-)
-```
-
-
-## Additional resources
-
-Below are 6 Transformers and W&B related articles you might enjoy
-
-
-
-Hyperparameter Optimization for Hugging Face Transformers
-
-* Three strategies for hyperparameter optimization for Hugging Face Transformers are compared: Grid Search, Bayesian Optimization, and Population Based Training.
-* We use a standard uncased BERT model from Hugging Face transformers, and we want to fine-tune on the RTE dataset from the SuperGLUE benchmark
-* Results show that Population Based Training is the most effective approach to hyperparameter optimization of our Hugging Face transformer model.
-
-Read the [Hyperparameter Optimization for Hugging Face Transformers report](https://wandb.ai/amogkam/transformers/reports/Hyperparameter-Optimization-for-Hugging-Face-Transformers--VmlldzoyMTc2ODI).
-
-
-
-
-Hugging Tweets: Train a Model to Generate Tweets
-
-* In the article, the author demonstrates how to fine-tune a pre-trained GPT2 HuggingFace Transformer model on anyone's Tweets in five minutes.
-* The model uses the following pipeline: Downloading Tweets, Optimizing the Dataset, Initial Experiments, Comparing Losses Between Users, Fine-Tuning the Model.
-
-Read the full report [here](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
-
-
-
-
-Sentence Classification With Hugging Face BERT and WB
-
-* In this article, we'll build a sentence classifier leveraging the power of recent breakthroughs in Natural Language Processing, focusing on an application of transfer learning to NLP.
-* We'll be using The Corpus of Linguistic Acceptability (CoLA) dataset for single sentence classification, which is a set of sentences labeled as grammatically correct or incorrect that was first published in May 2018.
-* We'll use Google's BERT to create high performance models with minimal effort on a range of NLP tasks.
-
-Read the full report [here](https://wandb.ai/cayush/bert-finetuning/reports/Sentence-Classification-With-Huggingface-BERT-and-W-B--Vmlldzo4MDMwNA).
-
-
-
-
-A Step by Step Guide to Tracking Hugging Face Model Performance
-
-* We use W&B and Hugging Face transformers to train DistilBERT, a Transformer that's 40% smaller than BERT but retains 97% of BERT's accuracy, on the GLUE benchmark
-* The GLUE benchmark is a collection of nine datasets and tasks for training NLP models
-
-Read the full report [here](https://wandb.ai/jxmorris12/huggingface-demo/reports/A-Step-by-Step-Guide-to-Tracking-HuggingFace-Model-Performance--VmlldzoxMDE2MTU).
-
-
-
-
-Examples of Early Stopping in HuggingFace
-
-* Fine-tuning a Hugging Face Transformer using Early Stopping regularization can be done natively in PyTorch or TensorFlow.
-* Using the EarlyStopping callback in TensorFlow is straightforward with the `tf.keras.callbacks.EarlyStopping`callback.
-* In PyTorch, there is not an off-the-shelf early stopping method, but there is a working early stopping hook available on GitHub Gist.
-
-Read the full report [here](https://wandb.ai/ayush-thakur/huggingface/reports/Early-Stopping-in-HuggingFace-Examples--Vmlldzo0MzE2MTM).
-
-
-
-
-How to Fine-Tune Hugging Face Transformers on a Custom Dataset
-
-We fine tune a DistilBERT transformer for sentiment analysis (binary classification) on a custom IMDB dataset.
-
-Read the full report [here](https://wandb.ai/ayush-thakur/huggingface/reports/How-to-Fine-Tune-HuggingFace-Transformers-on-a-Custom-Dataset--Vmlldzo0MzQ2MDc).
-
-
-## Get help or request features
-
-For any issues, questions, or feature requests for the Hugging Face W&B integration, feel free to post in [this thread on the Hugging Face forums](https://discuss.huggingface.co/t/logging-experiment-tracking-with-w-b/498) or open an issue on the Hugging Face [Transformers GitHub repo](https://github.com/huggingface/transformers).
\ No newline at end of file
diff --git a/content/en/guides/integrations/hydra.md b/content/en/guides/integrations/hydra.md
deleted file mode 100644
index 4e15f445c4..0000000000
--- a/content/en/guides/integrations/hydra.md
+++ /dev/null
@@ -1,101 +0,0 @@
----
-description: How to integrate W&B with Hydra.
-menu:
- default:
- identifier: hydra
- parent: integrations
-title: Hydra
-weight: 150
----
-
-> [Hydra](https://hydra.cc) is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.
-
-You can continue to use Hydra for configuration management while taking advantage of the power of W&B.
-
-## Track metrics
-
-Track your metrics as normal with `wandb.init()` and `wandb.Run.log()` . Here, `wandb.entity` and `wandb.project` are defined within a hydra configuration file.
-
-```python
-import wandb
-
-
-@hydra.main(config_path="configs/", config_name="defaults")
-def run_experiment(cfg):
-
- with wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project) as run:
- run.log({"loss": loss})
-```
-
-## Track Hyperparameters
-
-Hydra uses [omegaconf](https://omegaconf.readthedocs.io/en/2.1_branch/) as the default way to interface with configuration dictionaries. `OmegaConf`'s dictionary are not a subclass of primitive dictionaries so directly passing Hydra's `Config` to `wandb.Run.config` leads to unexpected results on the dashboard. It's necessary to convert `omegaconf.DictConfig` to the primitive `dict` type before passing to `wandb.Run.config`.
-
-```python
-@hydra.main(config_path="configs/", config_name="defaults")
-def run_experiment(cfg):
- with wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project) as run:
- run.config = omegaconf.OmegaConf.to_container(
- cfg, resolve=True, throw_on_missing=True
- )
- run = wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project)
- run.log({"loss": loss})
- model = Model(**run.config.model.configs)
-```
-
-## Troubleshoot multiprocessing
-
-If your process hangs when started, this may be caused by [this known issue]({{< relref "/guides/models/track/log/distributed-training.md" >}}). To solve this, try to changing wandb's multiprocessing protocol either by adding an extra settings parameter to `wandb.init()` as:
-
-```python
-wandb.init(settings=wandb.Settings(start_method="thread"))
-```
-
-or by setting a global environment variable from your shell:
-
-```bash
-$ export WANDB_START_METHOD=thread
-```
-
-## Optimize Hyperparameters
-
-[W&B Sweeps]({{< relref "/guides/models/sweeps/" >}}) is a highly scalable hyperparameter search platform, which provides interesting insights and visualization about W&B experiments with minimal requirements code real-estate. Sweeps integrates seamlessly with Hydra projects with no-coding requirements. The only thing needed is a configuration file describing the various parameters to sweep over as normal.
-
-A simple example `sweep.yaml` file would be:
-
-```yaml
-program: main.py
-method: bayes
-metric:
- goal: maximize
- name: test/accuracy
-parameters:
- dataset:
- values: [mnist, cifar10]
-
-command:
- - ${env}
- - python
- - ${program}
- - ${args_no_hyphens}
-```
-
-Invoke the sweep:
-
-``` bash
-wandb sweep sweep.yaml` \
-```
-
-W&B automatically creates a sweep inside your project and returns a `wandb agent` command for you to run on each machine you want to run your sweep.
-
-### Pass parameters not present in Hydra defaults
-
-
-
-Hydra supports passing extra parameters through the command line which aren't present in the default configuration file, by using a `+` before command. For example, you can pass an extra parameter with some value by simply calling:
-
-```bash
-$ python program.py +experiment=some_experiment
-```
-
-You cannot sweep over such `+` configurations similar to what one does while configuring [Hydra Experiments](https://hydra.cc/docs/patterns/configuring_experiments/). To work around this, you can initialize the experiment parameter with a default empty file and use W&B Sweep to override those empty configs on each call. For more information, read [this W&B Report](https://wandb.me/hydra).
diff --git a/content/en/guides/integrations/ignite.md b/content/en/guides/integrations/ignite.md
deleted file mode 100644
index 293cdfa39c..0000000000
--- a/content/en/guides/integrations/ignite.md
+++ /dev/null
@@ -1,192 +0,0 @@
----
-description: How to integrate W&B with PyTorch Ignite.
-menu:
- default:
- identifier: ignite
- parent: integrations
-title: PyTorch Ignite
-weight: 330
----
-
-* See the resulting visualizations in this [example W&B report →](https://app.wandb.ai/example-team/pytorch-ignite-example/reports/PyTorch-Ignite-with-W%26B--Vmlldzo0NzkwMg)
-* Try running the code yourself in this [example hosted notebook →](https://colab.research.google.com/drive/15e-yGOvboTzXU4pe91Jg-Yr7sae3zBOJ#scrollTo=ztVifsYAmnRr)
-
-Ignite supports W&B handler to log metrics, model/optimizer parameters, gradients during training and validation. It can also be used to log model checkpoints to the W&B cloud. This class is also a wrapper for the wandb module. This means that you can call any wandb function using this wrapper. See examples on how to save model parameters and gradients.
-
-## Basic setup
-
-```python
-from argparse import ArgumentParser
-import wandb
-import torch
-from torch import nn
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-import torch.nn.functional as F
-from torchvision.transforms import Compose, ToTensor, Normalize
-from torchvision.datasets import MNIST
-
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
-
-from tqdm import tqdm
-
-
-class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
- self.conv2_drop = nn.Dropout2d()
- self.fc1 = nn.Linear(320, 50)
- self.fc2 = nn.Linear(50, 10)
-
- def forward(self, x):
- x = F.relu(F.max_pool2d(self.conv1(x), 2))
- x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
- x = x.view(-1, 320)
- x = F.relu(self.fc1(x))
- x = F.dropout(x, training=self.training)
- x = self.fc2(x)
- return F.log_softmax(x, dim=-1)
-
-
-def get_data_loaders(train_batch_size, val_batch_size):
- data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
-
- train_loader = DataLoader(MNIST(download=True, root=".", transform=data_transform, train=True),
- batch_size=train_batch_size, shuffle=True)
-
- val_loader = DataLoader(MNIST(download=False, root=".", transform=data_transform, train=False),
- batch_size=val_batch_size, shuffle=False)
- return train_loader, val_loader
-```
-
-Using `WandBLogger` in ignite is a modular process. First, you create a `WandBLogger` object. Next, you attach it to a trainer or evaluator to automatically log the metrics. This example shows:
-
-* Logs training loss, attached to the trainer object.
-* Logs validation loss, attached to the evaluator.
-* Logs optional Parameters, such as learning rate.
-* Watches the model.
-
-```python
-from ignite.contrib.handlers.wandb_logger import *
-def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval):
- train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)
- model = Net()
- device = 'cpu'
-
- if torch.cuda.is_available():
- device = 'cuda'
-
- optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
- trainer = create_supervised_trainer(model, optimizer, F.nll_loss, device=device)
- evaluator = create_supervised_evaluator(model,
- metrics={'accuracy': Accuracy(),
- 'nll': Loss(F.nll_loss)},
- device=device)
-
- desc = "ITERATION - loss: {:.2f}"
- pbar = tqdm(
- initial=0, leave=False, total=len(train_loader),
- desc=desc.format(0)
- )
- #WandBlogger Object Creation
- wandb_logger = WandBLogger(
- project="pytorch-ignite-integration",
- name="cnn-mnist",
- config={"max_epochs": epochs,"batch_size":train_batch_size},
- tags=["pytorch-ignite", "mninst"]
- )
-
- wandb_logger.attach_output_handler(
- trainer,
- event_name=Events.ITERATION_COMPLETED,
- tag="training",
- output_transform=lambda loss: {"loss": loss}
- )
-
- wandb_logger.attach_output_handler(
- evaluator,
- event_name=Events.EPOCH_COMPLETED,
- tag="training",
- metric_names=["nll", "accuracy"],
- global_step_transform=lambda *_: trainer.state.iteration,
- )
-
- wandb_logger.attach_opt_params_handler(
- trainer,
- event_name=Events.ITERATION_STARTED,
- optimizer=optimizer,
- param_name='lr' # optional
- )
-
- wandb_logger.watch(model)
-```
-
-You can optionally utilize ignite `EVENTS` to log the metrics directly to the terminal
-
-```python
- @trainer.on(Events.ITERATION_COMPLETED(every=log_interval))
- def log_training_loss(engine):
- pbar.desc = desc.format(engine.state.output)
- pbar.update(log_interval)
-
- @trainer.on(Events.EPOCH_COMPLETED)
- def log_training_results(engine):
- pbar.refresh()
- evaluator.run(train_loader)
- metrics = evaluator.state.metrics
- avg_accuracy = metrics['accuracy']
- avg_nll = metrics['nll']
- tqdm.write(
- "Training Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}"
- .format(engine.state.epoch, avg_accuracy, avg_nll)
- )
-
- @trainer.on(Events.EPOCH_COMPLETED)
- def log_validation_results(engine):
- evaluator.run(val_loader)
- metrics = evaluator.state.metrics
- avg_accuracy = metrics['accuracy']
- avg_nll = metrics['nll']
- tqdm.write(
- "Validation Results - Epoch: {} Avg accuracy: {:.2f} Avg loss: {:.2f}"
- .format(engine.state.epoch, avg_accuracy, avg_nll))
-
- pbar.n = pbar.last_print_n = 0
-
- trainer.run(train_loader, max_epochs=epochs)
- pbar.close()
-
-
-if __name__ == "__main__":
- parser = ArgumentParser()
- parser.add_argument('--batch_size', type=int, default=64,
- help='input batch size for training (default: 64)')
- parser.add_argument('--val_batch_size', type=int, default=1000,
- help='input batch size for validation (default: 1000)')
- parser.add_argument('--epochs', type=int, default=10,
- help='number of epochs to train (default: 10)')
- parser.add_argument('--lr', type=float, default=0.01,
- help='learning rate (default: 0.01)')
- parser.add_argument('--momentum', type=float, default=0.5,
- help='SGD momentum (default: 0.5)')
- parser.add_argument('--log_interval', type=int, default=10,
- help='how many batches to wait before logging training status')
-
- args = parser.parse_args()
- run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_interval)
-```
-
-This code generates these visualizations::
-
-{{< img src="/images/integrations/pytorch-ignite-1.png" alt="PyTorch Ignite training dashboard" >}}
-
-{{< img src="/images/integrations/pytorch-ignite-2.png" alt="PyTorch Ignite performance" >}}
-
-{{< img src="/images/integrations/pytorch-ignite-3.png" alt="PyTorch Ignite hyperparameter tuning results" >}}
-
-{{< img src="/images/integrations/pytorch-ignite-4.png" alt="PyTorch Ignite model comparison dashboard" >}}
-
-Refer to the [Ignite Docs](https://pytorch.org/ignite/contrib/handlers.html#module-ignite.contrib.handlers.wandb_logger) for more details.
\ No newline at end of file
diff --git a/content/en/guides/integrations/keras.md b/content/en/guides/integrations/keras.md
deleted file mode 100644
index 883e773b7d..0000000000
--- a/content/en/guides/integrations/keras.md
+++ /dev/null
@@ -1,312 +0,0 @@
----
-menu:
- default:
- identifier: keras
- parent: integrations
-title: Keras
-weight: 160
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases_keras.ipynb" >}}
-
-## Keras callbacks
-
-W&B has three callbacks for Keras, available from `wandb` v0.13.4. For the legacy `WandbCallback` scroll down.
-
-
-- **`WandbMetricsLogger`** : Use this callback for [Experiment Tracking]({{< relref "/guides/models/track" >}}). It logs your training and validation metrics along with system metrics to W&B.
-
-- **`WandbModelCheckpoint`** : Use this callback to log your model checkpoints to W&B [Artifacts]({{< relref "/guides/core/artifacts/" >}}).
-
-- **`WandbEvalCallback`**: This base callback logs model predictions to W&B [Tables]({{< relref "/guides/models/tables/" >}}) for interactive visualization.
-
-These new callbacks:
-
-* Adhere to Keras design philosophy.
-* Reduce the cognitive load of using a single callback (`WandbCallback`) for everything.
-* Make it easy for Keras users to modify the callback by subclassing it to support their niche use case.
-
-## Track experiments with `WandbMetricsLogger`
-
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/master/colabs/keras/Use_WandbMetricLogger_in_your_Keras_workflow.ipynb" >}}
-
-`WandbMetricsLogger` automatically logs Keras' `logs` dictionary that callback methods such as `on_epoch_end`, `on_batch_end` etc, take as an argument.
-
-This tracks:
-
-* Training and validation metrics defined in `model.compile`.
-* System (CPU/GPU/TPU) metrics.
-* Learning rate (both for a fixed value or a learning rate scheduler.
-
-```python
-import wandb
-from wandb.integration.keras import WandbMetricsLogger
-
-# Initialize a new W&B Run
-wandb.init(config={"bs": 12})
-
-# Pass the WandbMetricsLogger to model.fit
-model.fit(
- X_train, y_train, validation_data=(X_test, y_test), callbacks=[WandbMetricsLogger()]
-)
-```
-
-### `WandbMetricsLogger` reference
-
-
-| Parameter | Description |
-| --------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
-| `log_freq` | (`epoch`, `batch`, or an `int`): if `epoch`, logs metrics at the end of each epoch. If `batch`, logs metrics at the end of each batch. If an `int`, logs metrics at the end of that many batches. Defaults to `epoch`. |
-| `initial_global_step` | (int): Use this argument to correctly log the learning rate when you resume training from some initial_epoch, and a learning rate scheduler is used. This can be computed as step_size * initial_step. Defaults to 0. |
-
-## Checkpoint a model using `WandbModelCheckpoint`
-
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/master/colabs/keras/Use_WandbModelCheckpoint_in_your_Keras_workflow.ipynb" >}}
-
-Use `WandbModelCheckpoint` callback to save the Keras model (`SavedModel` format) or model weights periodically and uploads them to W&B as a `wandb.Artifact` for model versioning.
-
-This callback is subclassed from [`tf.keras.callbacks.ModelCheckpoint`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) ,thus the checkpointing logic is taken care of by the parent callback.
-
-This callback saves:
-
-* The model that has achieved best performance based on the monitor.
-* The model at the end of every epoch regardless of the performance.
-* The model at the end of the epoch or after a fixed number of training batches.
-* Only model weights or the whole model.
-* The model either in `SavedModel` format or in `.h5` format.
-
-Use this callback in conjunction with `WandbMetricsLogger`.
-
-```python
-import wandb
-from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint
-
-# Initialize a new W&B Run
-wandb.init(config={"bs": 12})
-
-# Pass the WandbModelCheckpoint to model.fit
-model.fit(
- X_train,
- y_train,
- validation_data=(X_test, y_test),
- callbacks=[
- WandbMetricsLogger(),
- WandbModelCheckpoint("models"),
- ],
-)
-```
-
-### `WandbModelCheckpoint` reference
-
-| Parameter | Description |
-| ------------------------- | ---- |
-| `filepath` | (str): path to save the mode file.|
-| `monitor` | (str): The metric name to monitor. |
-| `verbose` | (int): Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. |
-| `save_best_only` | (Boolean): if `save_best_only=True`, it only saves the latest model or the model it considers the best, according to the defined by the `monitor` and `mode` attributes. |
-| `save_weights_only` | (Boolean): if True, saves only the model's weights. |
-| `mode` | (`auto`, `min`, or `max`): For `val_acc`, set it to `max`, for `val_loss`, set it to `min`, and so on | |
-| `save_freq` | ("epoch" or int): When using ‘epoch’, the callback saves the model after each epoch. When using an integer, the callback saves the model at end of this many batches. Note that when monitoring validation metrics such as `val_acc` or `val_loss`, `save_freq` must be set to "epoch" as those metrics are only available at the end of an epoch. |
-| `options` | (str): Optional `tf.train.CheckpointOptions` object if `save_weights_only` is true or optional `tf.saved_model.SaveOptions` object if `save_weights_only` is false. |
-| `initial_value_threshold` | (float): Floating point initial "best" value of the metric to be monitored. |
-
-### Log checkpoints after N epochs
-
-By default (`save_freq="epoch"`), the callback creates a checkpoint and uploads it as an artifact after each epoch. To create a checkpoint after a specific number of batches, set `save_freq` to an integer. To checkpoint after `N` epochs, compute the cardinality of the `train` dataloader and pass it to `save_freq`:
-
-```python
-WandbModelCheckpoint(
- filepath="models/",
- save_freq=int((trainloader.cardinality()*N).numpy())
-)
-```
-
-### Efficiently log checkpoints on a TPU architecture
-
-While checkpointing on TPUs you might encounter `UnimplementedError: File system scheme '[local]' not implemented` error message. This happens because the model directory (`filepath`) must use a cloud storage bucket path (`gs://bucket-name/...`), and this bucket must be accessible from the TPU server. We can however, use the local path for checkpointing which in turn is uploaded as an Artifacts.
-
-```python
-checkpoint_options = tf.saved_model.SaveOptions(experimental_io_device="/job:localhost")
-
-WandbModelCheckpoint(
- filepath="models/,
- options=checkpoint_options,
-)
-```
-
-## Visualize model predictions using `WandbEvalCallback`
-
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/e66f16fbe7ae7a2e636d59350a50059d3f7e5494/colabs/keras/Use_WandbEvalCallback_in_your_Keras_workflow.ipynb" >}}
-
-The `WandbEvalCallback` is an abstract base class to build Keras callbacks primarily for model prediction and, secondarily, dataset visualization.
-
-This abstract callback is agnostic with respect to the dataset and the task. To use this, inherit from this base `WandbEvalCallback` callback class and implement the `add_ground_truth` and `add_model_prediction` methods.
-
-The `WandbEvalCallback` is a utility class that provides methods to:
-
-* Create data and prediction `wandb.Table` instances.
-* Log data and prediction Tables as `wandb.Artifact`.
-* Log the data table `on_train_begin`.
-* log the prediction table `on_epoch_end`.
-
-The following example uses `WandbClfEvalCallback` for an image classification task. This example callback logs the validation data (`data_table`) to W&B, performs inference, and logs the prediction (`pred_table`) to W&B at the end of every epoch.
-
-```python
-import wandb
-from wandb.integration.keras import WandbMetricsLogger, WandbEvalCallback
-
-
-# Implement your model prediction visualization callback
-class WandbClfEvalCallback(WandbEvalCallback):
- def __init__(
- self, validation_data, data_table_columns, pred_table_columns, num_samples=100
- ):
- super().__init__(data_table_columns, pred_table_columns)
-
- self.x = validation_data[0]
- self.y = validation_data[1]
-
- def add_ground_truth(self, logs=None):
- for idx, (image, label) in enumerate(zip(self.x, self.y)):
- self.data_table.add_data(idx, wandb.Image(image), label)
-
- def add_model_predictions(self, epoch, logs=None):
- preds = self.model.predict(self.x, verbose=0)
- preds = tf.argmax(preds, axis=-1)
-
- table_idxs = self.data_table_ref.get_index()
-
- for idx in table_idxs:
- pred = preds[idx]
- self.pred_table.add_data(
- epoch,
- self.data_table_ref.data[idx][0],
- self.data_table_ref.data[idx][1],
- self.data_table_ref.data[idx][2],
- pred,
- )
-
-
-# ...
-
-# Initialize a new W&B Run
-wandb.init(config={"hyper": "parameter"})
-
-# Add the Callbacks to Model.fit
-model.fit(
- X_train,
- y_train,
- validation_data=(X_test, y_test),
- callbacks=[
- WandbMetricsLogger(),
- WandbClfEvalCallback(
- validation_data=(X_test, y_test),
- data_table_columns=["idx", "image", "label"],
- pred_table_columns=["epoch", "idx", "image", "label", "pred"],
- ),
- ],
-)
-```
-
-{{% alert %}}
-The W&B [Artifact page]({{< relref "/guides/core/artifacts/explore-and-traverse-an-artifact-graph" >}}) includes Table logs by default, rather than the **Workspace** page.
-{{% /alert %}}
-
-### `WandbEvalCallback` reference
-
-| Parameter | Description |
-| -------------------- | ------------------------------------------------ |
-| `data_table_columns` | (list) List of column names for the `data_table` |
-| `pred_table_columns` | (list) List of column names for the `pred_table` |
-
-### Memory footprint details
-
-We log the `data_table` to W&B when the `on_train_begin` method is invoked. Once it's uploaded as a W&B Artifact, we get a reference to this table which can be accessed using `data_table_ref` class variable. The `data_table_ref` is a 2D list that can be indexed like `self.data_table_ref[idx][n]`, where `idx` is the row number while `n` is the column number. Let's see the usage in the example below.
-
-### Customize the callback
-
-You can override the `on_train_begin` or `on_epoch_end` methods to have more fine-grained control. If you want to log the samples after `N` batches, you can implement `on_train_batch_end` method.
-
-{{% alert %}}
-If you are implementing a callback for model prediction visualization by inheriting `WandbEvalCallback` and something needs to be clarified or fixed, let us know by opening an [issue](https://github.com/wandb/wandb/issues).
-{{% /alert %}}
-
-## `WandbCallback` [legacy]
-
-Use the W&B library `WandbCallback` Class to automatically save all the metrics and the loss values tracked in `model.fit`.
-
-```python
-import wandb
-from wandb.integration.keras import WandbCallback
-
-wandb.init(config={"hyper": "parameter"})
-
-... # code to set up your model in Keras
-
-# Pass the callback to model.fit
-model.fit(
- X_train, y_train, validation_data=(X_test, y_test), callbacks=[WandbCallback()]
-)
-```
-
-You can watch the short video [Get Started with Keras and W&B in Less Than a Minute](https://www.youtube.com/watch?ab_channel=Weights&Biases&v=4FjDIJ-vO_M).
-
-For a more detailed video, watch [Integrate W&B with Keras](https://www.youtube.com/watch?v=Bsudo7jbMow\&ab_channel=Weights%26Biases). You can review the [Colab Jupyter Notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Keras_pipeline_with_Weights_and_Biases.ipynb).
-
-{{% alert %}}
-See our [example repo](https://github.com/wandb/examples) for scripts, including a [Fashion MNIST example](https://github.com/wandb/examples/blob/master/examples/keras/keras-cnn-fashion/train.py) and the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
-{{% /alert %}}
-
-The `WandbCallback` class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training_data and validation_data, and more.
-
-Check out the reference documentation for the `keras.WandbCallback` for full details.
-
-The `WandbCallback`
-
-* Automatically logs history data from any metrics collected by Keras: loss and anything passed into `keras_model.compile()`.
-* Sets summary metrics for the run associated with the "best" training step, as defined by the `monitor` and `mode` attributes. This defaults to the epoch with the minimum `val_loss`. `WandbCallback` by default saves the model associated with the best `epoch`.
-* Optionally logs gradient and parameter histogram.
-* Optionally saves training and validation data for wandb to visualize.
-
-### `WandbCallback` reference
-
-| Arguments | |
-| -------------------------- | ------------------------------------------- |
-| `monitor` | (str) name of metric to monitor. Defaults to `val_loss`. |
-| `mode` | (str) one of {`auto`, `min`, `max`}. `min` - save model when monitor is minimized `max` - save model when monitor is maximized `auto` - try to guess when to save the model (default). |
-| `save_model` | True - save a model when monitor beats all previous epochs False - don't save models |
-| `save_graph` | (boolean) if True save model graph to wandb (default to True). |
-| `save_weights_only` | (boolean) if True, saves only the model's weights(`model.save_weights(filepath)`). Otherwise, saves the full model). |
-| `log_weights` | (boolean) if True save histograms of the model's layer's weights. |
-| `log_gradients` | (boolean) if True log histograms of the training gradients |
-| `training_data` | (tuple) Same format `(X,y)` as passed to `model.fit`. This is needed for calculating gradients - this is mandatory if `log_gradients` is `True`. |
-| `validation_data` | (tuple) Same format `(X,y)` as passed to `model.fit`. A set of data for wandb to visualize. If you set this field, every epoch, wandb makes a small number of predictions and saves the results for later visualization. |
-| `generator` | (generator) a generator that returns validation data for wandb to visualize. This generator should return tuples `(X,y)`. Either `validate_data` or generator should be set for wandb to visualize specific data examples. |
-| `validation_steps` | (int) if `validation_data` is a generator, how many steps to run the generator for the full validation set. |
-| `labels` | (list) If you are visualizing your data with wandb this list of labels converts numeric output to understandable string if you are building a classifier with multiple classes. For a binary classifier, you can pass in a list of two labels \[`label for false`, `label for true`]. If `validate_data` and `generator` are both false, this does nothing. |
-| `predictions` | (int) the number of predictions to make for visualization each epoch, max is 100. |
-| `input_type` | (string) type of the model input to help visualization. can be one of: (`image`, `images`, `segmentation_mask`). |
-| `output_type` | (string) type of the model output to help visualziation. can be one of: (`image`, `images`, `segmentation_mask`). |
-| `log_evaluation` | (boolean) if True, save a Table containing validation data and the model's predictions at each epoch. See `validation_indexes`, `validation_row_processor`, and `output_row_processor` for additional details. |
-| `class_colors` | (\[float, float, float]) if the input or output is a segmentation mask, an array containing an rgb tuple (range 0-1) for each class. |
-| `log_batch_frequency` | (integer) if None, callback logs every epoch. If set to integer, callback logs training metrics every `log_batch_frequency` batches. |
-| `log_best_prefix` | (string) if None, saves no extra summary metrics. If set to a string, prepends the monitored metric and epoch with the prefix and saves the results as summary metrics. |
-| `validation_indexes` | (\[wandb.data_types._TableLinkMixin]) an ordered list of index keys to associate with each validation example. If `log_evaluation` is True and you provide `validation_indexes`, does not create a Table of validation data. Instead, associates each prediction with the row represented by the `TableLinkMixin`. To obtain a list of row keys, use `Table.get_index() `. |
-| `validation_row_processor` | (Callable) a function to apply to the validation data, commonly used to visualize the data. The function receives an `ndx` (int) and a `row` (dict). If your model has a single input, then `row["input"]` contains the input data for the row. Otherwise, it contains the names of the input slots. If your fit function takes a single target, then `row["target"]` contains the target data for the row. Otherwise, it contains the names of the output slots. For example, if your input data is a single array, to visualize the data as an Image, provide `lambda ndx, row: {"img": wandb.Image(row["input"])}` as the processor. Ignored if `log_evaluation` is False or `validation_indexes` are present. |
-| `output_row_processor` | (Callable) same as `validation_row_processor`, but applied to the model's output. `row["output"]` contains the results of the model output. |
-| `infer_missing_processors` | (Boolean) Determines whether to infer `validation_row_processor` and `output_row_processor` if they are missing. Defaults to True. If you provide `labels`, W&B attempts to infer classification-type processors where appropriate. |
-| `log_evaluation_frequency` | (int) Determines how often to log evaluation results. Defaults to `0` to log only at the end of training. Set to 1 to log every epoch, 2 to log every other epoch, and so on. Has no effect when `log_evaluation` is False. |
-
-## Frequently Asked Questions
-
-### How do I use `Keras` multiprocessing with `wandb`?
-
-When setting `use_multiprocessing=True`, this error may occur:
-
-```python
-Error("You must call wandb.init() before wandb.config.batch_size")
-```
-
-To work around it:
-
-1. In the `Sequence` class construction, add: `wandb.init(group='...')`.
-2. In `main`, make sure you're using `if __name__ == "__main__":` and put the rest of your script logic inside it.
\ No newline at end of file
diff --git a/content/en/guides/integrations/kubeflow-pipelines-kfp.md b/content/en/guides/integrations/kubeflow-pipelines-kfp.md
deleted file mode 100644
index e32f85d098..0000000000
--- a/content/en/guides/integrations/kubeflow-pipelines-kfp.md
+++ /dev/null
@@ -1,201 +0,0 @@
----
-description: How to integrate W&B with Kubeflow Pipelines.
-menu:
- default:
- identifier: kubeflow-pipelines-kfp
- parent: integrations
-title: Kubeflow Pipelines (kfp)
-weight: 170
----
-
-
-[Kubeflow Pipelines (kfp) ](https://www.kubeflow.org/docs/components/pipelines/overview/)is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.
-
-This integration lets users apply decorators to kfp python functional components to automatically log parameters and artifacts to W&B.
-
-This feature was enabled in `wandb==0.12.11` and requires `kfp<2.0.0`
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-## Decorate your components
-
-Add the `@wandb_log` decorator and create your components as usual. This will automatically log the input/outputs parameters and artifacts to W&B each time you run your pipeline.
-
-```python
-from kfp import components
-from wandb.integration.kfp import wandb_log
-
-
-@wandb_log
-def add(a: float, b: float) -> float:
- return a + b
-
-
-add = components.create_component_from_func(add)
-```
-
-## Pass environment variables to containers
-
-You may need to explicitly pass [environment variables]({{< relref "/guides/models/track/environment-variables.md" >}}) to your containers. For two-way linking, you should also set the environment variables `WANDB_KUBEFLOW_URL` to the base URL of your Kubeflow Pipelines instance. For example, `https://kubeflow.mysite.com`.
-
-```python
-import os
-from kubernetes.client.models import V1EnvVar
-
-
-def add_wandb_env_variables(op):
- env = {
- "WANDB_API_KEY": os.getenv("WANDB_API_KEY"),
- "WANDB_BASE_URL": os.getenv("WANDB_BASE_URL"),
- }
-
- for name, value in env.items():
- op = op.add_env_variable(V1EnvVar(name, value))
- return op
-
-
-@dsl.pipeline(name="example-pipeline")
-def example_pipeline(param1: str, param2: int):
- conf = dsl.get_pipeline_conf()
- conf.add_op_transformer(add_wandb_env_variables)
-```
-
-## Access your data programmatically
-
-### Via the Kubeflow Pipelines UI
-
-Click on any Run in the Kubeflow Pipelines UI that has been logged with W&B.
-
-* Find details about inputs and outputs in the `Input/Output` and `ML Metadata` tabs.
-* View the W&B web app from the `Visualizations` tab.
-
-{{< img src="/images/integrations/kubeflow_app_pipelines_ui.png" alt="W&B in Kubeflow UI" >}}
-
-### Via the web app UI
-
-The web app UI has the same content as the `Visualizations` tab in Kubeflow Pipelines, but with more space. Learn [more about the web app UI here]({{< relref "/guides/models/app" >}}).
-
-{{< img src="/images/integrations/kubeflow_pipelines.png" alt="Run details" >}}
-
-{{< img src="/images/integrations/kubeflow_via_app.png" alt="Pipeline DAG" >}}
-
-### Via the Public API (for programmatic access)
-
-* For programmatic access, [see our Public API]({{< relref "/ref/python/public-api/index.md" >}}).
-
-### Concept mapping from Kubeflow Pipelines to W&B
-
-Here's a mapping of Kubeflow Pipelines concepts to W&B
-
-| Kubeflow Pipelines | W&B | Location in W&B |
-| ------------------ | --- | --------------- |
-| Input Scalar | [`config`]({{< relref "/guides/models/track/config" >}}) | [Overview tab]({{< relref "/guides/models/track/runs/#overview-tab" >}}) |
-| Output Scalar | [`summary`]({{< relref "/guides/models/track/log" >}}) | [Overview tab]({{< relref "/guides/models/track/runs/#overview-tab" >}}) |
-| Input Artifact | Input Artifact | [Artifacts tab]({{< relref "/guides/models/track/runs/#artifacts-tab" >}}) |
-| Output Artifact | Output Artifact | [Artifacts tab]({{< relref "/guides/models/track/runs/#artifacts-tab" >}}) |
-
-## Fine-grain logging
-
-If you want finer control of logging, you can sprinkle in `wandb.log` and `wandb.log_artifact` calls in the component.
-
-### With explicit `wandb.log_artifacts` calls
-
-In this example below, we are training a model. The `@wandb_log` decorator will automatically track the relevant inputs and outputs. If you want to log the training process, you can explicitly add that logging like so:
-
-```python
-@wandb_log
-def train_model(
- train_dataloader_path: components.InputPath("dataloader"),
- test_dataloader_path: components.InputPath("dataloader"),
- model_path: components.OutputPath("pytorch_model"),
-):
- with wandb.init() as run:
- ...
- for epoch in epochs:
- for batch_idx, (data, target) in enumerate(train_dataloader):
- ...
- if batch_idx % log_interval == 0:
- run.log(
- {"epoch": epoch, "step": batch_idx * len(data), "loss": loss.item()}
- )
- ...
- run.log_artifact(model_artifact)
-```
-
-### With implicit wandb integrations
-
-If you're using a [framework integration we support]({{< relref "/guides/integrations/" >}}), you can also pass in the callback directly:
-
-```python
-@wandb_log
-def train_model(
- train_dataloader_path: components.InputPath("dataloader"),
- test_dataloader_path: components.InputPath("dataloader"),
- model_path: components.OutputPath("pytorch_model"),
-):
- from pytorch_lightning.loggers import WandbLogger
- from pytorch_lightning import Trainer
-
- trainer = Trainer(logger=WandbLogger())
- ... # do training
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/lightgbm.md b/content/en/guides/integrations/lightgbm.md
deleted file mode 100644
index de80857fc5..0000000000
--- a/content/en/guides/integrations/lightgbm.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-description: Track your trees with W&B.
-menu:
- default:
- identifier: lightgbm
- parent: integrations
-title: LightGBM
-weight: 190
----
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Simple_LightGBM_Integration.ipynb" >}}
-
-The `wandb` library includes a special callback for [LightGBM](https://lightgbm.readthedocs.io/en/latest/). It's also easy to use the generic logging features of W&B to track large experiments, like hyperparameter sweeps.
-
-```python
-from wandb.integration.lightgbm import wandb_callback, log_summary
-import lightgbm as lgb
-
-# Log metrics to W&B
-gbm = lgb.train(..., callbacks=[wandb_callback()])
-
-# Log feature importance plot and upload model checkpoint to W&B
-log_summary(gbm, save_model_checkpoint=True)
-```
-
-{{% alert %}}
-Looking for working code examples? Check out [our repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms).
-{{% /alert %}}
-
-## Tuning your hyperparameters with Sweeps
-
-Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. W&B [Sweeps]({{< relref "/guides/models/sweeps/" >}}) is a powerful toolkit for configuring, orchestrating, and analyzing large hyperparameter testing experiments.
-
-To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out this interactive Colab notebook.
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Using_W%26B_Sweeps_with_XGBoost.ipynb" >}}
-
-{{< img src="/images/integrations/lightgbm_sweeps.png" alt="LightGBM performance comparison" >}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/lightning.md b/content/en/guides/integrations/lightning.md
deleted file mode 100644
index 8a90dabd54..0000000000
--- a/content/en/guides/integrations/lightning.md
+++ /dev/null
@@ -1,658 +0,0 @@
----
-menu:
- default:
- identifier: lightning
- parent: integrations
-title: PyTorch Lightning
-weight: 340
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch-lightning/Optimize_PyTorch_Lightning_models_with_Weights_%26_Biases.ipynb" >}}
-
-PyTorch Lightning provides a lightweight wrapper for organizing your PyTorch code and easily adding advanced features such as distributed training and 16-bit precision. W&B provides a lightweight wrapper for logging your ML experiments. But you don't need to combine the two yourself: W&B is incorporated directly into the PyTorch Lightning library via the [`WandbLogger`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb).
-
-## Integrate with Lightning
-
-{{< tabpane text=true >}}
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-from lightning.pytorch.loggers import WandbLogger
-from lightning.pytorch import Trainer
-
-wandb_logger = WandbLogger(log_model="all")
-trainer = Trainer(logger=wandb_logger)
-```
-
-{{% alert %}}
-**Using wandb.log():** The `WandbLogger` logs to W&B using the Trainer's `global_step`. If you make additional calls to `wandb.log` directly in your code, **do not** use the `step` argument in `wandb.log()`.
-
-Instead, log the Trainer's `global_step` like your other metrics:
-
-```python
-wandb.log({"accuracy":0.99, "trainer/global_step": step})
-```
-{{% /alert %}}
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-import lightning as L
-from wandb.integration.lightning.fabric import WandbLogger
-
-wandb_logger = WandbLogger(log_model="all")
-fabric = L.Fabric(loggers=[wandb_logger])
-fabric.launch()
-fabric.log_dict({"important_metric": important_metric})
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-{{< img src="/images/integrations/n6P7K4M.gif" alt="Interactive dashboards" >}}
-
-### Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-## Use PyTorch Lightning's `WandbLogger`
-
-PyTorch Lightning has multiple `WandbLogger` classes to log metrics and model weights, media, and more.
-
-- [`PyTorch`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
-- [`Fabric`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
-
-To integrate with Lightning, instantiate the `WandbLogger` and pass it to Lightning's `Trainer` or `Fabric`.
-
-{{< tabpane text=true >}}
-
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-trainer = Trainer(logger=wandb_logger)
-```
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-fabric = L.Fabric(loggers=[wandb_logger])
-fabric.launch()
-fabric.log_dict({
- "important_metric": important_metric
-})
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-
-### Common logger arguments
-
-Below are some of the most used parameters in `WandbLogger`. Review the PyTorch Lightning documentation for details about all logger arguments.
-
-- [`PyTorch`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
-- [`Fabric`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
-
-| Parameter | Description |
-| ----------- | ----------------------------------------------------------------------------- |
-| `project` | Define what wandb Project to log to |
-| `name` | Give a name to your wandb run |
-| `log_model` | Log all models if `log_model="all"` or at end of training if `log_model=True` |
-| `save_dir` | Path where data is saved |
-
-## Log your hyperparameters
-
-{{< tabpane text=true >}}
-
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-class LitModule(LightningModule):
- def __init__(self, *args, **kwarg):
- self.save_hyperparameters()
-```
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-wandb_logger.log_hyperparams(
- {
- "hyperparameter_1": hyperparameter_1,
- "hyperparameter_2": hyperparameter_2,
- }
-)
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-## Log additional config parameters
-
-```python
-# add one parameter
-wandb_logger.experiment.config["key"] = value
-
-# add multiple parameters
-wandb_logger.experiment.config.update({key1: val1, key2: val2})
-
-# use directly wandb module
-wandb.config["key"] = value
-wandb.config.update()
-```
-
-## Log gradients, parameter histogram and model topology
-
-You can pass your model object to `wandblogger.watch()` to monitor your models's gradients and parameters as you train. See the PyTorch Lightning `WandbLogger` documentation
-
-## Log metrics
-
-{{< tabpane text=true >}}
-
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-You can log your metrics to W&B when using the `WandbLogger` by calling `self.log('my_metric_name', metric_vale)` within your `LightningModule`, such as in your `training_step` or `validation_step methods.`
-
-The code snippet below shows how to define your `LightningModule` to log your metrics and your `LightningModule` hyperparameters. This example uses the [`torchmetrics`](https://github.com/PyTorchLightning/metrics) library to calculate your metrics
-
-```python
-import torch
-from torch.nn import Linear, CrossEntropyLoss, functional as F
-from torch.optim import Adam
-from torchmetrics.functional import accuracy
-from lightning.pytorch import LightningModule
-
-
-class My_LitModule(LightningModule):
- def __init__(self, n_classes=10, n_layer_1=128, n_layer_2=256, lr=1e-3):
- """method used to define the model parameters"""
- super().__init__()
-
- # mnist images are (1, 28, 28) (channels, width, height)
- self.layer_1 = Linear(28 * 28, n_layer_1)
- self.layer_2 = Linear(n_layer_1, n_layer_2)
- self.layer_3 = Linear(n_layer_2, n_classes)
-
- self.loss = CrossEntropyLoss()
- self.lr = lr
-
- # save hyper-parameters to self.hparams (auto-logged by W&B)
- self.save_hyperparameters()
-
- def forward(self, x):
- """method used for inference input -> output"""
-
- # (b, 1, 28, 28) -> (b, 1*28*28)
- batch_size, channels, width, height = x.size()
- x = x.view(batch_size, -1)
-
- # let's do 3 x (linear + relu)
- x = F.relu(self.layer_1(x))
- x = F.relu(self.layer_2(x))
- x = self.layer_3(x)
- return x
-
- def training_step(self, batch, batch_idx):
- """needs to return a loss from a single batch"""
- _, loss, acc = self._get_preds_loss_accuracy(batch)
-
- # Log loss and metric
- self.log("train_loss", loss)
- self.log("train_accuracy", acc)
- return loss
-
- def validation_step(self, batch, batch_idx):
- """used for logging metrics"""
- preds, loss, acc = self._get_preds_loss_accuracy(batch)
-
- # Log loss and metric
- self.log("val_loss", loss)
- self.log("val_accuracy", acc)
- return preds
-
- def configure_optimizers(self):
- """defines model optimizer"""
- return Adam(self.parameters(), lr=self.lr)
-
- def _get_preds_loss_accuracy(self, batch):
- """convenience function since train/valid/test steps are similar"""
- x, y = batch
- logits = self(x)
- preds = torch.argmax(logits, dim=1)
- loss = self.loss(logits, y)
- acc = accuracy(preds, y)
- return preds, loss, acc
-```
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-import lightning as L
-import torch
-import torchvision as tv
-from wandb.integration.lightning.fabric import WandbLogger
-import wandb
-
-fabric = L.Fabric(loggers=[wandb_logger])
-fabric.launch()
-
-model = tv.models.resnet18()
-optimizer = torch.optim.SGD(model.parameters(), lr=lr)
-model, optimizer = fabric.setup(model, optimizer)
-
-train_dataloader = fabric.setup_dataloaders(
- torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)
-)
-
-model.train()
-for epoch in range(num_epochs):
- for batch in train_dataloader:
- optimizer.zero_grad()
- loss = model(batch)
- loss.backward()
- optimizer.step()
- fabric.log_dict({"loss": loss})
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-## Log the min/max of a metric
-
-Using wandb's [`define_metric`]({{< relref "/ref/python/experiments/run.md#define_metric" >}}) function you can define whether you'd like your W&B summary metric to display the min, max, mean or best value for that metric. If `define`_`metric` _ isn't used, then the last value logged with appear in your summary metrics. See the `define_metric` [reference docs here]({{< relref "/ref/python/experiments/run.md#define_metric" >}}) and the [guide here]({{< relref "/guides/models/track/log/customize-logging-axes" >}}) for more.
-
-To tell W&B to keep track of the max validation accuracy in the W&B summary metric, call `wandb.define_metric` only once, at the beginning of training:
-
-{{< tabpane text=true >}}
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-class My_LitModule(LightningModule):
- ...
-
- def validation_step(self, batch, batch_idx):
- if trainer.global_step == 0:
- wandb.define_metric("val_accuracy", summary="max")
-
- preds, loss, acc = self._get_preds_loss_accuracy(batch)
-
- # Log loss and metric
- self.log("val_loss", loss)
- self.log("val_accuracy", acc)
- return preds
-```
-
-{{% /tab %}}
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-wandb.define_metric("val_accuracy", summary="max")
-fabric = L.Fabric(loggers=[wandb_logger])
-fabric.launch()
-fabric.log_dict({"val_accuracy": val_accuracy})
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Checkpoint a model
-
-To save model checkpoints as W&B [Artifacts]({{< relref "/guides/core/artifacts/" >}}),
-use the Lightning [`ModelCheckpoint`](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html#pytorch_lightning.callbacks.ModelCheckpoint) callback and set the `log_model` argument in the `WandbLogger`.
-
-{{< tabpane text=true >}}
-
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-trainer = Trainer(logger=wandb_logger, callbacks=[checkpoint_callback])
-```
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-fabric = L.Fabric(loggers=[wandb_logger], callbacks=[checkpoint_callback])
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-The _latest_ and _best_ aliases are automatically set to easily retrieve a model checkpoint from a W&B [Artifact]({{< relref "/guides/core/artifacts/" >}}):
-
-```python
-# reference can be retrieved in artifacts panel
-# "VERSION" can be a version (ex: "v2") or an alias ("latest or "best")
-checkpoint_reference = "USER/PROJECT/MODEL-RUN_ID:VERSION"
-```
-
-{{< tabpane text=true >}}
-{{% tab header="Via Logger" value="logger" %}}
-
-```python
-# download checkpoint locally (if not already cached)
-wandb_logger.download_artifact(checkpoint_reference, artifact_type="model")
-```
-
-{{% /tab %}}
-
-{{% tab header="Via wandb" value="wandb" %}}
-
-```python
-# download checkpoint locally (if not already cached)
-run = wandb.init(project="MNIST")
-artifact = run.use_artifact(checkpoint_reference, type="model")
-artifact_dir = artifact.download()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-{{< tabpane text=true >}}
-{{% tab header="PyTorch Logger" value="pytorch" %}}
-
-```python
-# load checkpoint
-model = LitModule.load_from_checkpoint(Path(artifact_dir) / "model.ckpt")
-```
-
-{{% /tab %}}
-
-{{% tab header="Fabric Logger" value="fabric" %}}
-
-```python
-# Request the raw checkpoint
-full_checkpoint = fabric.load(Path(artifact_dir) / "model.ckpt")
-
-model.load_state_dict(full_checkpoint["model"])
-optimizer.load_state_dict(full_checkpoint["optimizer"])
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-The model checkpoints you log are viewable through the [W&B Artifacts]({{< relref "/guides/core/artifacts" >}}) UI, and include the full model lineage (see an example model checkpoint in the UI [here](https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage?_gl=1*yyql5q*_ga*MTQxOTYyNzExOS4xNjg0NDYyNzk1*_ga_JH1SJHJQXJ*MTY5MjMwNzI2Mi4yNjkuMS4xNjkyMzA5NjM2LjM3LjAuMA..)).
-
-To bookmark your best model checkpoints and centralize them across your team, you can link them to the [W&B Model Registry]({{< relref "/guides/models" >}}).
-
-Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate]({{< relref "/guides/core/automations/" >}}) downstream actions with webhooks or jobs.
-
-## Log images, text, and more
-
-The `WandbLogger` has `log_image`, `log_text` and `log_table` methods for logging media.
-
-You can also directly call `wandb.log` or `trainer.logger.experiment.log` to log other media types such as Audio, Molecules, Point Clouds, 3D Objects and more.
-
-{{< tabpane text=true >}}
-
-{{% tab header="Log Images" value="images" %}}
-
-```python
-# using tensors, numpy arrays or PIL images
-wandb_logger.log_image(key="samples", images=[img1, img2])
-
-# adding captions
-wandb_logger.log_image(key="samples", images=[img1, img2], caption=["tree", "person"])
-
-# using file path
-wandb_logger.log_image(key="samples", images=["img_1.jpg", "img_2.jpg"])
-
-# using .log in the trainer
-trainer.logger.experiment.log(
- {"samples": [wandb.Image(img, caption=caption) for (img, caption) in my_images]},
- step=current_trainer_global_step,
-)
-```
-
-{{% /tab %}}
-
-{{% tab header="Log Text" value="text" %}}
-
-```python
-# data should be a list of lists
-columns = ["input", "label", "prediction"]
-my_data = [["cheese", "english", "english"], ["fromage", "french", "spanish"]]
-
-# using columns and data
-wandb_logger.log_text(key="my_samples", columns=columns, data=my_data)
-
-# using a pandas DataFrame
-wandb_logger.log_text(key="my_samples", dataframe=my_dataframe)
-```
-
-{{% /tab %}}
-
-{{% tab header="Log Tables" value="tables" %}}
-
-```python
-# log a W&B Table that has a text caption, an image and audio
-columns = ["caption", "image", "sound"]
-
-# data should be a list of lists
-my_data = [
- ["cheese", wandb.Image(img_1), wandb.Audio(snd_1)],
- ["wine", wandb.Image(img_2), wandb.Audio(snd_2)],
-]
-
-# log the Table
-wandb_logger.log_table(key="my_samples", columns=columns, data=data)
-```
-
-{{% /tab %}}
-
-{{< /tabpane >}}
-
-You can use Lightning's Callbacks system to control when you log to W&B via the `WandbLogger`, in this example we log a sample of our validation images and predictions:
-
-
-```python
-import torch
-import wandb
-import lightning.pytorch as pl
-from lightning.pytorch.loggers import WandbLogger
-
-# or
-# from wandb.integration.lightning.fabric import WandbLogger
-
-
-class LogPredictionSamplesCallback(Callback):
- def on_validation_batch_end(
- self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
- ):
- """Called when the validation batch ends."""
-
- # `outputs` comes from `LightningModule.validation_step`
- # which corresponds to our model predictions in this case
-
- # Let's log 20 sample image predictions from the first batch
- if batch_idx == 0:
- n = 20
- x, y = batch
- images = [img for img in x[:n]]
- captions = [
- f"Ground Truth: {y_i} - Prediction: {y_pred}"
- for y_i, y_pred in zip(y[:n], outputs[:n])
- ]
-
- # Option 1: log images with `WandbLogger.log_image`
- wandb_logger.log_image(key="sample_images", images=images, caption=captions)
-
- # Option 2: log images and predictions as a W&B Table
- columns = ["image", "ground truth", "prediction"]
- data = [
- [wandb.Image(x_i), y_i, y_pred] or x_i,
- y_i,
- y_pred in list(zip(x[:n], y[:n], outputs[:n])),
- ]
- wandb_logger.log_table(key="sample_table", columns=columns, data=data)
-
-
-trainer = pl.Trainer(callbacks=[LogPredictionSamplesCallback()])
-```
-
-## Use multiple GPUs with Lightning and W&B
-
-PyTorch Lightning has Multi-GPU support through their DDP Interface. However, PyTorch Lightning's design requires you to be careful about how you instantiate our GPUs.
-
-Lightning assumes that each GPU (or Rank) in your training loop must be instantiated in exactly the same way - with the same initial conditions. However, only rank 0 process gets access to the `wandb.run` object, and for non-zero rank processes: `wandb.run = None`. This could cause your non-zero processes to fail. Such a situation can put you in a **deadlock** because rank 0 process will wait for the non-zero rank processes to join, which have already crashed.
-
-For this reason, be careful about how we set up your training code. The recommended way to set it up would be to have your code be independent of the `wandb.run` object.
-
-```python
-class MNISTClassifier(pl.LightningModule):
- def __init__(self):
- super(MNISTClassifier, self).__init__()
-
- self.model = nn.Sequential(
- nn.Flatten(),
- nn.Linear(28 * 28, 128),
- nn.ReLU(),
- nn.Linear(128, 10),
- )
-
- self.loss = nn.CrossEntropyLoss()
-
- def forward(self, x):
- return self.model(x)
-
- def training_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self.forward(x)
- loss = self.loss(y_hat, y)
-
- self.log("train/loss", loss)
- return {"train_loss": loss}
-
- def validation_step(self, batch, batch_idx):
- x, y = batch
- y_hat = self.forward(x)
- loss = self.loss(y_hat, y)
-
- self.log("val/loss", loss)
- return {"val_loss": loss}
-
- def configure_optimizers(self):
- return torch.optim.Adam(self.parameters(), lr=0.001)
-
-
-def main():
- # Setting all the random seeds to the same value.
- # This is important in a distributed training setting.
- # Each rank will get its own set of initial weights.
- # If they don't match up, the gradients will not match either,
- # leading to training that may not converge.
- pl.seed_everything(1)
-
- train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)
- val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=4)
-
- model = MNISTClassifier()
- wandb_logger = WandbLogger(project="")
- callbacks = [
- ModelCheckpoint(
- dirpath="checkpoints",
- every_n_train_steps=100,
- ),
- ]
- trainer = pl.Trainer(
- max_epochs=3, gpus=2, logger=wandb_logger, strategy="ddp", callbacks=callbacks
- )
- trainer.fit(model, train_loader, val_loader)
-```
-
-
-
-## Examples
-
-You can follow along in a [video tutorial with a Colab notebook](https://wandb.me/lit-colab).
-
-## Frequently Asked Questions
-
-### How does W&B integrate with Lightning?
-
-The core integration is based on the [Lightning `loggers` API](https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html), which lets you write much of your logging code in a framework-agnostic way. `Logger`s are passed to the [Lightning `Trainer`](https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html) and are triggered based on that API's rich [hook-and-callback system](https://pytorch-lightning.readthedocs.io/en/stable/extensions/callbacks.html). This keeps your research code well-separated from engineering and logging code.
-
-### What does the integration log without any additional code?
-
-We'll save your model checkpoints to W&B, where you can view them or download them for use in future runs. We'll also capture [system metrics]({{< relref "/ref/python/experiments/system-metrics.md" >}}), like GPU usage and network I/O, environment information, like hardware and OS information, [code state]({{< relref "/guides/models/app/features/panels/code.md" >}}) (including git commit and diff patch, notebook contents and session history), and anything printed to the standard out.
-
-### What if I need to use `wandb.run` in my training setup?
-
-You need to expand the scope of the variable you need to access yourself. In other words, make sure that the initial conditions are the same on all processes.
-
-```python
-if os.environ.get("LOCAL_RANK", None) is None:
- os.environ["WANDB_DIR"] = wandb.run.dir
-```
-
-If they are, you can use `os.environ["WANDB_DIR"]` to set up the model checkpoints directory. This way, any non-zero rank process can access `wandb.run.dir`.
\ No newline at end of file
diff --git a/content/en/guides/integrations/metaflow.md b/content/en/guides/integrations/metaflow.md
deleted file mode 100644
index b8b56042de..0000000000
--- a/content/en/guides/integrations/metaflow.md
+++ /dev/null
@@ -1,218 +0,0 @@
----
-description: How to integrate W&B with Metaflow.
-menu:
- default:
- identifier: metaflow
- parent: integrations
-title: Metaflow
-weight: 200
----
-## Overview
-
-[Metaflow](https://docs.metaflow.org) is a framework created by [Netflix](https://netflixtechblog.com) for creating and running ML workflows.
-
-This integration lets users apply decorators to Metaflow [steps and flows](https://docs.metaflow.org/metaflow/basics) to automatically log parameters and artifacts to W&B.
-
-* Decorating a step will turn logging off or on for certain types within that step.
-* Decorating the flow will turn logging off or on for every step in the flow.
-
-## Quickstart
-
-### Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{% alert %}}
-For `wandb` version 0.19.8 or below, install `fastcore` version 1.8.0 or below (`fastcore<1.8.0`) instead of `plum-dispatch`.
-{{% /alert %}}
-
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install -Uqqq metaflow "plum-dispatch<3.0.0" wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install -Uqqq metaflow "plum-dispatch<3.0.0" wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install -Uqqq metaflow "plum-dispatch<3.0.0" wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-### Decorate your flows and steps
-
-{{< tabpane text=true >}}
-{{% tab header="Step" value="step" %}}
-
-Decorating a step turns logging off or on for certain types within that step.
-
-In this example, all datasets and models in `start` will be logged
-
-```python
-from wandb.integration.metaflow import wandb_log
-
-class WandbExampleFlow(FlowSpec):
- @wandb_log(datasets=True, models=True, settings=wandb.Settings(...))
- @step
- def start(self):
- self.raw_df = pd.read_csv(...). # pd.DataFrame -> upload as dataset
- self.model_file = torch.load(...) # nn.Module -> upload as model
- self.next(self.transform)
-```
-{{% /tab %}}
-
-{{% tab header="Flow" value="flow" %}}
-
-Decorating a flow is equivalent to decorating all the constituent steps with a default.
-
-In this case, all steps in `WandbExampleFlow` default to logging datasets and models by default, just like decorating each step with `@wandb_log(datasets=True, models=True)`
-
-```python
-from wandb.integration.metaflow import wandb_log
-
-@wandb_log(datasets=True, models=True) # decorate all @step
-class WandbExampleFlow(FlowSpec):
- @step
- def start(self):
- self.raw_df = pd.read_csv(...). # pd.DataFrame -> upload as dataset
- self.model_file = torch.load(...) # nn.Module -> upload as model
- self.next(self.transform)
-```
-{{% /tab %}}
-
-{{% tab header="Flow and Steps" value="flow_and_steps" %}}
-
-Decorating the flow is equivalent to decorating all steps with a default. That means if you later decorate a Step with another `@wandb_log`, it overrides the flow-level decoration.
-
-In this example:
-
-* `start` and `mid` log both datasets and models.
-* `end` logs neither datasets nor models.
-
-```python
-from wandb.integration.metaflow import wandb_log
-
-@wandb_log(datasets=True, models=True) # same as decorating start and mid
-class WandbExampleFlow(FlowSpec):
- # this step will log datasets and models
- @step
- def start(self):
- self.raw_df = pd.read_csv(...). # pd.DataFrame -> upload as dataset
- self.model_file = torch.load(...) # nn.Module -> upload as model
- self.next(self.mid)
-
- # this step will also log datasets and models
- @step
- def mid(self):
- self.raw_df = pd.read_csv(...). # pd.DataFrame -> upload as dataset
- self.model_file = torch.load(...) # nn.Module -> upload as model
- self.next(self.end)
-
- # this step is overwritten and will NOT log datasets OR models
- @wandb_log(datasets=False, models=False)
- @step
- def end(self):
- self.raw_df = pd.read_csv(...).
- self.model_file = torch.load(...)
-```
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Access your data programmatically
-
-You can access the information we've captured in three ways: inside the original Python process being logged using the [`wandb` client library]({{< relref "/ref/python/" >}}), with the [web app UI]({{< relref "/guides/models/track/workspaces.md" >}}), or programmatically using [our Public API]({{< relref "/ref/python/public-api/" >}}). `Parameter`s are saved to W&B's [`config`]({{< relref "/guides/models/track/config.md" >}}) and can be found in the [Overview tab]({{< relref "/guides/models/track/runs/#overview-tab" >}}). `datasets`, `models`, and `others` are saved to [W&B Artifacts]({{< relref "/guides/core/artifacts/" >}}) and can be found in the [Artifacts tab]({{< relref "/guides/models/track/runs/#artifacts-tab" >}}). Base python types are saved to W&B's [`summary`]({{< relref "/guides/models/track/log/" >}}) dict and can be found in the Overview tab. See our [guide to the Public API]({{< relref "/guides/models/track/public-api-guide.md" >}}) for details on using the API to get this information programmatically from outside .
-
-### Quick reference
-
-| Data | Client library | UI |
-| ----------------------------------------------- | ----------------------------------------- | --------------------- |
-| `Parameter(...)` | `wandb.Run.config` | Overview tab, Config |
-| `datasets`, `models`, `others` | `wandb.Run.use_artifact("{var_name}:latest")` | Artifacts tab |
-| Base Python types (`dict`, `list`, `str`, etc.) | `wandb.Run.summary` | Overview tab, Summary |
-
-### `wandb_log` kwargs
-
-| kwarg | Options |
-| ---------- | ------------------------------------------------------------------------------- |
-| `datasets` |
True: Log instance variables that are a dataset
False
|
-| `models` |
True: Log instance variables that are a model
False
|
-| `others` |
True: Log anything else that is serializable as a pickle
False
|
-| `settings` |
wandb.Settings(...): Specify your own wandb settings for this step or flow
None: Equivalent to passing wandb.Settings()
By default, if:
settings.run_group is None, it will be set to \{flow_name\}/\{run_id\}
settings.run_job_type is None, it will be set to \{run_job_type\}/\{step_name\}
|
-
-## Frequently Asked Questions
-
-### What exactly do you log? Do you log all instance and local variables?
-
-`wandb_log` only logs instance variables. Local variables are NEVER logged. This is useful to avoid logging unnecessary data.
-
-### Which data types get logged?
-
-We currently support these types:
-
-| Logging Setting | Type |
-| ------------------- | --------------------------------------------------------------------------------------------------------------------------- |
-| default (always on) |
dict, list, set, str, int, float, bool
|
-| `datasets` |
pd.DataFrame
pathlib.Path
|
-| `models` |
nn.Module
sklearn.base.BaseEstimator
|
-| `others` |
Anything that is pickle-able and JSON serializable
|
-
-### How can I configure logging behavior?
-
-| Kind of Variable | behavior | Example | Data Type |
-| ---------------- | ------------------------------ | --------------- | -------------- |
-| Instance | Auto-logged | `self.accuracy` | `float` |
-| Instance | Logged if `datasets=True` | `self.df` | `pd.DataFrame` |
-| Instance | Not logged if `datasets=False` | `self.df` | `pd.DataFrame` |
-| Local | Never logged | `accuracy` | `float` |
-| Local | Never logged | `df` | `pd.DataFrame` |
-
-### Is artifact lineage tracked?
-
-Yes. If you have an artifact that is an output of step A and an input to step B, we automatically construct the lineage DAG for you.
-
-For an example of this behavior, please see this[ notebook](https://colab.research.google.com/drive/1wZG-jYzPelk8Rs2gIM3a71uEoG46u_nG#scrollTo=DQQVaKS0TmDU) and its corresponding [W&B Artifacts page](https://wandb.ai/megatruong/metaflow_integration/artifacts/dataset/raw_df/7d14e6578d3f1cfc72fe/graph)
\ No newline at end of file
diff --git a/content/en/guides/integrations/mmengine.md b/content/en/guides/integrations/mmengine.md
deleted file mode 100644
index 5116f6864e..0000000000
--- a/content/en/guides/integrations/mmengine.md
+++ /dev/null
@@ -1,125 +0,0 @@
----
-menu:
- default:
- identifier: mmengine
- parent: integrations
-title: MMEngine
-weight: 210
----
-MMEngine by [OpenMMLab](https://github.com/open-mmlab) is a foundational library for training deep learning models based on PyTorch. MMEngine implements a next-generation training architecture for the OpenMMLab algorithm library, providing a unified execution foundation for over 30 algorithm libraries within OpenMMLab. Its core components include the training engine, evaluation engine, and module management.
-
-[W&B](https://wandb.ai/site) is directly integrated into MMEngine through a dedicated [`WandbVisBackend`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.visualization.WandbVisBackend.html#mmengine.visualization.WandbVisBackend) that can be used to
-- log training and evaluation metrics.
-- log and manage experiment configs.
-- log additional records such as graph, images, scalars, etc.
-
-## Get started
-
-Install `openmim` and `wandb`.
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="script" %}}
-
-``` bash
-pip install -q -U openmim wandb
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-``` bash
-!pip install -q -U openmim wandb
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-Next, install `mmengine` and `mmcv` using `mim`.
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="script" %}}
-
-``` bash
-mim install -q mmengine mmcv
-```
-
-{{% /tab %}}
-
-{{% tab header="Notebook" value="notebook" %}}
-
-``` bash
-!mim install -q mmengine mmcv
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Use the `WandbVisBackend` with MMEngine Runner
-
-This section demonstrates a typical workflow using `WandbVisBackend` using [`mmengine.runner.Runner`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.Runner.html#mmengine.runner.Runner).
-
-1. Define a `visualizer` from a visualization config.
-
- ```python
- from mmengine.visualization import Visualizer
-
- # define the visualization configs
- visualization_cfg = dict(
- name="wandb_visualizer",
- vis_backends=[
- dict(
- type='WandbVisBackend',
- init_kwargs=dict(project="mmengine"),
- )
- ],
- save_dir="runs/wandb"
- )
-
- # get the visualizer from the visualization configs
- visualizer = Visualizer.get_instance(**visualization_cfg)
- ```
-
- {{% alert %}}
- You pass a dictionary of arguments for [W&B run initialization]({{< relref "/ref/python/functions/init.md" >}}) input parameters to `init_kwargs`.
- {{% /alert %}}
-
-2. Initialize a `runner` with the `visualizer`, and call `runner.train()`.
-
- ```python
- from mmengine.runner import Runner
-
- # build the mmengine Runner which is a training helper for PyTorch
- runner = Runner(
- model,
- work_dir='runs/gan/',
- train_dataloader=train_dataloader,
- train_cfg=train_cfg,
- optim_wrapper=opt_wrapper_dict,
- visualizer=visualizer, # pass the visualizer
- )
-
- # start training
- runner.train()
- ```
-
-## Use the `WandbVisBackend` with OpenMMLab computer vision libraries
-
-The `WandbVisBackend` can also be used easily to track experiments with OpenMMLab computer vision libraries such as [MMDetection](https://mmdetection.readthedocs.io/).
-
-```python
-# inherit base configs from the default runtime configs
-_base_ = ["../_base_/default_runtime.py"]
-
-# Assign the `WandbVisBackend` config dictionary to the
-# `vis_backends` of the `visualizer` from the base configs
-_base_.visualizer.vis_backends = [
- dict(
- type='WandbVisBackend',
- init_kwargs={
- 'project': 'mmdet',
- 'entity': 'geekyrakshit'
- },
- ),
-]
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/mmf.md b/content/en/guides/integrations/mmf.md
deleted file mode 100644
index a397f25f3d..0000000000
--- a/content/en/guides/integrations/mmf.md
+++ /dev/null
@@ -1,55 +0,0 @@
----
-description: How to integrate W&B with Meta AI's MMF.
-menu:
- default:
- identifier: mmf
- parent: integrations
-title: MMF
-weight: 220
----
-
-The `WandbLogger` class in [Meta AI's MMF](https://github.com/facebookresearch/mmf) library will enable W&B to log the training/validation metrics, system (GPU and CPU) metrics, model checkpoints and configuration parameters.
-
-## Current features
-
-The following features are currently supported by the `WandbLogger` in MMF:
-
-* Training & Validation metrics
-* Learning Rate over time
-* Model Checkpoint saving to W&B Artifacts
-* GPU and CPU system metrics
-* Training configuration parameters
-
-## Config parameters
-
-The following options are available in MMF config to enable and customize the wandb logging:
-
-```
-training:
- wandb:
- enabled: true
-
- # An entity is a username or team name where you're sending runs.
- # By default it will log the run to your user account.
- entity: null
-
- # Project name to be used while logging the experiment with wandb
- project: mmf
-
- # Experiment/ run name to be used while logging the experiment
- # under the project with wandb. The default experiment name
- # is: ${training.experiment_name}
- name: ${training.experiment_name}
-
- # Turn on model checkpointing, saving checkpoints to W&B Artifacts
- log_model_checkpoint: true
-
- # Additional argument values that you want to pass to wandb.init() such as:
- # job_type: 'train'
- # tags: ['tag1', 'tag2']
-
-env:
- # To change the path to the directory where wandb metadata would be
- # stored (Default: env.log_dir):
- wandb_logdir: ${env:MMF_WANDB_LOGDIR,}
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/openai-api.md b/content/en/guides/integrations/openai-api.md
deleted file mode 100644
index 5fef255010..0000000000
--- a/content/en/guides/integrations/openai-api.md
+++ /dev/null
@@ -1,82 +0,0 @@
----
-description: How to use W&B with the OpenAI API.
-menu:
- default:
- identifier: openai-api
- parent: integrations
-title: OpenAI API
-weight: 240
----
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/master/colabs/openai/OpenAI_API_Autologger_Quickstart.ipynb" >}}
-
-Use the W&B OpenAI API integration to log requests, responses, token counts and model metadata for all OpenAI models, including fine-tuned models.
-
-
-{{% alert %}}
-See the [OpenAI fine-tuning integration]({{< relref "./openai-fine-tuning.md" >}}) to learn how to use W&B to track your fine-tuning experiments, models, and datasets and share your results with your colleagues.
-{{% /alert %}}
-
-Log your API inputs and outputs you can quickly evaluate the performance of difference prompts, compare different model settings (such as temperature), and track other usage metrics such as token usage.
-
-
-
-
-{{< img src="/images/integrations/open_ai_autolog.png" alt="OpenAI API automatic logging" >}}
-
-
-## Install OpenAI Python API library
-
-The W&B autolog integration works with OpenAI version 0.28.1 and below.
-
-To install OpenAI Python API version 0.28.1, run:
-```python
-pip install openai==0.28.1
-```
-
-## Use the OpenAI Python API
-
-### 1. Import autolog and initialise it
-First, import `autolog` from `wandb.integration.openai` and initialise it.
-
-```python
-import os
-import openai
-from wandb.integration.openai import autolog
-
-autolog({"project": "gpt5"})
-```
-
-You can optionally pass a dictionary with argument that `wandb.init()` accepts to `autolog`. This includes a project name, team name, entity, and more. For more information about [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}), see the API Reference Guide.
-
-### 2. Call the OpenAI API
-Each call you make to the OpenAI API is now logged to W&B automatically.
-
-```python
-os.environ["OPENAI_API_KEY"] = "XXX"
-
-chat_request_kwargs = dict(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": "You are a helpful assistant."},
- {"role": "user", "content": "Who won the world series in 2020?"},
- {"role": "assistant", "content": "The Los Angeles Dodgers"},
- {"role": "user", "content": "Where was it played?"},
- ],
-)
-response = openai.ChatCompletion.create(**chat_request_kwargs)
-```
-
-### 3. View your OpenAI API inputs and responses
-
-Click on the W&B [run]({{< relref "/guides/models/track/runs/" >}}) link generated by `autolog` in **step 1**. This redirects you to your project workspace in the W&B App.
-
-Select a run you created to view the trace table, trace timeline and the model architecture of the OpenAI LLM used.
-
-## Turn off autolog
-W&B recommends that you call `disable()` to close all W&B processes when you are finished using the OpenAI API.
-
-```python
-autolog.disable()
-```
-
-Now your inputs and completions will be logged to W&B, ready for analysis or to be shared with colleagues.
\ No newline at end of file
diff --git a/content/en/guides/integrations/openai-fine-tuning.md b/content/en/guides/integrations/openai-fine-tuning.md
deleted file mode 100644
index 0cab4ce77e..0000000000
--- a/content/en/guides/integrations/openai-fine-tuning.md
+++ /dev/null
@@ -1,176 +0,0 @@
----
-description: How to Fine-Tune OpenAI models using W&B.
-menu:
- default:
- identifier: openai-fine-tuning
- parent: integrations
-title: OpenAI Fine-Tuning
-weight: 250
----
-{{< cta-button colabLink="https://wandb.me/openai-colab" >}}
-
-Log your OpenAI GPT-3.5 or GPT-4 model's fine-tuning metrics and configuration to W&B. Utilize the W&B ecosystem to track your fine-tuning experiments, models, and datasets and share your results with your colleagues.
-
-{{% alert %}}
-See the [OpenAI documentation](https://platform.openai.com/docs/guides/fine-tuning/which-models-can-be-fine-tuned) for a list of models that you can fine tune.
-{{% /alert %}}
-
-See the [W&B Integration](https://platform.openai.com/docs/guides/fine-tuning/weights-and-biases-integration) section in the OpenAI documentation for supplemental information on how to integrate W&B with OpenAI for fine-tuning.
-
-
-## Install or update OpenAI Python API
-
-The W&B OpenAI fine-tuning integration works with OpenAI version 1.0 and above. See the PyPI documentation for the latest version of the [OpenAI Python API](https://pypi.org/project/openai/) library.
-
-
-To install OpenAI Python API, run:
-```python
-pip install openai
-```
-
-If you already have OpenAI Python API installed, you can update it with:
-```python
-pip install -U openai
-```
-
-
-## Sync your OpenAI fine-tuning results
-
-Integrate W&B with OpenAI's fine-tuning API to log your fine-tuning metrics and configuration to W&B. To do this, use the `WandbLogger` class from the `wandb.integration.openai.fine_tuning` module.
-
-
-```python
-from wandb.integration.openai.fine_tuning import WandbLogger
-
-# Finetuning logic
-
-WandbLogger.sync(fine_tune_job_id=FINETUNE_JOB_ID)
-```
-
-{{< img src="/images/integrations/open_ai_auto_scan.png" alt="OpenAI auto-scan feature" >}}
-
-### Sync your fine-tunes
-
-Sync your results from your script
-
-
-```python
-from wandb.integration.openai.fine_tuning import WandbLogger
-
-# one line command
-WandbLogger.sync()
-
-# passing optional parameters
-WandbLogger.sync(
- fine_tune_job_id=None,
- num_fine_tunes=None,
- project="OpenAI-Fine-Tune",
- entity=None,
- overwrite=False,
- model_artifact_name="model-metadata",
- model_artifact_type="model",
- **kwargs_wandb_init
-)
-```
-
-### Reference
-
-| Argument | Description |
-| ------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
-| fine_tune_job_id | This is the OpenAI Fine-Tune ID which you get when you create your fine-tune job using `client.fine_tuning.jobs.create`. If this argument is None (default), all the OpenAI fine-tune jobs that haven't already been synced will be synced to W&B. |
-| openai_client | Pass an initialized OpenAI client to `sync`. If no client is provided, one is initialized by the logger itself. By default it is None. |
-| num_fine_tunes | If no ID is provided, then all the unsynced fine-tunes will be logged to W&B. This argument allows you to select the number of recent fine-tunes to sync. If num_fine_tunes is 5, it selects the 5 most recent fine-tunes. |
-| project | W&B project name where your fine-tune metrics, models, data, etc. will be logged. By default, the project name is "OpenAI-Fine-Tune." |
-| entity | W&B Username or team name where you're sending runs. By default, your default entity is used, which is usually your username. |
-| overwrite | Forces logging and overwrite existing wandb run of the same fine-tune job. By default this is False. |
-| wait_for_job_success | Once an OpenAI fine-tuning job is started it usually takes a bit of time. To ensure that your metrics are logged to W&B as soon as the fine-tune job is finished, this setting will check every 60 seconds for the status of the fine-tune job to change to `succeeded`. Once the fine-tune job is detected as being successful, the metrics will be synced automatically to W&B. Set to True by default. |
-| model_artifact_name | The name of the model artifact that is logged. Defaults to `"model-metadata"`. |
-| model_artifact_type | The type of the model artifact that is logged. Defaults to `"model"`. |
-| \*\*kwargs_wandb_init | Aany additional argument passed directly to [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) |
-
-## Dataset Versioning and Visualization
-
-### Versioning
-
-The training and validation data that you upload to OpenAI for fine-tuning are automatically logged as W&B Artifacts for easier version control. Below is an view of the training file in Artifacts. Here you can see the W&B run that logged this file, when it was logged, what version of the dataset this is, the metadata, and DAG lineage from the training data to the trained model.
-
-{{< img src="/images/integrations/openai_data_artifacts.png" alt="W&B Artifacts with training datasets" >}}
-
-### Visualization
-
-The datasets are visualized as W&B Tables, which allows you to explore, search, and interact with the dataset. Check out the training samples visualized using W&B Tables below.
-
-{{< img src="/images/integrations/openai_data_visualization.png" alt="OpenAI data" >}}
-
-
-## The fine-tuned model and model versioning
-
-OpenAI gives you an id of the fine-tuned model. Since we don't have access to the model weights, the `WandbLogger` creates a `model_metadata.json` file with all the details (hyperparameters, data file ids, etc.) of the model along with the `fine_tuned_model`` id and is logged as a W&B Artifact.
-
-This model (metadata) artifact can further be linked to a model in the [W&B Registry]({{< relref "/guides/core/registry/" >}}).
-
-{{< img src="/images/integrations/openai_model_metadata.png" alt="OpenAI model metadata" >}}
-
-
-## Frequently Asked Questions
-
-### How do I share my fine-tune results with my team in W&B?
-
-Log your fine-tune jobs to your team account with:
-
-```python
-WandbLogger.sync(entity="YOUR_TEAM_NAME")
-```
-
-### How can I organize my runs?
-
-Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate, training filename and any other hyper-parameter.
-
-In addition, you can rename your runs, add notes or create tags to group them.
-
-Once you’re satisfied, you can save your workspace and use it to create report, importing data from your runs and saved artifacts (training/validation files).
-
-### How can I access my fine-tuned model?
-
-Fine-tuned model ID is logged to W&B as artifacts (`model_metadata.json`) as well config.
-
-```python
-import wandb
-
-with wandb.init(project="OpenAI-Fine-Tune", entity="YOUR_TEAM_NAME") as run:
- ft_artifact = run.use_artifact("ENTITY/PROJECT/model_metadata:VERSION")
- artifact_dir = ft_artifact.download()
-```
-
-where `VERSION` is either:
-
-* a version number such as `v2`
-* the fine-tune id such as `ft-xxxxxxxxx`
-* an alias added automatically such as `latest` or manually
-
-You can then access `fine_tuned_model` id by reading the downloaded `model_metadata.json` file.
-
-### What if a fine-tune was not synced successfully?
-
-If a fine-tune was not logged to W&B successfully, you can use the `overwrite=True` and pass the fine-tune job id:
-
-```python
-WandbLogger.sync(
- fine_tune_job_id="FINE_TUNE_JOB_ID",
- overwrite=True,
-)
-```
-
-### Can I track my datasets and models with W&B?
-
-The training and validation data are logged automatically to W&B as artifacts. The metadata including the ID for the fine-tuned model is also logged as artifacts.
-
-You can always control the pipeline using low level wandb APIs like `wandb.Artifact`, `wandb.Run.log`, etc. This will allow complete traceability of your data and models.
-
-{{< img src="/images/integrations/open_ai_faq_can_track.png" alt="OpenAI tracking FAQ" >}}
-
-## Resources
-
-* [OpenAI Fine-tuning Documentation](https://platform.openai.com/docs/guides/fine-tuning/) is very thorough and contains many useful tips
-* [Demo Colab](https://wandb.me/openai-colab)
-* [How to Fine-Tune Your OpenAI GPT-3.5 and GPT-4 Models with W&B](https://wandb.me/openai-report) report
\ No newline at end of file
diff --git a/content/en/guides/integrations/openai-gym.md b/content/en/guides/integrations/openai-gym.md
deleted file mode 100644
index 650ccb263d..0000000000
--- a/content/en/guides/integrations/openai-gym.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-description: How to integrate W&B with OpenAI Gym.
-menu:
- default:
- identifier: openai-gym
- parent: integrations
-title: OpenAI Gym
-weight: 260
----
-
-{{% alert %}}
-"The team that has been maintaining Gym since 2021 has moved all future development to [Gymnasium](https://github.com/Farama-Foundation/Gymnasium), a drop in replacement for Gym (import gymnasium as gym), and Gym will not be receiving any future updates." ([Source](https://github.com/openai/gym#the-team-that-has-been-maintaining-gym-since-2021-has-moved-all-future-development-to-gymnasium-a-drop-in-replacement-for-gym-import-gymnasium-as-gym-and-gym-will-not-be-receiving-any-future-updates-please-switch-over-to-gymnasium-as-soon-as-youre-able-to-do-so-if-youd-like-to-read-more-about-the-story-behind-this-switch-please-check-out-this-blog-post))
-
-Since Gym is no longer an actively maintained project, try out our integration with Gymnasium.
-{{% /alert %}}
-
-If you're using [OpenAI Gym](https://github.com/openai/gym), W&B automatically logs videos of your environment generated by `gym.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`]({{< relref "/ref/python/functions/init.md" >}}) to `True` or call `wandb.gym.monitor()`.
-
-Our gym integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/master/wandb/integration/gym/__init__.py#L15) being logged from `gym` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video]({{< relref "/guides/models/track/log/media.md" >}}).
-
-The [OpenRL Benchmark](https://wandb.me/openrl-benchmark-report) by[ CleanRL](https://github.com/vwxyzjn/cleanrl) uses this integration for its OpenAI Gym examples. You can find source code (including [the specific code used for specific runs](https://wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang)) that demonstrates how to use gym with
-
-{{< img src="/images/integrations/open_ai_report_example.png" alt="OpenAI Gym dashboard" >}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/paddledetection.md b/content/en/guides/integrations/paddledetection.md
deleted file mode 100644
index 7ac2961f30..0000000000
--- a/content/en/guides/integrations/paddledetection.md
+++ /dev/null
@@ -1,120 +0,0 @@
----
-description: How to integrate W&B with PaddleDetection.
-menu:
- default:
- identifier: paddledetection
- parent: integrations
-title: PaddleDetection
-weight: 270
----
-
-{{< cta-button colabLink="https://colab.research.google.com/drive/1ywdzcZKPmynih1GuGyCWB4Brf5Jj7xRY?usp=sharing" >}}
-
-[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) is an end-to-end object-detection development kit based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle). It detects various mainstream objects, segments instances, and tracks and detects keypoints using configurable modules such as network components, data augmentations, and losses.
-
-PaddleDetection now includes a built-in W&B integration which logs all your training and validation metrics, as well as your model checkpoints and their corresponding metadata.
-
-The PaddleDetection `WandbLogger` logs your training and evaluation metrics to W&B as well as your model checkpoints while training.
-
-[Read a W&B blog post](https://wandb.ai/manan-goel/PaddleDetectionYOLOX/reports/Object-Detection-with-PaddleDetection-and-W-B--VmlldzoyMDU4MjY0) which illustrates how to integrate a YOLOX model with PaddleDetection on a subset of the `COCO2017` dataset.
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Activate the `WandbLogger` in your training script
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-To use wandb via arguments to `train.py` in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/):
-
-* Add the `--use_wandb` flag
-* The first wandb arguments must be preceded by `-o` (you only need to pass this once)
-* Each individual argument must contain the prefix `"wandb-"` . For example any argument to be passed to [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) would get the `wandb-` prefix
-
-```shell
-python tools/train.py
- -c config.yml \
- --use_wandb \
- -o \
- wandb-project=MyDetector \
- wandb-entity=MyTeam \
- wandb-save_dir=./logs
-```
-{{% /tab %}}
-{{% tab header="`config.yml`" value="config" %}}
-Add the wandb arguments to the config.yml file under the `wandb` key:
-
-```
-wandb:
- project: MyProject
- entity: MyTeam
- save_dir: ./logs
-```
-
-When you run your `train.py` file, it generates a link to your W&B Dashboard.
-
-{{< img src="/images/integrations/paddledetection_wb_dashboard.png" alt="A W&B Dashboard" >}}
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Feedback or issues
-
-If you have any feedback or issues about the W&B integration, open an issue on the [PaddleDetection GitHub](https://github.com/PaddlePaddle/PaddleDetection) or email support@wandb.com.
\ No newline at end of file
diff --git a/content/en/guides/integrations/paddleocr.md b/content/en/guides/integrations/paddleocr.md
deleted file mode 100644
index d8653b5d72..0000000000
--- a/content/en/guides/integrations/paddleocr.md
+++ /dev/null
@@ -1,113 +0,0 @@
----
-description: How to integrate W&B with PaddleOCR.
-menu:
- default:
- identifier: paddleocr
- parent: integrations
-title: PaddleOCR
-weight: 280
----
-[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice implemented in PaddlePaddle. PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial solution. PaddleOCR now comes with a W&B integration for logging training and evaluation metrics along with model checkpoints with corresponding metadata.
-
-## Example Blog & Colab
-
-[Read here](https://wandb.ai/manan-goel/text_detection/reports/Train-and-Debug-Your-OCR-Models-with-PaddleOCR-and-W-B--VmlldzoyMDUwMDIw) to see how to train a model with PaddleOCR on the ICDAR2015 dataset. This also comes with a [Google Colab](https://colab.research.google.com/drive/1id2VTIQ5-M1TElAkzjzobUCdGeJeW-nV?usp=sharing) and the corresponding live W&B dashboard is available [here](https://wandb.ai/manan-goel/text_detection). There is also a Chinese version of this blog here: [W&B对您的OCR模型进行训练和调试](https://wandb.ai/wandb_fc/chinese/reports/W-B-OCR---VmlldzoyMDk1NzE4)
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Add wandb to your `config.yml` file
-
-PaddleOCR requires configuration variables to be provided using a yaml file. Adding the following snippet at the end of the configuration yaml file will automatically log all training and validation metrics to a W&B dashboard along with model checkpoints:
-
-```python
-Global:
- use_wandb: True
-```
-
-Any additional, optional arguments that you might like to pass to [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) can also be added under the `wandb` header in the yaml file:
-
-```
-wandb:
- project: CoolOCR # (optional) this is the wandb project name
- entity: my_team # (optional) if you're using a wandb team, you can pass the team name here
- name: MyOCRModel # (optional) this is the name of the wandb run
-```
-
-## Pass the `config.yml` file to `train.py`
-
-The yaml file is then provided as an argument to the [training script](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/tools/train.py) available in the PaddleOCR repository.
-
-```bash
-python tools/train.py -c config.yml
-```
-
-Once you run your `train.py` file with W&B turned on, a link will be generated to bring you to your W&B dashboard:
-
-{{< img src="/images/integrations/paddleocr_wb_dashboard1.png" alt="PaddleOCR training dashboard" >}}
-
-{{< img src="/images/integrations/paddleocr_wb_dashboard2.png" alt="PaddleOCR validation dashboard" >}}
-
-{{< img src="/images/integrations/paddleocr_wb_dashboard3.png" alt="Text Detection Model dashboard" >}}
-
-## Feedback or issues
-
-If you have any feedback or issues about the W&B integration, open an issue on the [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR) or email support@wandb.com.
\ No newline at end of file
diff --git a/content/en/guides/integrations/prodigy.md b/content/en/guides/integrations/prodigy.md
deleted file mode 100644
index 6f36af1002..0000000000
--- a/content/en/guides/integrations/prodigy.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-description: How to integrate W&B with Prodigy.
-menu:
- default:
- identifier: prodigy
- parent: integrations
-title: Prodigy
-weight: 290
----
-
-[Prodigy](https://prodi.gy/) is an annotation tool for creating training and evaluation data for machine learning models, error analysis, data inspection & cleaning. [W&B Tables]({{< relref "/guides/models/tables/tables-walkthrough.md" >}}) allow you to log, visualize, analyze, and share datasets (and more!) inside W&B.
-
-The [W&B integration with Prodigy](https://github.com/wandb/wandb/blob/master/wandb/integration/prodigy/prodigy.py) adds simple and easy-to-use functionality to upload your Prodigy-annotated dataset directly to W&B for use with Tables.
-
-Run a few lines of code, like these:
-
-```python
-import wandb
-from wandb.integration.prodigy import upload_dataset
-
-with wandb.init(project="prodigy"):
- upload_dataset("news_headlines_ner")
-```
-
-and get visual, interactive, shareable tables like this one:
-
-{{< img src="/images/integrations/prodigy_interactive_visual.png" alt="Prodigy annotation table" >}}
-
-## Quickstart
-
-Use `wandb.integration.prodigy.upload_dataset` to upload your annotated prodigy dataset directly from the local Prodigy database to W&B in our [Table]({{< relref "/ref/python/data-types/table.md" >}}) format. For more information on Prodigy, including installation & setup, please refer to the [Prodigy documentation](https://prodi.gy/docs/).
-
-W&B will automatically try to convert images and named entity fields to [`wandb.Image`]({{< relref "/ref/python/data-types/image.md" >}}) and [`wandb.Html`]({{< relref "/ref/python/data-types/html.md" >}})respectively. Extra columns may be added to the resulting table to include these visualizations.
-
-## Read through a detailed example
-
-Explore the [Visualizing Prodigy Datasets Using W&B Tables](https://wandb.ai/kshen/prodigy/reports/Visualizing-Prodigy-Datasets-Using-W-B-Tables--Vmlldzo5NDE2MTc) for example visualizations generated with W&B Prodigy integration.
-
-## Also using spaCy?
-
-W&B also has an integration with spaCy, see the [docs here]({{< relref "/guides/integrations/spacy" >}}).
\ No newline at end of file
diff --git a/content/en/guides/integrations/pytorch-geometric.md b/content/en/guides/integrations/pytorch-geometric.md
deleted file mode 100644
index b6531e9631..0000000000
--- a/content/en/guides/integrations/pytorch-geometric.md
+++ /dev/null
@@ -1,179 +0,0 @@
----
-menu:
- default:
- identifier: pytorch-geometric
- parent: integrations
-title: PyTorch Geometric
-weight: 310
----
-[PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) or PyG is one of the most popular libraries for geometric deep learning and W&B works extremely well with it for visualizing graphs and tracking experiments.
-
-After you have installed Pytorch Geometric, follow these steps to get started.
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Visualize the graphs
-
-You can save details about the input graphs including number of edges, number of nodes and more. W&B supports logging plotly charts and HTML panels so any visualizations you create for your graph can then also be logged to W&B.
-
-### Use PyVis
-
-The following snippet shows how you could do that with PyVis and HTML.
-
-```python
-from pyvis.network import Network
-import wandb
-
-with wandb.init(project=’graph_vis’) as run:
- net = Network(height="750px", width="100%", bgcolor="#222222", font_color="white")
-
- # Add the edges from the PyG graph to the PyVis network
- for e in tqdm(g.edge_index.T):
- src = e[0].item()
- dst = e[1].item()
-
- net.add_node(dst)
- net.add_node(src)
-
- net.add_edge(src, dst, value=0.1)
-
- # Save the PyVis visualisation to a HTML file
- net.show("graph.html")
- run.log({"eda/graph": wandb.Html("graph.html")})
-```
-
-{{< img src="/images/integrations/pyg_graph_wandb.png" alt="Interactive graph visualization" >}}
-
-### Use Plotly
-
-To use plotly to create a graph visualization, first you need to convert the PyG graph to a networkx object. Following this you will need to create Plotly scatter plots for both nodes and edges. The snippet below can be used for this task.
-
-```python
-def create_vis(graph):
- G = to_networkx(graph)
- pos = nx.spring_layout(G)
-
- edge_x = []
- edge_y = []
- for edge in G.edges():
- x0, y0 = pos[edge[0]]
- x1, y1 = pos[edge[1]]
- edge_x.append(x0)
- edge_x.append(x1)
- edge_x.append(None)
- edge_y.append(y0)
- edge_y.append(y1)
- edge_y.append(None)
-
- edge_trace = go.Scatter(
- x=edge_x, y=edge_y,
- line=dict(width=0.5, color='#888'),
- hoverinfo='none',
- mode='lines'
- )
-
- node_x = []
- node_y = []
- for node in G.nodes():
- x, y = pos[node]
- node_x.append(x)
- node_y.append(y)
-
- node_trace = go.Scatter(
- x=node_x, y=node_y,
- mode='markers',
- hoverinfo='text',
- line_width=2
- )
-
- fig = go.Figure(data=[edge_trace, node_trace], layout=go.Layout())
-
- return fig
-
-
-with wandb.init(project=’visualize_graph’) as run:
- run.log({‘graph’: wandb.Plotly(create_vis(graph))})
-```
-
-{{< img src="/images/integrations/pyg_graph_plotly.png" alt="A visualization created using the example function and logged inside a W&B Table." >}}
-
-## Log metrics
-
-You can use W&B to track your experiments and related metrics, such as loss functions, accuracy, and more. Add the following line to your training loop:
-
-```python
-with wandb.init(project="my_project", entity="my_entity") as run:
- run.log({
- 'train/loss': training_loss,
- 'train/acc': training_acc,
- 'val/loss': validation_loss,
- 'val/acc': validation_acc
- })
-```
-
-{{< img src="/images/integrations/pyg_metrics.png" alt="hits@K metrics over epochs" >}}
-
-## More resources
-
-- [Recommending Amazon Products using Graph Neural Networks in PyTorch Geometric](https://wandb.ai/manan-goel/gnn-recommender/reports/Recommending-Amazon-Products-using-Graph-Neural-Networks-in-PyTorch-Geometric--VmlldzozMTA3MzYw#what-does-the-data-look-like?)
-- [Point Cloud Classification using PyTorch Geometric](https://wandb.ai/geekyrakshit/pyg-point-cloud/reports/Point-Cloud-Classification-using-PyTorch-Geometric--VmlldzozMTExMTE3)
-- [Point Cloud Segmentation using PyTorch Geometric](https://wandb.ai/wandb/point-cloud-segmentation/reports/Point-Cloud-Segmentation-using-Dynamic-Graph-CNN--VmlldzozMTk5MDcy)
\ No newline at end of file
diff --git a/content/en/guides/integrations/pytorch.md b/content/en/guides/integrations/pytorch.md
deleted file mode 100644
index 8f73f04eb7..0000000000
--- a/content/en/guides/integrations/pytorch.md
+++ /dev/null
@@ -1,109 +0,0 @@
----
-menu:
- default:
- identifier: pytorch
- parent: integrations
-title: PyTorch
-weight: 300
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb" >}}
-
-PyTorch is one of the most popular frameworks for deep learning in Python, especially among researchers. W&B provides first class support for PyTorch, from logging gradients to profiling your code on the CPU and GPU.
-
-Try our integration out in a Colab notebook.
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch/Simple_PyTorch_Integration.ipynb" >}}
-
-You can also see our [example repo](https://github.com/wandb/examples) for scripts, including one on hyperparameter optimization using [Hyperband](https://arxiv.org/abs/1603.06560) on [Fashion MNIST](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion), plus the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
-
-## Log gradients with `run.watch`
-
-To automatically log gradients, you can call [`wandb.Run.watch()`]({{< relref "/ref/python/experiments/run.md/#method-runwatch" >}}) and pass in your PyTorch model.
-
-```python
-import wandb
-
-with wandb.init(config=args) as run:
-
- model = ... # set up your model
-
- # Magic
- run.watch(model, log_freq=100)
-
- model.train()
- for batch_idx, (data, target) in enumerate(train_loader):
- output = model(data)
- loss = F.nll_loss(output, target)
- loss.backward()
- optimizer.step()
- if batch_idx % args.log_interval == 0:
- run.log({"loss": loss})
-```
-
-If you need to track multiple models in the same script, you can call [`wandb.Run.watch()`]({{< relref "/ref/python/experiments/run/#method-runwatch" >}}) on each model separately.
-
-{{% alert color="secondary" %}}
-Gradients, metrics, and the graph won't be logged until `wandb.Run.log()` is called after a forward _and_ backward pass.
-{{% /alert %}}
-
-## Log images and media
-
-You can pass PyTorch `Tensors` with image data into [`wandb.Image`]({{< relref "/ref/python/data-types/image.md" >}}) and utilities from [`torchvision`](https://pytorch.org/vision/stable/index.html) will be used to convert them to images automatically:
-
-```python
-with wandb.init(project="my_project", entity="my_entity") as run:
- images_t = ... # generate or load images as PyTorch Tensors
- run.log({"examples": [wandb.Image(im) for im in images_t]})
-```
-
-For more on logging rich media to W&B in PyTorch and other frameworks, check out our [media logging guide]({{< relref "/guides/models/track/log/media.md" >}}).
-
-If you also want to include information alongside media, like your model's predictions or derived metrics, use a `wandb.Table`.
-
-```python
-with wandb.init() as run:
- my_table = wandb.Table()
-
- my_table.add_column("image", images_t)
- my_table.add_column("label", labels)
- my_table.add_column("class_prediction", predictions_t)
-
- # Log your Table to W&B
- run.log({"mnist_predictions": my_table})
-```
-
-{{< img src="/images/integrations/pytorch_example_table.png" alt="PyTorch model results" >}}
-
-For more on logging and visualizing datasets and models, check out our [guide to W&B Tables]({{< relref "/guides/models/tables/" >}}).
-
-## Profile PyTorch code
-
-{{< img src="/images/integrations/pytorch_example_dashboard.png" alt="PyTorch execution traces" >}}
-
-W&B integrates directly with [PyTorch Kineto](https://github.com/pytorch/kineto)'s [Tensorboard plugin](https://github.com/pytorch/kineto/blob/master/tb_plugin/README.md) to provide tools for profiling PyTorch code, inspecting the details of CPU and GPU communication, and identifying bottlenecks and optimizations.
-
-```python
-profile_dir = "path/to/run/tbprofile/"
-profiler = torch.profiler.profile(
- schedule=schedule, # see the profiler docs for details on scheduling
- on_trace_ready=torch.profiler.tensorboard_trace_handler(profile_dir),
- with_stack=True,
-)
-
-with profiler:
- ... # run the code you want to profile here
- # see the profiler docs for detailed usage information
-
-# create a wandb Artifact
-profile_art = wandb.Artifact("trace", type="profile")
-# add the pt.trace.json files to the Artifact
-profile_art.add_file(glob.glob(profile_dir + ".pt.trace.json"))
-# log the artifact
-profile_art.save()
-```
-
-See and run working example code in [this Colab](https://wandb.me/trace-colab).
-
-{{% alert color="secondary" %}}
-The interactive trace viewing tool is based on the Chrome Trace Viewer, which works best with the Chrome browser.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/ray-tune.md b/content/en/guides/integrations/ray-tune.md
deleted file mode 100644
index 5d662dfec2..0000000000
--- a/content/en/guides/integrations/ray-tune.md
+++ /dev/null
@@ -1,115 +0,0 @@
----
-description: How to integrate W&B with Ray Tune.
-menu:
- default:
- identifier: ray-tune
- parent: integrations
-title: Ray Tune
-weight: 360
----
-
-W&B integrates with [Ray](https://github.com/ray-project/ray) by offering two lightweight integrations.
-
-- The`WandbLoggerCallback` function automatically logs metrics reported to Tune to the Wandb API.
-- The `setup_wandb()` function, which can be used with the function API, automatically initializes the Wandb API with Tune's training information. You can use the Wandb API as usual. such as by using `run.log()` to log your training process.
-
-## Configure the integration
-
-```python
-from ray.air.integrations.wandb import WandbLoggerCallback
-```
-
-Wandb configuration is done by passing a wandb key to the config parameter of `tune.run()` (see example below).
-
-The content of the wandb config entry is passed to `wandb.init()` as keyword arguments. The exception are the following settings, which are used to configure the `WandbLoggerCallback` itself:
-
-### Parameters
-
-`project (str)`: Name of the Wandb project. Mandatory.
-
-`api_key_file (str)`: Path to file containing the Wandb API KEY.
-
-`api_key (str)`: Wandb API Key. Alternative to setting `api_key_file`.
-
-`excludes (list)`: List of metrics to exclude from the log.
-
-`log_config (bool)`: Whether to log the config parameter of the results dictionary. Defaults to False.
-
-`upload_checkpoints (bool)`: If True, model checkpoints are uploaded as artifacts. Defaults to False.
-
-### Example
-
-```python
-from ray import tune, train
-from ray.air.integrations.wandb import WandbLoggerCallback
-
-
-def train_fc(config):
- for i in range(10):
- train.report({"mean_accuracy": (i + config["alpha"]) / 10})
-
-
-tuner = tune.Tuner(
- train_fc,
- param_space={
- "alpha": tune.grid_search([0.1, 0.2, 0.3]),
- "beta": tune.uniform(0.5, 1.0),
- },
- run_config=train.RunConfig(
- callbacks=[
- WandbLoggerCallback(
- project="", api_key="", log_config=True
- )
- ]
- ),
-)
-
-results = tuner.fit()
-```
-
-## setup_wandb
-
-```python
-from ray.air.integrations.wandb import setup_wandb
-```
-
-This utility function helps initialize Wandb for use with Ray Tune. For basic usage, call `setup_wandb()` in your training function:
-
-```python
-from ray.air.integrations.wandb import setup_wandb
-
-
-def train_fn(config):
- # Initialize wandb
- wandb = setup_wandb(config)
- run = wandb.init(
- project=config["wandb"]["project"],
- api_key_file=config["wandb"]["api_key_file"],
- )
-
- for i in range(10):
- loss = config["a"] + config["b"]
- run.log({"loss": loss})
- tune.report(loss=loss)
- run.finish()
-
-
-tuner = tune.Tuner(
- train_fn,
- param_space={
- # define search space here
- "a": tune.choice([1, 2, 3]),
- "b": tune.choice([4, 5, 6]),
- # wandb configuration
- "wandb": {"project": "Optimization_Project", "api_key_file": "/path/to/file"},
- },
-)
-results = tuner.fit()
-```
-
-## Example Code
-
-We've created a few examples for you to see how the integration works:
-
-* [Colab](https://wandb.me/raytune-colab): A simple demo to try the integration.
-* [Dashboard](https://wandb.ai/anmolmann/ray_tune): View dashboard generated from the example.
\ No newline at end of file
diff --git a/content/en/guides/integrations/sagemaker.md b/content/en/guides/integrations/sagemaker.md
deleted file mode 100644
index e0143cf06f..0000000000
--- a/content/en/guides/integrations/sagemaker.md
+++ /dev/null
@@ -1,43 +0,0 @@
----
-description: How to integrate W&B with Amazon SageMaker.
-menu:
- default:
- identifier: sagemaker
- parent: integrations
-title: SageMaker
-weight: 370
----
-
-
-W&B integrates with [Amazon SageMaker](https://aws.amazon.com/sagemaker/), automatically reading hyperparameters, grouping distributed runs, and resuming runs from checkpoints.
-
-## Authentication
-
-W&B looks for a file named `secrets.env` relative to the training script and loads them into the environment when `wandb.init()` is called. You can generate a `secrets.env` file by calling `wandb.sagemaker_auth(path="source_dir")` in the script you use to launch your experiments. Be sure to add this file to your `.gitignore`!
-
-## Existing estimators
-
-If you're using one of SageMakers preconfigured estimators you need to add a `requirements.txt` to your source directory that includes wandb
-
-```text
-wandb
-```
-
-If you're using an estimator that's running Python 2, you'll need to install `psutil` directly from this [wheel](https://pythonwheels.com) before installing wandb:
-
-```text
-https://wheels.galaxyproject.org/packages/psutil-5.4.8-cp27-cp27mu-manylinux1_x86_64.whl
-wandb
-```
-
-Review a complete example on [GitHub](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cifar10-sagemaker), and read more on our [blog](https://wandb.ai/site/articles/running-sweeps-with-sagemaker).
-
-You can also read the [Deploy Sentiment Analyzer Using SageMaker and W&B tutorial](https://wandb.ai/authors/sagemaker/reports/Deploy-Sentiment-Analyzer-Using-SageMaker-and-W-B--VmlldzoxODA1ODE) on deploying a sentiment analyzer using SageMaker and W&B.
-
-{{% alert color="secondary" %}}
-The W&B sweep agent behaves as expected in a SageMaker job only if your SageMaker integration is turned off. Turn off the SageMaker integration by modifying your invocation of `wandb.init`:
-
-```python
-wandb.init(..., settings=wandb.Settings(sagemaker_disable=True))
-```
-{{% /alert %}}
diff --git a/content/en/guides/integrations/scikit.md b/content/en/guides/integrations/scikit.md
deleted file mode 100644
index 4d99722921..0000000000
--- a/content/en/guides/integrations/scikit.md
+++ /dev/null
@@ -1,329 +0,0 @@
----
-menu:
- default:
- identifier: scikit
- parent: integrations
-title: Scikit-Learn
-weight: 380
----
-You can use wandb to visualize and compare your scikit-learn models' performance with just a few lines of code. [Try an example →](https://wandb.me/scikit-colab)
-
-## Get started
-
-### Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-### Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-### Log metrics
-
-```python
-import wandb
-
-wandb.init(project="visualize-sklearn") as run:
-
- y_pred = clf.predict(X_test)
- accuracy = sklearn.metrics.accuracy_score(y_true, y_pred)
-
- # If logging metrics over time, then use run.log
- run.log({"accuracy": accuracy})
-
- # OR to log a final metric at the end of training you can also use run.summary
- run.summary["accuracy"] = accuracy
-```
-
-### Make plots
-
-#### Step 1: Import wandb and initialize a new run
-
-```python
-import wandb
-
-run = wandb.init(project="visualize-sklearn")
-```
-
-#### Step 2: Visualize plots
-
-#### Individual plots
-
-After training a model and making predictions you can then generate plots in wandb to analyze your predictions. See the **Supported Plots** section below for a full list of supported charts.
-
-```python
-# Visualize single plot
-wandb.sklearn.plot_confusion_matrix(y_true, y_pred, labels)
-```
-
-#### All plots
-
-W&B has functions such as `plot_classifier` that will plot several relevant plots:
-
-```python
-# Visualize all classifier plots
-wandb.sklearn.plot_classifier(
- clf,
- X_train,
- X_test,
- y_train,
- y_test,
- y_pred,
- y_probas,
- labels,
- model_name="SVC",
- feature_names=None,
-)
-
-# All regression plots
-wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name="Ridge")
-
-# All clustering plots
-wandb.sklearn.plot_clusterer(
- kmeans, X_train, cluster_labels, labels=None, model_name="KMeans"
-)
-
-run.finish()
-```
-
-#### Existing Matplotlib plots
-
-Plots created on Matplotlib can also be logged on W&B Dashboard. To do that, it is first required to install `plotly`.
-
-```bash
-pip install plotly
-```
-
-Finally, the plots can be logged on W&B's dashboard as follows:
-
-```python
-import matplotlib.pyplot as plt
-import wandb
-
-with wandb.init(project="visualize-sklearn") as run:
-
- # do all the plt.plot(), plt.scatter(), etc. here.
- # ...
-
- # instead of doing plt.show() do:
- run.log({"plot": plt})
-```
-
-## Supported plots
-
-### Learning curve
-
-{{< img src="/images/integrations/scikit_learning_curve.png" alt="Scikit-learn learning curve" >}}
-
-Trains model on datasets of varying lengths and generates a plot of cross validated scores vs dataset size, for both training and test sets.
-
-`wandb.sklearn.plot_learning_curve(model, X, y)`
-
-* model (clf or reg): Takes in a fitted regressor or classifier.
-* X (arr): Dataset features.
-* y (arr): Dataset labels.
-
-### ROC
-
-{{< img src="/images/integrations/scikit_roc.png" alt="Scikit-learn ROC curve" >}}
-
-ROC curves plot true positive rate (y-axis) vs false positive rate (x-axis). The ideal score is a TPR = 1 and FPR = 0, which is the point on the top left. Typically we calculate the area under the ROC curve (AUC-ROC), and the greater the AUC-ROC the better.
-
-`wandb.sklearn.plot_roc(y_true, y_probas, labels)`
-
-* y_true (arr): Test set labels.
-* y_probas (arr): Test set predicted probabilities.
-* labels (list): Named labels for target variable (y).
-
-### Class proportions
-
-{{< img src="/images/integrations/scikic_class_props.png" alt="Scikit-learn classification properties" >}}
-
-Plots the distribution of target classes in training and test sets. Useful for detecting imbalanced classes and ensuring that one class doesn't have a disproportionate influence on the model.
-
-`wandb.sklearn.plot_class_proportions(y_train, y_test, ['dog', 'cat', 'owl'])`
-
-* y_train (arr): Training set labels.
-* y_test (arr): Test set labels.
-* labels (list): Named labels for target variable (y).
-
-### Precision recall curve
-
-{{< img src="/images/integrations/scikit_precision_recall.png" alt="Scikit-learn precision-recall curve" >}}
-
-Computes the tradeoff between precision and recall for different thresholds. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
-
-High scores for both show that the classifier is returning accurate results (high precision), as well as returning a majority of all positive results (high recall). PR curve is useful when the classes are very imbalanced.
-
-`wandb.sklearn.plot_precision_recall(y_true, y_probas, labels)`
-
-* y_true (arr): Test set labels.
-* y_probas (arr): Test set predicted probabilities.
-* labels (list): Named labels for target variable (y).
-
-### Feature importances
-
-{{< img src="/images/integrations/scikit_feature_importances.png" alt="Scikit-learn feature importance chart" >}}
-
-Evaluates and plots the importance of each feature for the classification task. Only works with classifiers that have a `feature_importances_` attribute, like trees.
-
-`wandb.sklearn.plot_feature_importances(model, ['width', 'height, 'length'])`
-
-* model (clf): Takes in a fitted classifier.
-* feature_names (list): Names for features. Makes plots easier to read by replacing feature indexes with corresponding names.
-
-### Calibration curve
-
-{{< img src="/images/integrations/scikit_calibration_curve.png" alt="Scikit-learn calibration curve" >}}
-
-Plots how well calibrated the predicted probabilities of a classifier are and how to calibrate an uncalibrated classifier. Compares estimated predicted probabilities by a baseline logistic regression model, the model passed as an argument, and by both its isotonic calibration and sigmoid calibrations.
-
-The closer the calibration curves are to a diagonal the better. A transposed sigmoid like curve represents an overfitted classifier, while a sigmoid like curve represents an underfitted classifier. By training isotonic and sigmoid calibrations of the model and comparing their curves we can figure out whether the model is over or underfitting and if so which calibration (sigmoid or isotonic) might help fix this.
-
-For more details, check out [sklearn's docs](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html).
-
-`wandb.sklearn.plot_calibration_curve(clf, X, y, 'RandomForestClassifier')`
-
-* model (clf): Takes in a fitted classifier.
-* X (arr): Training set features.
-* y (arr): Training set labels.
-* model_name (str): Model name. Defaults to 'Classifier'
-
-### Confusion matrix
-
-{{< img src="/images/integrations/scikit_confusion_matrix.png" alt="Scikit-learn confusion matrix" >}}
-
-Computes the confusion matrix to evaluate the accuracy of a classification. It's useful for assessing the quality of model predictions and finding patterns in the predictions the model gets wrong. The diagonal represents the predictions the model got right, such as where the actual label is equal to the predicted label.
-
-`wandb.sklearn.plot_confusion_matrix(y_true, y_pred, labels)`
-
-* y_true (arr): Test set labels.
-* y_pred (arr): Test set predicted labels.
-* labels (list): Named labels for target variable (y).
-
-### Summary metrics
-
-{{< img src="/images/integrations/scikit_summary_metrics.png" alt="Scikit-learn summary metrics" >}}
-
-- Calculates summary metrics for classification, such as `mse`, `mae`, and `r2` score.
-- Calculates summary metrics for regression, such as `f1`, accuracy, precision, and recall.
-
-`wandb.sklearn.plot_summary_metrics(model, X_train, y_train, X_test, y_test)`
-
-* model (clf or reg): Takes in a fitted regressor or classifier.
-* X (arr): Training set features.
-* y (arr): Training set labels.
- * X_test (arr): Test set features.
-* y_test (arr): Test set labels.
-
-### Elbow plot
-
-{{< img src="/images/integrations/scikit_elbow_plot.png" alt="Scikit-learn elbow plot" >}}
-
-Measures and plots the percentage of variance explained as a function of the number of clusters, along with training times. Useful in picking the optimal number of clusters.
-
-`wandb.sklearn.plot_elbow_curve(model, X_train)`
-
-* model (clusterer): Takes in a fitted clusterer.
-* X (arr): Training set features.
-
-### Silhouette plot
-
-{{< img src="/images/integrations/scikit_silhouette_plot.png" alt="Scikit-learn silhouette plot" >}}
-
-Measures & plots how close each point in one cluster is to points in the neighboring clusters. The thickness of the clusters corresponds to the cluster size. The vertical line represents the average silhouette score of all the points.
-
-Silhouette coefficients near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters and negative values indicate that those samples might have been assigned to the wrong cluster.
-
-In general we want all silhouette cluster scores to be above average (past the red line) and as close to 1 as possible. We also prefer cluster sizes that reflect the underlying patterns in the data.
-
-`wandb.sklearn.plot_silhouette(model, X_train, ['spam', 'not spam'])`
-
-* model (clusterer): Takes in a fitted clusterer.
-* X (arr): Training set features.
- * cluster_labels (list): Names for cluster labels. Makes plots easier to read by replacing cluster indexes with corresponding names.
-
-### Outlier candidates plot
-
-{{< img src="/images/integrations/scikit_outlier_plot.png" alt="Scikit-learn outlier plot" >}}
-
-Measures a datapoint's influence on regression model via cook's distance. Instances with heavily skewed influences could potentially be outliers. Useful for outlier detection.
-
-`wandb.sklearn.plot_outlier_candidates(model, X, y)`
-
-* model (regressor): Takes in a fitted classifier.
-* X (arr): Training set features.
-* y (arr): Training set labels.
-
-### Residuals plot
-
-{{< img src="/images/integrations/scikit_residuals_plot.png" alt="Scikit-learn residuals plot" >}}
-
-Measures and plots the predicted target values (y-axis) vs the difference between actual and predicted target values (x-axis), as well as the distribution of the residual error.
-
-Generally, the residuals of a well-fit model should be randomly distributed because good models will account for most phenomena in a data set, except for random error.
-
-`wandb.sklearn.plot_residuals(model, X, y)`
-
-* model (regressor): Takes in a fitted classifier.
-* X (arr): Training set features.
-* y (arr): Training set labels.
-
- If you have any questions, we'd love to answer them in our [slack community](https://wandb.me/slack).
-
-## Example
-
-* [Run in colab](https://wandb.me/scikit-colab): A simple notebook to get you started.
\ No newline at end of file
diff --git a/content/en/guides/integrations/simpletransformers.md b/content/en/guides/integrations/simpletransformers.md
deleted file mode 100644
index af67056f6f..0000000000
--- a/content/en/guides/integrations/simpletransformers.md
+++ /dev/null
@@ -1,128 +0,0 @@
----
-description: How to integrate W&B with the Transformers library by Hugging Face.
-menu:
- default:
- identifier: simpletransformers
- parent: integrations
-title: Simple Transformers
-weight: 390
----
-
-This library is based on the Transformers library by Hugging Face. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize a model, train the model, and evaluate a model. It supports Sequence Classification, Token Classification \(NER\),Question Answering,Language Model Fine-Tuning, Language Model Training, Language Generation, T5 Model, Seq2Seq Tasks , Multi-Modal Classification and Conversational AI.
-
-To use W&B for visualizing model training. To use this, set a project name for W&B in the `wandb_project` attribute of the `args` dictionary. This logs all hyperparameter values, training losses, and evaluation metrics to the given project.
-
-```python
-model = ClassificationModel('roberta', 'roberta-base', args={'wandb_project': 'project-name'})
-```
-
-Any additional arguments that go into `wandb.init` can be passed as `wandb_kwargs`.
-
-## Structure
-
-The library is designed to have a separate class for every NLP task. The classes that provide similar functionality are grouped together.
-
-* `simpletransformers.classification` - Includes all Classification models.
- * `ClassificationModel`
- * `MultiLabelClassificationModel`
-* `simpletransformers.ner` - Includes all Named Entity Recognition models.
- * `NERModel`
-* `simpletransformers.question_answering` - Includes all Question Answering models.
- * `QuestionAnsweringModel`
-
-Here are some minimal examples
-
-## MultiLabel Classification
-
-```text
- model = MultiLabelClassificationModel("distilbert","distilbert-base-uncased",num_labels=6,
- args={"reprocess_input_data": True, "overwrite_output_dir": True, "num_train_epochs":epochs,'learning_rate':learning_rate,
- 'wandb_project': "simpletransformers"},
- )
- # Train the model
- model.train_model(train_df)
-
- # Evaluate the model
- result, model_outputs, wrong_predictions = model.eval_model(eval_df)
-```
-
-## Question Answering
-
-```text
- train_args = {
- 'learning_rate': wandb.config.learning_rate,
- 'num_train_epochs': 2,
- 'max_seq_length': 128,
- 'doc_stride': 64,
- 'overwrite_output_dir': True,
- 'reprocess_input_data': False,
- 'train_batch_size': 2,
- 'fp16': False,
- 'wandb_project': "simpletransformers"
-}
-
-model = QuestionAnsweringModel('distilbert', 'distilbert-base-cased', args=train_args)
-model.train_model(train_data)
-```
-
-
-SimpleTransformers provides classes as well as training scripts for all common natural language tasks. Here is the complete list of global arguments that are supported by the library, with their default arguments.
-
-```text
-global_args = {
- "adam_epsilon": 1e-8,
- "best_model_dir": "outputs/best_model",
- "cache_dir": "cache_dir/",
- "config": {},
- "do_lower_case": False,
- "early_stopping_consider_epochs": False,
- "early_stopping_delta": 0,
- "early_stopping_metric": "eval_loss",
- "early_stopping_metric_minimize": True,
- "early_stopping_patience": 3,
- "encoding": None,
- "eval_batch_size": 8,
- "evaluate_during_training": False,
- "evaluate_during_training_silent": True,
- "evaluate_during_training_steps": 2000,
- "evaluate_during_training_verbose": False,
- "fp16": True,
- "fp16_opt_level": "O1",
- "gradient_accumulation_steps": 1,
- "learning_rate": 4e-5,
- "local_rank": -1,
- "logging_steps": 50,
- "manual_seed": None,
- "max_grad_norm": 1.0,
- "max_seq_length": 128,
- "multiprocessing_chunksize": 500,
- "n_gpu": 1,
- "no_cache": False,
- "no_save": False,
- "num_train_epochs": 1,
- "output_dir": "outputs/",
- "overwrite_output_dir": False,
- "process_count": cpu_count() - 2 if cpu_count() > 2 else 1,
- "reprocess_input_data": True,
- "save_best_model": True,
- "save_eval_checkpoints": True,
- "save_model_every_epoch": True,
- "save_steps": 2000,
- "save_optimizer_and_scheduler": True,
- "silent": False,
- "tensorboard_dir": None,
- "train_batch_size": 8,
- "use_cached_eval_features": False,
- "use_early_stopping": False,
- "use_multiprocessing": True,
- "wandb_kwargs": {},
- "wandb_project": None,
- "warmup_ratio": 0.06,
- "warmup_steps": 0,
- "weight_decay": 0,
-}
-```
-
-Refer to [simpletransformers on github](https://github.com/ThilinaRajapakse/simpletransformers) for more detailed documentation.
-
-Checkout [this W&B report](https://app.wandb.ai/cayush/simpletransformers/reports/Using-simpleTransformer-on-common-NLP-applications---Vmlldzo4Njk2NA) that covers training transformers on some the most popular GLUE benchmark datasets. [Try it out yourself on colab](https://colab.research.google.com/drive/1oXROllqMqVvBFcPgTKJRboTq96uWuqSz?usp=sharing).
\ No newline at end of file
diff --git a/content/en/guides/integrations/skorch.md b/content/en/guides/integrations/skorch.md
deleted file mode 100644
index fc6eac6c28..0000000000
--- a/content/en/guides/integrations/skorch.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-description: How to integrate W&B with Skorch.
-menu:
- default:
- identifier: skorch
- parent: integrations
-title: Skorch
-weight: 400
----
-
-You can use W&B with Skorch to automatically log the model with the best performance, along with all model performance metrics, the model topology and compute resources after each epoch. Every file saved in `wandb_run.dir` is automatically logged to W&B.
-
-See [example run](https://app.wandb.ai/borisd13/skorch/runs/s20or4ct?workspace=user-borisd13).
-
-## Parameters
-
-| Parameter | Type | Description |
-| :--- | :--- | :--- |
-| `wandb_run` | `wandb.wandb_run`. Run | wandb run used to log data. |
-|`save_model` | bool (default=True)| Whether to save a checkpoint of the best model and upload it to your Run on W&B.|
-|`keys_ignored`| str or list of str (default=None) | Key or list of keys that should not be logged to tensorboard. Note that in addition to the keys provided by the user, keys such as those starting with `event_` or ending on `_best` are ignored by default.|
-
-## Example Code
-
-We've created a few examples for you to see how the integration works:
-
-* [Colab](https://colab.research.google.com/drive/1Bo8SqN1wNPMKv5Bn9NjwGecBxzFlaNZn?usp=sharing): A simple demo to try the integration
-* [A step by step guide](https://app.wandb.ai/cayush/uncategorized/reports/Automate-Kaggle-model-training-with-Skorch-and-W%26B--Vmlldzo4NTQ1NQ): to tracking your Skorch model performance
-
-```python
-# Install wandb
-... pip install wandb
-
-import wandb
-from skorch.callbacks import WandbLogger
-
-# Create a wandb Run
-wandb_run = wandb.init()
-# Alternative: Create a wandb Run without a W&B account
-wandb_run = wandb.init(anonymous="allow")
-
-# Log hyper-parameters (optional)
-wandb_run.config.update({"learning rate": 1e-3, "batch size": 32})
-
-net = NeuralNet(..., callbacks=[WandbLogger(wandb_run)])
-net.fit(X, y)
-```
-
-## Method reference
-
-| Method | Description |
-| :--- | :--- |
-| `initialize`\(\) | \(Re-\)Set the initial state of the callback. |
-| `on_batch_begin`\(net\[, X, y, training\]\) | Called at the beginning of each batch. |
-| `on_batch_end`\(net\[, X, y, training\]\) | Called at the end of each batch. |
-| `on_epoch_begin`\(net\[, dataset_train, …\]\) | Called at the beginning of each epoch. |
-| `on_epoch_end`\(net, \*\*kwargs\) | Log values from the last history step and save best model |
-| `on_grad_computed`\(net, named_parameters\[, X, …\]\) | Called once per batch after gradients have been computed but before an update step was performed. |
-| `on_train_begin`\(net, \*\*kwargs\) | Log model topology and add a hook for gradients |
-| `on_train_end`\(net\[, X, y\]\) | Called at the end of training. |
\ No newline at end of file
diff --git a/content/en/guides/integrations/spacy.md b/content/en/guides/integrations/spacy.md
deleted file mode 100644
index 066a608d36..0000000000
--- a/content/en/guides/integrations/spacy.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-menu:
- default:
- identifier: spacy
- parent: integrations
-title: spaCy
-weight: 410
----
-[spaCy](https://spacy.io) is a popular "industrial-strength" NLP library: fast, accurate models with a minimum of fuss. As of spaCy v3, W&B can now be used with [`spacy train`](https://spacy.io/api/cli#train) to track your spaCy model's training metrics as well as to save and version your models and datasets. And all it takes is a few added lines in your configuration.
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Add the `WandbLogger` to your spaCy config file
-
-spaCy config files are used to specify all aspects of training, not just logging -- GPU allocation, optimizer choice, dataset paths, and more. Minimally, under `[training.logger]` you need to provide the key `@loggers` with the value `"spacy.WandbLogger.v3"`, plus a `project_name`.
-
-{{% alert %}}
-For more on how spaCy training config files work and on other options you can pass in to customize training, check out [spaCy's documentation](https://spacy.io/usage/training).
-{{% /alert %}}
-
-```python
-[training.logger]
-@loggers = "spacy.WandbLogger.v3"
-project_name = "my_spacy_project"
-remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
-log_dataset_dir = "./corpus"
-model_log_interval = 1000
-```
-
-| Name | Description |
-| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `project_name` | `str`. The name of the W&B Project. The project will be created automatically if it doesn’t exist yet. |
-| `remove_config_values` | `List[str]` . A list of values to exclude from the config before it is uploaded to W&B. `[]` by default. |
-| `model_log_interval` | `Optional int`. `None` by default. If set, enables [model versioning]({{< relref "/guides/core/registry/" >}}) with [Artifacts]({{< relref "/guides/core/artifacts/" >}}). Pass in the number of steps to wait between logging model checkpoints. `None` by default. |
-| `log_dataset_dir` | `Optional str`. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. `None` by default. |
-| `entity` | `Optional str` . If passed, the run will be created in the specified entity |
-| `run_name` | `Optional str` . If specified, the run will be created with the specified name. |
-
-## Start training
-
-Once you have added the `WandbLogger` to your spaCy training config you can run `spacy train` as usual.
-
-{{< tabpane text=true >}}
-
-{{% tab header="Command Line" value="cli" %}}
-
-```python
-python -m spacy train \
- config.cfg \
- --output ./output \
- --paths.train ./train \
- --paths.dev ./dev
-```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```python
-python -m spacy train \
- config.cfg \
- --output ./output \
- --paths.train ./train \
- --paths.dev ./dev
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="notebook" %}}
-
-```notebook
-!python -m spacy train \
- config.cfg \
- --output ./output \
- --paths.train ./train \
- --paths.dev ./dev
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-When training begins, a link to your training run's [W&B page]({{< relref "/guides/models/track/runs/" >}}) will be output which will take you to this run's experiment tracking [dashboard]({{< relref "/guides/models/track/workspaces.md" >}}) in the W&B web UI.
\ No newline at end of file
diff --git a/content/en/guides/integrations/stable-baselines-3.md b/content/en/guides/integrations/stable-baselines-3.md
deleted file mode 100644
index 4ed99a0841..0000000000
--- a/content/en/guides/integrations/stable-baselines-3.md
+++ /dev/null
@@ -1,90 +0,0 @@
----
-description: How to integrate W&B with Stable Baseline 3.
-menu:
- default:
- identifier: stable-baselines-3
- parent: integrations
-title: Stable Baselines 3
-weight: 420
----
-
-[Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) \(SB3\) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. W&B's SB3 integration:
-
-* Records metrics such as losses and episodic returns.
-* Uploads videos of agents playing the games.
-* Saves the trained model.
-* Logs the model's hyperparameters.
-* Logs the model gradient histograms.
-
-Review an [example SB3 training run](https://wandb.ai/wandb/sb3/runs/1jyr6z10).
-
-## Log your SB3 experiments
-
-```python
-from wandb.integration.sb3 import WandbCallback
-
-model.learn(..., callback=WandbCallback())
-```
-
-{{< img src="/images/integrations/stable_baselines_demo.gif" alt="Stable Baselines 3 training with W&B" >}}
-
-## WandbCallback Arguments
-
-| Argument | Usage |
-| :--- | :--- |
-| `verbose` | The verbosity of sb3 output |
-| `model_save_path` | Path to the folder where the model will be saved, The default value is \`None\` so the model is not logged |
-| `model_save_freq` | Frequency to save the model |
-| `gradient_save_freq` | Frequency to log gradient. The default value is 0 so the gradients are not logged |
-
-## Basic Example
-
-The W&B SB3 integration uses the logs output from TensorBoard to log your metrics
-
-```python
-import gym
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import DummyVecEnv, VecVideoRecorder
-import wandb
-from wandb.integration.sb3 import WandbCallback
-
-
-config = {
- "policy_type": "MlpPolicy",
- "total_timesteps": 25000,
- "env_name": "CartPole-v1",
-}
-run = wandb.init(
- project="sb3",
- config=config,
- sync_tensorboard=True, # auto-upload sb3's tensorboard metrics
- monitor_gym=True, # auto-upload the videos of agents playing the game
- save_code=True, # optional
-)
-
-
-def make_env():
- env = gym.make(config["env_name"])
- env = Monitor(env) # record stats such as returns
- return env
-
-
-env = DummyVecEnv([make_env])
-env = VecVideoRecorder(
- env,
- f"videos/{run.id}",
- record_video_trigger=lambda x: x % 2000 == 0,
- video_length=200,
-)
-model = PPO(config["policy_type"], env, verbose=1, tensorboard_log=f"runs/{run.id}")
-model.learn(
- total_timesteps=config["total_timesteps"],
- callback=WandbCallback(
- gradient_save_freq=100,
- model_save_path=f"models/{run.id}",
- verbose=2,
- ),
-)
-run.finish()
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/tensorboard.md b/content/en/guides/integrations/tensorboard.md
deleted file mode 100644
index 263622154f..0000000000
--- a/content/en/guides/integrations/tensorboard.md
+++ /dev/null
@@ -1,110 +0,0 @@
----
-menu:
- default:
- identifier: tensorboard
- parent: integrations
-title: TensorBoard
-weight: 430
----
-{{< cta-button colabLink="https://github.com/wandb/examples/blob/master/colabs/tensorboard/TensorBoard_and_Weights_and_Biases.ipynb" >}}
-
-{{% alert %}}
-W&B support embedded TensorBoard for W&B Multi-tenant SaaS.
-{{% /alert %}}
-
-Upload your TensorBoard logs to the cloud, quickly share your results among colleagues and classmates and keep your analysis in one centralized location.
-
-{{< img src="/images/integrations/tensorboard_oneline_code.webp" alt="TensorBoard integration code" >}}
-
-## Get started
-
-```python
-import wandb
-
-# Start a wandb run with `sync_tensorboard=True`
-wandb.init(project="my-project", sync_tensorboard=True) as run:
- # Your training code using TensorBoard
- ...
-
-```
-
-Review an [example TensorBoard integration run](https://wandb.ai/rymc/simple-tensorboard-example/runs/oab614zf/tensorboard).
-
-Once your run finishes, you can access your TensorBoard event files in W&B and you can visualize your metrics in native W&B charts, together with additional useful information like the system's CPU or GPU utilization, the `git` state, the terminal command the run used, and more.
-
-{{% alert %}}
-W&B supports TensorBoard with all versions of TensorFlow. W&B also supports TensorBoard 1.14 and higher with PyTorch as well as TensorBoardX.
-{{% /alert %}}
-
-## Frequently asked questions
-
-### How can I log metrics to W&B that aren't logged to TensorBoard?
-
-If you need to log additional custom metrics that aren't being logged to TensorBoard, you can call `wandb.Run.log()` in your code `run.log({"custom": 0.8})`
-
-Setting the step argument in `run.log()` is turned off when syncing Tensorboard. If you'd like to set a different step count, you can log the metrics with a step metric as:
-
-`run.log({"custom": 0.8, "global_step": global_step})`
-
-### How do I configure Tensorboard when I'm using it with `wandb`?
-
-If you want more control over how TensorBoard is patched you can call `wandb.tensorboard.patch` instead of passing `sync_tensorboard=True` to `wandb.init`.
-
-```python
-import wandb
-
-wandb.tensorboard.patch(root_logdir="")
-run = wandb.init()
-
-# Finish the wandb run to upload the tensorboard logs to W&B (if running in Notebook)
-run.finish()
-```
-
-You can pass `tensorboard_x=False` to this method to ensure vanilla TensorBoard is patched, if you're using TensorBoard > 1.14 with PyTorch you can pass `pytorch=True` to ensure it's patched. Both of these options have smart defaults depending on what versions of these libraries have been imported.
-
-By default, we also sync the `tfevents` files and any `.pbtxt` files. This enables us to launch a TensorBoard instance on your behalf. You will see a [TensorBoard tab](https://www.wandb.com/articles/hosted-tensorboard) on the run page. This behavior can be turned off by passing `save=False` to `wandb.tensorboard.patch`
-
-```python
-import wandb
-
-run = wandb.init()
-wandb.tensorboard.patch(save=False, tensorboard_x=True)
-
-# If running in a notebook, finish the wandb run to upload the tensorboard logs to W&B
-run.finish()
-```
-
-{{% alert color="secondary" %}}
-You must call either `wandb.init()` or `wandb.tensorboard.patch` **before** calling `tf.summary.create_file_writer` or constructing a `SummaryWriter` via `torch.utils.tensorboard`.
-{{% /alert %}}
-
-### How do I sync historical TensorBoard runs?
-
-If you have existing `tfevents` files stored locally and you would like to import them into W&B, you can run `wandb sync log_dir`, where `log_dir` is a local directory containing the `tfevents` files.
-
-### How do I use Google Colab or Jupyter with TensorBoard?
-
-If running your code in a Jupyter or Colab notebook, make sure to call `wandb.Run.finish()` and the end of your training. This will finish the wandb run and upload the tensorboard logs to W&B so they can be visualized. This is not necessary when running a `.py` script as wandb finishes automatically when a script finishes.
-
-To run shell commands in a notebook environment, you must prepend a `!`, as in `!wandb sync directoryname`.
-
-### How do I use PyTorch with TensorBoard?
-
-If you use PyTorch's TensorBoard integration, you may need to manually upload the PyTorch Profiler JSON file.
-
-```python
-with wandb.init(project="my-project", sync_tensorboard=True) as run:
- run.save(glob.glob(f"runs/*.pt.trace.json")[0], base_path=f"runs")
-```
-
-### Can I sync tfevents files stored in the cloud?
-
-`wandb` 0.20.0 and above supports syncing `tfevents` files stored in S3, GCS or Azure. `wandb` uses the default credentials for each cloud provider, corresponding to the commands in the following table:
-
-| Cloud provider | Credentials | Logging directory format |
-| -------------- | --------------------------------------- | ------------------------------------- |
-| S3 | `aws configure` | `s3://bucket/path/to/logs` |
-| GCS | `gcloud auth application-default login` | `gs://bucket/path/to/logs` |
-| Azure | `az login`[^1] | `az://account/container/path/to/logs` |
-
-[^1]: You must also set the `AZURE_STORAGE_ACCOUNT` and `AZURE_STORAGE_KEY` environment variables.
diff --git a/content/en/guides/integrations/tensorflow.md b/content/en/guides/integrations/tensorflow.md
deleted file mode 100644
index 18cbf03d50..0000000000
--- a/content/en/guides/integrations/tensorflow.md
+++ /dev/null
@@ -1,97 +0,0 @@
----
-menu:
- default:
- identifier: tensorflow
- parent: integrations
-title: TensorFlow
-weight: 440
----
-
-{{< cta-button colabLink="https://colab.research.google.com/drive/1JCpAbjkCFhYMT7LCQ399y35TS3jlMpvM" >}}
-
-## Get started
-
-If you're already using TensorBoard, it's easy to integrate with wandb.
-
-```python
-import tensorflow as tf
-import wandb
-```
-
-## Log custom metrics
-
-If you need to log additional custom metrics that aren't being logged to TensorBoard, you can call `run.log()` in your code `run.log({"custom": 0.8}) `
-
-Setting the step argument in `run.log()` is turned off when syncing Tensorboard. If you'd like to set a different step count, you can log the metrics with a step metric as:
-
-``` python
-with wandb.init(config=tf.flags.FLAGS, sync_tensorboard=True) as run:
- run.log({"custom": 0.8, "global_step":global_step}, step=global_step)
-```
-
-## TensorFlow estimators hook
-
-If you want more control over what gets logged, wandb also provides a hook for TensorFlow estimators. It will log all `tf.summary` values in the graph.
-
-```python
-import tensorflow as tf
-import wandb
-
-run = wandb.init(config=tf.FLAGS)
-
-estimator.train(hooks=[wandb.tensorflow.WandbHook(steps_per_log=1000)])
-run.finish()
-```
-
-## Log manually
-
-The simplest way to log metrics in TensorFlow is by logging `tf.summary` with the TensorFlow logger:
-
-```python
-import wandb
-run = wandb.init(config=tf.flags.FLAGS, sync_tensorboard=True)
-with tf.Session() as sess:
- # ...
- wandb.tensorflow.log(tf.summary.merge_all())
-```
-
-With TensorFlow 2, the recommended way of training a model with a custom loop is via using `tf.GradientTape`. You can read more in the [TensorFlow custom training walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough). If you want to incorporate `wandb` to log metrics in your custom TensorFlow training loops you can follow this snippet:
-
-```python
- with tf.GradientTape() as tape:
- # Get the probabilities
- predictions = model(features)
- # Calculate the loss
- loss = loss_func(labels, predictions)
-
- # Log your metrics
- run.log("loss": loss.numpy())
- # Get the gradients
- gradients = tape.gradient(loss, model.trainable_variables)
- # Update the weights
- optimizer.apply_gradients(zip(gradients, model.trainable_variables))
-```
-
-A [full example of customizing training loops in TensorFlow 2](https://www.wandb.com/articles/wandb-customizing-training-loops-in-tensorflow-2) is available.
-
-## How is W&B different from TensorBoard?
-
-When the cofounders started working on W&B, they were inspired to build a tool for the frustrated TensorBoard users at OpenAI. Here are a few things we've focused on improving:
-
-1. **Reproduce models**: W&B is good for experimentation, exploration, and reproducing models later. We capture not just the metrics, but also the hyperparameters and version of the code, and we can save your version-control status and model checkpoints for you so your project is reproducible.
-2. **Automatic organization**: Whether you're picking up a project from a collaborator, coming back from a vacation, or dusting off an old project, W&B makes it easy to see all the models that have been tried so no one wastes hours, GPU cycles, or carbon re-running experiments.
-3. **Fast, flexible integration**: Add W&B to your project in 5 minutes. Install our free open-source Python package and add a couple of lines to your code, and every time you run your model you'll have nice logged metrics and records.
-4. **Persistent, centralized dashboard**: No matter where you train your models, whether on your local machine, in a shared lab cluster, or on spot instances in the cloud, your results are shared to the same centralized dashboard. You don't need to spend your time copying and organizing TensorBoard files from different machines.
-5. **Powerful tables**: Search, filter, sort, and group results from different models. It's easy to look over thousands of model versions and find the best performing models for different tasks. TensorBoard isn't built to work well on large projects.
-6. **Tools for collaboration**: Use W&B to organize complex machine learning projects. It's easy to share a link to W&B, and you can use private teams to have everyone sending results to a shared project. We also support collaboration via reports— add interactive visualizations and describe your work in markdown. This is a great way to keep a work log, share findings with your supervisor, or present findings to your lab or team.
-
-Get started with a [free account](https://wandb.ai)
-
-## Examples
-
-We've created a few examples for you to see how the integration works:
-
-* [Example on Github](https://github.com/wandb/examples/blob/master/examples/tensorflow/tf-estimator-mnist/mnist.py): MNIST example Using TensorFlow Estimators
-* [Example on Github](https://github.com/wandb/examples/blob/master/examples/tensorflow/tf-cnn-fashion/train.py): Fashion MNIST example Using Raw TensorFlow
-* [Wandb Dashboard](https://app.wandb.ai/l2k2/examples-tf-estimator-mnist/runs/p0ifowcb): View result on W&B
-* Customizing Training Loops in TensorFlow 2 - [Article](https://www.wandb.com/articles/wandb-customizing-training-loops-in-tensorflow-2) | [Dashboard](https://app.wandb.ai/sayakpaul/custom_training_loops_tf)
\ No newline at end of file
diff --git a/content/en/guides/integrations/torchtune.md b/content/en/guides/integrations/torchtune.md
deleted file mode 100644
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--- a/content/en/guides/integrations/torchtune.md
+++ /dev/null
@@ -1,154 +0,0 @@
----
-menu:
- default:
- identifier: torchtune
- parent: integrations
-title: Pytorch torchtune
-weight: 320
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/torchtune/torchtune_and_wandb.ipynb" >}}
-
-[torchtune](https://pytorch.org/torchtune/stable/index.html) is a PyTorch-based library designed to streamline the authoring, fine-tuning, and experimentation processes for large language models (LLMs). Additionally, torchtune has built-in support for [logging with W&B](https://pytorch.org/torchtune/stable/deep_dives/wandb_logging.html), enhancing tracking and visualization of training processes.
-
-{{< img src="/images/integrations/torchtune_dashboard.png" alt="TorchTune training dashboard" >}}
-
-Check the W&B blog post on [Fine-tuning Mistral 7B using torchtune](https://wandb.ai/capecape/torchtune-mistral/reports/torchtune-The-new-PyTorch-LLM-fine-tuning-library---Vmlldzo3NTUwNjM0).
-
-## W&B logging at your fingertips
-
-{{< tabpane text=true >}}
-{{% tab header="Command line" value="cli" %}}
-
-Override command line arguments at launch:
-
-```bash
-tune run lora_finetune_single_device --config llama3/8B_lora_single_device \
- metric_logger._component_=torchtune.utils.metric_logging.WandBLogger \
- metric_logger.project="llama3_lora" \
- log_every_n_steps=5
-```
-
-{{% /tab %}}
-{{% tab header="Recipe's config" value="config" %}}
-
-Enable W&B logging on the recipe's config:
-
-```yaml
-# inside llama3/8B_lora_single_device.yaml
-metric_logger:
- _component_: torchtune.utils.metric_logging.WandBLogger
- project: llama3_lora
-log_every_n_steps: 5
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Use the W&B metric logger
-
-Enable W&B logging on the recipe's config file by modifying the `metric_logger` section. Change the `_component_` to `torchtune.utils.metric_logging.WandBLogger` class. You can also pass a `project` name and `log_every_n_steps` to customize the logging behavior.
-
-You can also pass any other `kwargs` as you would to the [wandb.init()]({{< relref "/ref/python/functions/init.md" >}}) method. For example, if you are working on a team, you can pass the `entity` argument to the `WandBLogger` class to specify the team name.
-
-{{< tabpane text=true >}}
-{{% tab header="Recipe's Config" value="config" %}}
-
-```yaml
-# inside llama3/8B_lora_single_device.yaml
-metric_logger:
- _component_: torchtune.utils.metric_logging.WandBLogger
- project: llama3_lora
- entity: my_project
- job_type: lora_finetune_single_device
- group: my_awesome_experiments
-log_every_n_steps: 5
-```
-
-{{% /tab %}}
-
-{{% tab header="Command Line" value="cli" %}}
-
-```shell
-tune run lora_finetune_single_device --config llama3/8B_lora_single_device \
- metric_logger._component_=torchtune.utils.metric_logging.WandBLogger \
- metric_logger.project="llama3_lora" \
- metric_logger.entity="my_project" \
- metric_logger.job_type="lora_finetune_single_device" \
- metric_logger.group="my_awesome_experiments" \
- log_every_n_steps=5
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## What is logged?
-
-You can explore the W&B dashboard to see the logged metrics. By default W&B logs all of the hyperparameters from the config file and the launch overrides.
-
-W&B captures the resolved config on the **Overview** tab. W&B also stores the config in YAML format on the [Files tab](https://wandb.ai/capecape/torchtune/runs/joyknwwa/files).
-
-{{< img src="/images/integrations/torchtune_config.png" alt="TorchTune configuration" >}}
-
-### Logged Metrics
-
-Each recipe has its own training loop. Check each individual recipe to see its logged metrics, which include these by default:
-
-| Metric | Description |
-| --- | --- |
-| `loss` | The loss of the model |
-| `lr` | The learning rate |
-| `tokens_per_second` | The tokens per second of the model |
-| `grad_norm` | The gradient norm of the model |
-| `global_step` | Corresponds to the current step in the training loop. Takes into account gradient accumulation, basically every time an optimizer step is taken, the model is updated, the gradients are accumulated and the model is updated once every `gradient_accumulation_steps` |
-
-{{% alert %}}
-`global_step` is not the same as the number of training steps. It corresponds to the current step in the training loop. Takes into account gradient accumulation, basically every time an optimizer step is taken the `global_step` is incremented by 1. For example, if the dataloader has 10 batches, gradient accumulation steps is 2 and run for 3 epochs, the optimizer will step 15 times, in this case `global_step` will range from 1 to 15.
-{{% /alert %}}
-
-The streamlined design of torchtune allows to easily add custom metrics or modify the existing ones. It suffices to modify the corresponding [recipe file](https://github.com/pytorch/torchtune/tree/main/recipes), for example, computing one could log `current_epoch` as a percentage of the total number of epochs as following:
-
-```python
-# inside `train.py` function in the recipe file
-self._metric_logger.log_dict(
- {"current_epoch": self.epochs * self.global_step / self._steps_per_epoch},
- step=self.global_step,
-)
-```
-
-{{% alert %}}
-This is a fast evolving library, the current metrics are subject to change. If you want to add a custom metric, you should modify the recipe and call the corresponding `self._metric_logger.*` function.
-{{% /alert %}}
-
-## Save and load checkpoints
-
-The torchtune library supports various [checkpoint formats](https://pytorch.org/torchtune/stable/deep_dives/checkpointer.html). Depending on the origin of the model you are using, you should switch to the appropriate [checkpointer class](https://pytorch.org/torchtune/stable/deep_dives/checkpointer.html).
-
-If you want to save the model checkpoints to [W&B Artifacts]({{< relref "/guides/core/artifacts/" >}}), the simplest solution is to override the `save_checkpoint` functions inside the corresponding recipe.
-
-Here is an example of how you can override the `save_checkpoint` function to save the model checkpoints to W&B Artifacts.
-
-```python
-def save_checkpoint(self, epoch: int) -> None:
- ...
- ## Let's save the checkpoint to W&B
- ## depending on the Checkpointer Class the file will be named differently
- ## Here is an example for the full_finetune case
- checkpoint_file = Path.joinpath(
- self._checkpointer._output_dir, f"torchtune_model_{epoch}"
- ).with_suffix(".pt")
- wandb_artifact = wandb.Artifact(
- name=f"torchtune_model_{epoch}",
- type="model",
- # description of the model checkpoint
- description="Model checkpoint",
- # you can add whatever metadata you want as a dict
- metadata={
- utils.SEED_KEY: self.seed,
- utils.EPOCHS_KEY: self.epochs_run,
- utils.TOTAL_EPOCHS_KEY: self.total_epochs,
- utils.MAX_STEPS_KEY: self.max_steps_per_epoch,
- },
- )
- wandb_artifact.add_file(checkpoint_file)
- wandb.log_artifact(wandb_artifact)
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/ultralytics.md b/content/en/guides/integrations/ultralytics.md
deleted file mode 100644
index f9c34f8232..0000000000
--- a/content/en/guides/integrations/ultralytics.md
+++ /dev/null
@@ -1,144 +0,0 @@
----
-menu:
- default:
- identifier: ultralytics
- parent: integrations
-title: Ultralytics
-weight: 480
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/ultralytics/01_train_val.ipynb" >}}
-
-[Ultralytics](https://github.com/ultralytics/ultralytics) is the home for cutting-edge, state-of-the-art computer vision models for tasks like image classification, object detection, image segmentation, and pose estimation. Not only it hosts [YOLOv8](https://docs.ultralytics.com/models/yolov8/), the latest iteration in the YOLO series of real-time object detection models, but other powerful computer vision models such as [SAM (Segment Anything Model)](https://docs.ultralytics.com/models/sam/#introduction-to-sam-the-segment-anything-model), [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), etc. Besides providing implementations of these models, Ultralytics also provides us with out-of-the-box workflows for training, fine-tuning, and applying these models using an easy-to-use API.
-
-## Get started
-
-1. Install `ultralytics` and `wandb`.
-
- {{< tabpane text=true >}}
- {{% tab header="Command Line" value="script" %}}
-
- ```shell
- pip install --upgrade ultralytics==8.0.238 wandb
-
- # or
- # conda install ultralytics
- ```
-
- {{% /tab %}}
- {{% tab header="Notebook" value="notebook" %}}
-
- ```bash
- !pip install --upgrade ultralytics==8.0.238 wandb
- ```
-
- {{% /tab %}}
- {{< /tabpane >}}
-
- The development team has tested the integration with `ultralyticsv8.0.238` and below. To report any issues with the integration, create a [GitHub issue](https://github.com/wandb/wandb/issues/new?template=sdk-bug.yml) with the tag `yolov8`.
-
-## Track experiments and visualize validation results
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/ultralytics/01_train_val.ipynb" >}}
-
-This section demonstrates a typical workflow of using an [Ultralytics](https://docs.ultralytics.com/modes/predict/) model for training, fine-tuning, and validation and performing experiment tracking, model-checkpointing, and visualization of the model's performance using [W&B](https://wandb.ai/site).
-
-You can also check out about the integration in this report: [Supercharging Ultralytics with W&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
-
-To use the W&B integration with Ultralytics, import the `wandb.integration.ultralytics.add_wandb_callback` function.
-
-```python
-import wandb
-from wandb.integration.ultralytics import add_wandb_callback
-
-from ultralytics import YOLO
-```
-
-Initialize the `YOLO` model of your choice, and invoke the `add_wandb_callback` function on it before performing inference with the model. This ensures that when you perform training, fine-tuning, validation, or inference, it automatically saves the experiment logs and the images, overlaid with both ground-truth and the respective prediction results using the [interactive overlays for computer vision tasks]({{< relref "/guides/models/track/log/media#image-overlays-in-tables" >}}) on W&B along with additional insights in a [`wandb.Table`]({{< relref "/guides/models/tables/" >}}).
-
-```python
-with wandb.init(project="ultralytics", job_type="train") as run:
-
- # Initialize YOLO Model
- model = YOLO("yolov8n.pt")
-
- # Add W&B callback for Ultralytics
- add_wandb_callback(model, enable_model_checkpointing=True)
-
- # Train/fine-tune your model
- # At the end of each epoch, predictions on validation batches are logged
- # to a W&B table with insightful and interactive overlays for
- # computer vision tasks
- model.train(project="ultralytics", data="coco128.yaml", epochs=5, imgsz=640)
-```
-
-Here's how experiments tracked using W&B for an Ultralytics training or fine-tuning workflow looks like:
-
-
-
-## Visualize prediction results
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/ultralytics/00_inference.ipynb" >}}
-
-This section demonstrates a typical workflow of using an [Ultralytics](https://docs.ultralytics.com/modes/predict/) model for inference and visualizing the results using [W&B](https://wandb.ai/site).
-
-You can try out the code in Google Colab: [Open in Colab](https://wandb.me/ultralytics-inference).
-
-You can also check out about the integration in this report: [Supercharging Ultralytics with W&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
-
-In order to use the W&B integration with Ultralytics, we need to import the `wandb.integration.ultralytics.add_wandb_callback` function.
-
-```python
-import wandb
-from wandb.integration.ultralytics import add_wandb_callback
-
-from ultralytics.engine.model import YOLO
-```
-
-Download a few images to test the integration on. You can use still images, videos, or camera sources. For more information on inference sources, check out the [Ultralytics docs](https://docs.ultralytics.com/modes/predict/).
-
-```bash
-!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img1.png
-!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img2.png
-!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img4.png
-!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img5.png
-```
-
-Initialize a W&B [run]({{< relref "/guides/models/track/runs/" >}}) using `wandb.init()`. Next, Initialize your desired `YOLO` model and invoke the `add_wandb_callback` function on it before you perform inference with the model. This ensures that when you perform inference, it automatically logs the images overlaid with your [interactive overlays for computer vision tasks]({{< relref "/guides/models/track/log/media#image-overlays-in-tables" >}}) along with additional insights in a [`wandb.Table`]({{< relref "/guides/models/tables/" >}}).
-
-```python
-# Initialize W&B Run
-with wandb.init(project="ultralytics", job_type="inference") as run:
- # Initialize YOLO Model
- model = YOLO("yolov8n.pt")
-
- # Add W&B callback for Ultralytics
- add_wandb_callback(model, enable_model_checkpointing=True)
-
- # Perform prediction which automatically logs to a W&B Table
- # with interactive overlays for bounding boxes, segmentation masks
- model(
- [
- "./assets/img1.jpeg",
- "./assets/img3.png",
- "./assets/img4.jpeg",
- "./assets/img5.jpeg",
- ]
- )
-```
-
-You do not need to explicitly initialize a run using `wandb.init()` in case of a training or fine-tuning workflow. However, if the code involves only prediction, you must explicitly create a run.
-
-Here's how the interactive bbox overlay looks:
-
-
-
-For more details, see the [W&B image overlays guide]({{< relref "/guides/models/track/log/media.md#image-overlays" >}}).
-
-## More resources
-
-* [Supercharging Ultralytics with W&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
-* [Object Detection using YOLOv8: An End-to-End Workflow](https://wandb.ai/reviewco/object-detection-bdd/reports/Object-Detection-using-YOLOv8-An-End-to-End-Workflow--Vmlldzo1NTAyMDQ1)
\ No newline at end of file
diff --git a/content/en/guides/integrations/w-and-b-for-julia.md b/content/en/guides/integrations/w-and-b-for-julia.md
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index 73318ef33a..0000000000
--- a/content/en/guides/integrations/w-and-b-for-julia.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-description: How to integrate W&B with Julia.
-menu:
- default:
- identifier: w-and-b-for-julia
- parent: integrations
-title: W&B for Julia
-weight: 450
----
-
-For those running machine learning experiments in the Julia programming language, a community contributor has created an unofficial set of Julia bindings called [wandb.jl](https://github.com/avik-pal/Wandb.jl) that you can use.
-
-You can find examples [in the documentation](https://github.com/avik-pal/Wandb.jl/tree/main/docs/src/examples) on the wandb.jl repository. Their "Getting Started" example is here:
-
-```julia
-using Wandb, Dates, Logging
-
-# Start a new run, tracking hyperparameters in config
-lg = WandbLogger(project = "Wandb.jl",
- name = "wandbjl-demo-$(now())",
- config = Dict("learning_rate" => 0.01,
- "dropout" => 0.2,
- "architecture" => "CNN",
- "dataset" => "CIFAR-100"))
-
-# Use LoggingExtras.jl to log to multiple loggers together
-global_logger(lg)
-
-# Simulating the training or evaluation loop
-for x ∈ 1:50
- acc = log(1 + x + rand() * get_config(lg, "learning_rate") + rand() + get_config(lg, "dropout"))
- loss = 10 - log(1 + x + rand() + x * get_config(lg, "learning_rate") + rand() + get_config(lg, "dropout"))
- # Log metrics from your script to W&B
- @info "metrics" accuracy=acc loss=loss
-end
-
-# Finish the run
-close(lg)
-```
\ No newline at end of file
diff --git a/content/en/guides/integrations/xgboost.md b/content/en/guides/integrations/xgboost.md
deleted file mode 100644
index 515a97d8c6..0000000000
--- a/content/en/guides/integrations/xgboost.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-description: Track your trees with W&B.
-menu:
- default:
- identifier: xgboost
- parent: integrations
-title: XGBoost
-weight: 460
----
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Credit_Scorecards_with_XGBoost_and_W%26B.ipynb" >}}
-
-The `wandb` library has a `WandbCallback` callback for logging metrics, configs and saved boosters from training with XGBoost. Here you can see a [live W&B Dashboard](https://wandb.ai/morg/credit_scorecard) with outputs from the XGBoost `WandbCallback`.
-
-{{< img src="/images/integrations/xgb_dashboard.png" alt="W&B Dashboard using XGBoost" >}}
-
-## Get started
-
-Logging XGBoost metrics, configs and booster models to W&B is as easy as passing the `WandbCallback` to XGBoost:
-
-```python
-from wandb.integration.xgboost import WandbCallback
-import xgboost as XGBClassifier
-
-...
-# Start a wandb run
-with wandb.init() as run:
- # Pass WandbCallback to the model
- bst = XGBClassifier()
- bst.fit(X_train, y_train, callbacks=[WandbCallback(log_model=True)])
-```
-
-You can open [this notebook](https://wandb.me/xgboost) for a comprehensive look at logging with XGBoost and W&B
-
-## `WandbCallback` reference
-
-### Functionality
-Passing `WandbCallback` to a XGBoost model will:
-- log the booster model configuration to W&B
-- log evaluation metrics collected by XGBoost, such as rmse, accuracy etc to W&B
-- log training metrics collected by XGBoost (if you provide data to eval_set)
-- log the best score and the best iteration
-- save and upload your trained model to W&B Artifacts (when `log_model = True`)
-- log feature importance plot when `log_feature_importance=True` (default).
-- Capture the best eval metric in `wandb.Run.summary` when `define_metric=True` (default).
-
-### Arguments
-- `log_model`: (boolean) if True save and upload the model to W&B Artifacts
-
-- `log_feature_importance`: (boolean) if True log a feature importance bar plot
-
-- `importance_type`: (str) one of `{weight, gain, cover, total_gain, total_cover}` for tree model. weight for linear model.
-
-- `define_metric`: (boolean) if True (default) capture model performance at the best step, instead of the last step, of training in your `run.summary`.
-
-
-You can review the [source code for WandbCallback](https://github.com/wandb/wandb/blob/main/wandb/integration/xgboost/xgboost.py).
-
-For additional examples, check out the [repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms).
-
-## Tune your hyperparameters with Sweeps
-
-Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. W&B [Sweeps]({{< relref "/guides/models/sweeps/" >}}) is a powerful toolkit for configuring, orchestrating, and analyzing large hyperparameter testing experiments.
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Using_W%26B_Sweeps_with_XGBoost.ipynb" >}}
-
-You can also try this [XGBoost & Sweeps Python script](https://github.com/wandb/examples/blob/master/examples/wandb-sweeps/sweeps-xgboost/xgboost_tune.py).
-
-{{< img src="/images/integrations/xgboost_sweeps_example.png" alt="XGBoost performance comparison" >}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/yolov5.md b/content/en/guides/integrations/yolov5.md
deleted file mode 100644
index e7a4d8a7a7..0000000000
--- a/content/en/guides/integrations/yolov5.md
+++ /dev/null
@@ -1,71 +0,0 @@
----
-menu:
- default:
- identifier: yolov5
- parent: integrations
-title: YOLOv5
-weight: 470
----
-
-{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/yolo/Train_and_Debug_YOLOv5_Models_with_Weights_%26_Biases_.ipynb" >}}
-
-[Ultralytics' YOLOv5](https://ultralytics.com/yolo) ("You Only Look Once") model family enables real-time object detection with convolutional neural networks without all the agonizing pain.
-
-[W&B](https://wandb.com) is directly integrated into YOLOv5, providing experiment metric tracking, model and dataset versioning, rich model prediction visualization, and more. **It's as easy as running a single `pip install` before you run your YOLO experiments.**
-
-{{% alert %}}
-All W&B logging features are compatible with data-parallel multi-GPU training, such as with [PyTorch DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
-{{% /alert %}}
-
-## Track core experiments
-Simply by installing `wandb`, you'll activate the built-in W&B [logging features]({{< relref "/guides/models/track/log/" >}}): system metrics, model metrics, and media logged to interactive [Dashboards]({{< relref "/guides/models/track/workspaces.md" >}}).
-
-```python
-pip install wandb
-git clone https://github.com/ultralytics/yolov5.git
-python yolov5/train.py # train a small network on a small dataset
-```
-
-Just follow the links printed to the standard out by wandb.
-
-{{< img src="/images/integrations/yolov5_experiment_tracking.png" alt="All these charts and more." >}}
-
-## Customize the integration
-
-By passing a few simple command line arguments to YOLO, you can take advantage of even more W&B features.
-
-* If you pass a number to `--save_period`, W&B saves a [model version]({{< relref "/guides/core/registry/" >}}) at the end of every `save_period` epochs. The model version includes the model weights and tags the best-performing model in the validation set.
-* Turning on the `--upload_dataset` flag will also upload the dataset for data versioning.
-* Passing a number to `--bbox_interval` will turn on [data visualization]({{< relref "../" >}}). At the end of every `bbox_interval` epochs, the outputs of the model on the validation set will be uploaded to W&B.
-
-{{< tabpane text=true >}}
-{{% tab header="Model Versioning Only" value="modelversioning" %}}
-
-```python
-python yolov5/train.py --epochs 20 --save_period 1
-```
-
-{{% /tab %}}
-{{% tab header="Model Versioning and Data Visualization" value="bothversioning" %}}
-
-```python
-python yolov5/train.py --epochs 20 --save_period 1 \
- --upload_dataset --bbox_interval 1
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-{{% alert %}}
-Every W&B account comes with 100 GB of free storage for datasets and models.
-{{% /alert %}}
-
-Here's what that looks like.
-
-{{< img src="/images/integrations/yolov5_model_versioning.png" alt="Model versioning" >}}
-
-{{< img src="/images/integrations/yolov5_data_visualization.png" alt="Data visualization" >}}
-
-{{% alert %}}
-With data and model versioning, you can resume paused or crashed experiments from any device, no setup necessary. Check out [the Colab ](https://wandb.me/yolo-colab) for details.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/integrations/yolox.md b/content/en/guides/integrations/yolox.md
deleted file mode 100644
index 0fb2dd8388..0000000000
--- a/content/en/guides/integrations/yolox.md
+++ /dev/null
@@ -1,101 +0,0 @@
----
-description: How to integrate W&B with YOLOX.
-menu:
- default:
- identifier: yolox
- parent: integrations
-title: YOLOX
-weight: 490
----
-
-[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) is an anchor-free version of YOLO with strong performance for object detection. You can use the YOLOX W&B integration to turn on logging of metrics related to training, validation, and the system, and you can interactively validate predictions with a single command-line argument.
-
-## Sign up and create an API key
-
-An API key authenticates your machine to W&B. You can generate an API key from your user profile.
-
-{{% alert %}}
-For a more streamlined approach, you can generate an API key by going directly to the [W&B authorization page](https://wandb.ai/authorize). Copy the displayed API key and save it in a secure location such as a password manager.
-{{% /alert %}}
-
-1. Click your user profile icon in the upper right corner.
-1. Select **User Settings**, then scroll to the **API Keys** section.
-1. Click **Reveal**. Copy the displayed API key. To hide the API key, reload the page.
-
-## Install the `wandb` library and log in
-
-To install the `wandb` library locally and log in:
-
-{{< tabpane text=true >}}
-{{% tab header="Command Line" value="cli" %}}
-
-1. Set the `WANDB_API_KEY` [environment variable]({{< relref "/guides/models/track/environment-variables.md" >}}) to your API key.
-
- ```bash
- export WANDB_API_KEY=
- ```
-
-1. Install the `wandb` library and log in.
-
-
-
- ```shell
- pip install wandb
-
- wandb login
- ```
-
-{{% /tab %}}
-
-{{% tab header="Python" value="python" %}}
-
-```bash
-pip install wandb
-```
-```python
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-
-{{% tab header="Python notebook" value="python" %}}
-
-```notebook
-!pip install wandb
-
-import wandb
-wandb.login()
-```
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Log metrics
-
-Use the `--logger wandb` command line argument to turn on logging with wandb. Optionally you can also pass all of the arguments that [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) expects; prepend each argument with `wandb-`.
-
-`num_eval_imges` controls the number of validation set images and predictions that are logged to W&B tables for model evaluation.
-
-```shell
-# login to wandb
-wandb login
-
-# call your yolox training script with the `wandb` logger argument
-python tools/train.py .... --logger wandb \
- wandb-project \
- wandb-entity
- wandb-name \
- wandb-id \
- wandb-save_dir \
- wandb-num_eval_imges \
- wandb-log_checkpoints
-```
-
-## Example
-
-[Example dashboard with YOLOX training and validation metrics ->](https://wandb.ai/manan-goel/yolox-nano/runs/3pzfeom)
-
-{{< img src="/images/integrations/yolox_example_dashboard.png" alt="YOLOX training dashboard" >}}
-
-Any questions or issues about this W&B integration? Open an issue in the [YOLOX repository](https://github.com/Megvii-BaseDetection/YOLOX).
\ No newline at end of file
diff --git a/content/en/guides/models/_index.md b/content/en/guides/models/_index.md
deleted file mode 100644
index 806dcd90fb..0000000000
--- a/content/en/guides/models/_index.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-menu:
- default:
- identifier: models
-title: W&B Models
-weight: 30
-no_list: true
----
-
-W&B Models is the system of record for ML Practitioners who want to organize their models, boost productivity and collaboration, and deliver production ML at scale.
-
-{{< img src="/images/general/architecture.png" alt="W&B Models architecture diagram" >}}
-
-With W&B Models, you can:
-
-- Track and visualize all [ML experiments]({{< relref "/guides/models/track/" >}}).
-- Optimize and fine-tune models at scale with [hyperparameter sweeps]({{< relref "/guides/models/sweeps/" >}}).
-- [Maintain a centralized hub of all models]({{< relref "/guides/core/registry/" >}}), with a seamless handoff point to devops and deployment
-- Configure custom automations that trigger key workflows for [model CI/CD]({{< relref "/guides/core/automations/" >}}).
-
-Machine learning practitioners rely on W&B Models as their ML system of record to track and visualize experiments, manage model versions and lineage, and optimize hyperparameters.
\ No newline at end of file
diff --git a/content/en/guides/models/app/_index.md b/content/en/guides/models/app/_index.md
deleted file mode 100644
index b23541a57e..0000000000
--- a/content/en/guides/models/app/_index.md
+++ /dev/null
@@ -1,27 +0,0 @@
----
-menu:
- default:
- identifier: w-b-app-ui-reference
- parent: models
-title: W&B App UI
-url: guides/app
-cascade:
-- url: guides/app/:filename
----
-
-This section provides details to help you use the W&B App UI. Manage workspaces, teams, and registries, visualize and observe experiments, create panels and reports, configure automations, and more.
-
-Access the W&B App in a web browser.
-
-- A W&B Multi-tenant deployment is accessible on the public web at https://wandb.ai/.
-- A W&B Dedicated Cloud deployment is accessible at the domain you configured when you signed up for W&B Dedicated Cloud. An admin user can update the domain in the W&B Management Console. Click on the icon in the top right corner and then click **System console**.
-- A W&B Self-Managed deployment is accessible at the hostname you configured when you deployed W&B. For example, if you deploy using Helm, the hostname is configured in `values.global.host`. An admin user can update the domain in the W&B Management Console. Click on the icon in the top right corner and then click **System console**.
-
-Learn more:
-
-- [Track experiments]({{< relref "/guides/models/track/" >}}) using runs or sweeps.
-- [Configure deployment settings]({{< relref "settings-page/" >}}) and [defaults]({{< relref "features/cascade-settings.md" >}}).
-- [Add panels]({{< relref "/guides/models/app/features/panels/" >}}) to visualize your experiments, such as line plots, bar plots, media panels, query panels, and tables.
-- [Add custom charts]({{< relref "/guides/models/app/features/custom-charts/" >}}).
-- [Create and share reports]({{< relref "/guides/core/reports/" >}}).
-
diff --git a/content/en/guides/models/app/console-logs.md b/content/en/guides/models/app/console-logs.md
deleted file mode 100644
index 9bdcf1aa52..0000000000
--- a/content/en/guides/models/app/console-logs.md
+++ /dev/null
@@ -1,154 +0,0 @@
----
-title: Console logs
-url: guides/app/console-logs
----
-
-
-When you run an experiment, you may notice various messages printed to your console. W&B captures console logs and displays them in the W&B App. Use these messages to debug and monitor the behavior of your experiment.
-
-## View console logs
-
-Access console logs for a run in the W&B App:
-
-1. Navigate to your project in the W&B App.
-2. Select a run within the **Runs** table.
-3. Click the **Logs** tab in the project sidebar.
-
-{{% alert %}}
-Only 10,000 lines of your logs are shown due to storage limitations
-{{% /alert %}}
-
-
-
-## Types of console logs
-
-W&B captures several types of console logs: informational messages, warnings, and errors, with a prefix to indicate the log's severity.
-
-### Informational messages
-Informational messages provide updates about the run's progress and status. They are typically prefixed with `wandb:`.
-
-```text
-wandb: Starting Run: abc123
-wandb: Run data is saved locally in ./wandb/run-20240125_120000-abc123
-```
-
-### Warning messages
-Warnings about potential issues that don't stop execution are prefixed with `WARNING:`
-
-```text
-WARNING Found .wandb file, not streaming tensorboard metrics.
-WARNING These runs were logged with a previous version of wandb.
-```
-
-### Error messages
-Error messages for serious issues are prefixed with `ERROR:`. These indicate problems that may prevent the run from completing successfully.
-
-```text
-ERROR Unable to save notebook session history.
-ERROR Failed to save notebook.
-```
-
-## Console log settings
-
-Within your code, pass the `wandb.Settings` object to `wandb.init()` to configure how W&B handles console logs. Within `wandb.Settings`, you can set the following parameters to control console log behavior:
-
-- `show_errors`: If set to `True`, error messages are displayed in the W&B App. If set to `False`, error messages are not shown.
-- `silent`: If set to `True`, all W&B console output will be suppressed. This is useful for production environments where you want to minimize console noise.
-- `show_warnings`: If set to `True`, warning messages are displayed in the W&B App. If set to `False`, warning messages are not shown.
-- `show_info`: If set to `True`, informational messages are displayed in the W&B App. If set to `False`, informational messages are not shown.
-
-The following example shows how to configure these settings:
-
-```python
-import wandb
-
-settings = wandb.Settings(
- show_errors=True, # Show error messages in the W&B App
- silent=False, # Disable all W&B console output
- show_warnings=True # Show warning messages in the W&B App
-)
-
-with wandb.init(settings=settings) as run:
- # Your training code here
- run.log({"accuracy": 0.95})
-```
-
-## Custom logging
-
-W&B captures console logs from your application, but it does not interfere with your own logging setup. You can use Python's built-in `print()` function or the `logging` module to log messages.
-
-```python
-import wandb
-
-with wandb.init(project="my-project") as run:
- for i in range(100, 1000, 100):
- # This will log to W&B and print to console
- run.log({"epoch": i, "loss": 0.1 * i})
- print(f"epoch: {i} loss: {0.1 * i}")
-```
-
-The console logs will look similar to the following:
-
-```text
-1 epoch: 100 loss: 1.3191105127334595
-2 epoch: 200 loss: 0.8664389848709106
-3 epoch: 300 loss: 0.6157898902893066
-4 epoch: 400 loss: 0.4961796700954437
-5 epoch: 500 loss: 0.42592573165893555
-6 epoch: 600 loss: 0.3771176040172577
-7 epoch: 700 loss: 0.3393910825252533
-8 epoch: 800 loss: 0.3082585036754608
-9 epoch: 900 loss: 0.28154927492141724
-```
-
-## Time stamps
-
-Time stamps are automatically added to each console log entry. This allows you to track when each log message was generated.
-
-You can toggle the time stamps in the console logs on or off. Within the console page select the **Timestamp visible** dropdown in the top left corner. You can choose to show or hide the time stamps.
-
-## Search console logs
-
-Use the search bar at the top of the console logs page to filter logs by keywords. You can search for specific terms, labels, or error messages.
-
-## Filter with custom labels
-
-{{% alert color="secondary" %}}
-Parameters prefixed by `x_` (such as `x_label`) are in public preview. Create a [GitHub issue in the W&B repository](https://github.com/wandb/wandb) to provide feedback.
-{{% /alert %}}
-
-You can filter console logs based on the labels you pass as arguments for `x_label` in `wandb.Settings` in the UI search bar located at the top of the console log page.
-
-```python
-import wandb
-
-# Initialize a run in the primary node
-run = wandb.init(
- entity="entity",
- project="project",
- settings=wandb.Settings(
- x_label="custom_label" # (Optional) Custom label for filtering logs
- )
-)
-```
-
-## Download console logs
-
-Download console logs for a run in the W&B App:
-
-1. Navigate to your project in the W&B App.
-2. Select a run within the **Runs** table.
-3. Click the **Logs** tab in the project sidebar.
-4. Click the download button in the top right corner of the console logs page.
-
-
-## Copy console logs
-
-Copy console logs for a run in the W&B App:
-
-1. Navigate to your project in the W&B App.
-2. Select a run within the **Runs** table.
-3. Click the **Logs** tab in the project sidebar.
-4. Click the copy button in the top right corner of the console logs page.
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/cascade-settings.md b/content/en/guides/models/app/features/cascade-settings.md
deleted file mode 100644
index b3e936f288..0000000000
--- a/content/en/guides/models/app/features/cascade-settings.md
+++ /dev/null
@@ -1,127 +0,0 @@
----
-menu:
- default:
- identifier: cascade-settings
- parent: w-b-app-ui-reference
-title: Manage workspace, section, and panel settings
-url: guides/app/features/cascade-settings
----
-
-Within a given workspace page there are three different setting levels: workspaces, sections, and panels. [Workspace settings]({{< relref "#workspace-settings" >}}) apply to the entire workspace. [Section settings]({{< relref "#section-settings" >}}) apply to all panels within a section. [Panel settings]({{< relref "#panel-settings" >}}) apply to individual panels.
-
-## Workspace settings
-
-Workspace settings apply to all sections and all panels within those sections. You can edit two types of workspace settings: [Workspace layout]({{< relref "#workspace-layout-options" >}}) and [Line plots]({{< relref "#line-plots-options" >}}). **Workspace layouts** determine the structure of the workspace, while **Line plots** settings control the default settings for line plots in the workspace.ne plots** settings control the default settings for line plots in the workspace.
-
-To edit settings that apply to the overall structure of this workspace:
-
-1. Navigate to your project workspace.
-2. Click the gear icon next to the **New report** button to view the workspace settings.
-3. Choose **Workspace layout** to change the workspace's layout, or choose **Line plots** to configure default settings for line plots in the workspace.
-{{< img src="/images/app_ui/workspace_settings.png" alt="Workspace settings gear icon" >}}
-
-{{% alert %}}
-After customizing your workspace, you can use _workspace templates_ to quickly create new workspaces with the same settings. Refer to [Workspace templates]({{< relref "/guides/models/track/workspaces.md#workspace-templates" >}}).
-{{% /alert %}}
-
-### Workspace layout options
-
-Configure a workspaces layout to define the overall structure of the workspace. This includes sectioning logic and panel organization.
-
-{{< img src="/images/app_ui/workspace_layout_settings.png" alt="Workspace layout options" >}}
-
-The workspace layout options page shows whether the workspace generates panels automatically or manually. To adjust a workspace's panel generation mode, refer to [Panels]({{< relref "panels/" >}}).
-
-This table describes each workspace layout option.
-
-| Workspace setting | Description |
-| ----- | ----- |
-| **Hide empty sections during search** | Hide sections that do not contain any panels when searching for a panel.|
-| **Sort panels alphabetically** | Sort panels in your workspaces alphabetically. |
-| **Section organization** | Remove all existing sections and panels and repopulate them with new section names. Groups the newly populated sections either by first or last prefix. |
-
-{{% alert %}}
-W&B suggests that you organize sections by grouping the first prefix rather than grouping by the last prefix. Grouping by the first prefix can result in fewer sections and better performance.
-{{% /alert %}}
-
-### Line plots options
-Set global defaults and custom rules for line plots in a workspace by modifying the **Line plots** workspace settings.
-
-{{< img src="/images/app_ui/workspace_settings_line_plots.png" alt="Line plot settings" >}}
-
-You can edit two main settings within **Line plots** settings: **Data** and **Display preferences**. The **Data** tab contains the following settings:
-
-
-| Line plot setting | Description |
-| ----- | ----- |
-| **X axis** | The scale of the x-axis in line plots. The x-axis is set to **Step** by default. See the proceeding table for the list of x-axis options. |
-| **Range** | Minimum and maximum settings to display for x axis. |
-| **Smoothing** | Change the smoothing on the line plot. For more information about smoothing, see [Smooth line plots]({{< relref "/guides/models/app/features/panels/line-plot/smoothing.md" >}}). |
-| **Outliers** | Rescale to exclude outliers from the default plot min and max scale. |
-| **Point aggregation method** | Improve data visualization accuracy and performance. See [Point aggregation]({{< relref "/guides/models/app/features/panels/line-plot/sampling.md" >}}) for more information. |
-| **Max number of runs or groups** | Limit the number of runs or groups displayed on the line plot. |
-
-In addition to **Step**, there are other options for the x-axis:
-
-| X axis option | Description |
-| ------------- | ----------- |
-| **Relative Time (Wall)**| Timestamp since the process starts. For example, suppose start a run and resume that run the next day. If you then log something, the recorded point is 24 hours.|
-| **Relative Time (Process)** | Timestamp inside the running process. For example, suppose you start a run and let it continue for 10 seconds. The next day you resume that run. The point is recorded as 10 seconds. |
-| **Wall Time** | Minutes elapsed since the start of the first run on the graph. |
-| **Step** | Increments each time you call `wandb.Run.log()`.|
-
-
-
-{{% alert %}}
-For information on how to edit an individual line plot, see [Edit line panel settings]({{< relref "/guides/models/app/features/panels/line-plot/#edit-line-panel-settings" >}}) in Line plots.
-{{% /alert %}}
-
-
-Within the **Display preferences** tab, you can toggle the proceeding settings:
-
-| Display preference | Description |
-| ----- | ----- |
-| **Remove legends from all panels** | Remove the panel's legend |
-| **Display colored run names in tooltips** | Show the runs as colored text within the tooltip |
-| **Only show highlighted run in companion chart tooltip** | Display only highlighted runs in chart tooltip |
-| **Number of runs shown in tooltips** | Display the number of runs in the tooltip |
-| **Display full run names on the primary chart tooltip**| Display the full name of the run in the chart tooltip |
-
-
-
-
-## Section settings
-
-Section settings apply to all panels within that section. Within a workspace section you can sort panels, rearrange panels, and rename the section name.
-
-Modify section settings by selecting the three horizontal dots (**...**) in the upper right corner of a section.
-
-{{< img src="/images/app_ui/section_settings.png" alt="Section settings menu" >}}
-
-From the dropdown, you can edit the following settings that apply to the entire section:
-
-| Section setting | Description |
-| ----- | ----- |
-| **Rename a section** | Rename the name of the section |
-| **Sort panels A-Z** | Sort panels within a section alphabetically |
-| **Rearrange panels** | Select and drag a panel within a section to manually order your panels |
-
-The proceeding animation demonstrates how to rearrange panels within a section:
-
-{{< img src="/images/app_ui/rearrange_panels.gif" alt="Rearranging panels" >}}
-
-{{% alert %}}
-In addition to the settings described in the preceding table, you can also edit how sections appear in your workspaces such as **Add section below**, **Add section above**, **Delete section**, and **Add section to report**.
-{{% /alert %}}
-
-## Panel settings
-
-Customize an individual panel's settings to compare multiple lines on the same plot, calculate custom axes, rename labels, and more. To edit a panel's settings:
-
-1. Hover your mouse over the panel you want to edit.
-2. Select the pencil icon that appears.
-{{< img src="/images/app_ui/panel_settings.png" alt="Panel edit icon" >}}
-3. Within the modal that appears, you can edit settings related to the panel's data, display preferences, and more.
-{{< img src="/images/app_ui/panel_settings_modal.png" alt="Panel settings modal" >}}
-
-For a complete list of settings you can apply to a panel, see [Edit line panel settings]({{< relref "/guides/models/app/features/panels/line-plot/#edit-line-panel-settings" >}}).
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/custom-charts/_index.md b/content/en/guides/models/app/features/custom-charts/_index.md
deleted file mode 100644
index fecd90971c..0000000000
--- a/content/en/guides/models/app/features/custom-charts/_index.md
+++ /dev/null
@@ -1,347 +0,0 @@
----
-menu:
- default:
- identifier: intro
- parent: w-b-app-ui-reference
-title: Custom charts
-weight: 3
-url: guides/app/features/custom-charts
-cascade:
-- url: guides/app/features/custom-charts/:filename
----
-
-Custom charts are visualizations you create explicitly. Use custom charts to visualize complex data relationships and have greater control over the appearance and behavior of the chart.
-
-To create a custom chart, you can either:
-
-* [Build a chart in the W&B App](#create-a-custom-chart-in-the-wb-app) by pulling in data from your runs using a [GraphQL](https://graphql.org) query.
-* Log a `wandb.Table` and call the `wandb.plot()` function from your script.
-
-{{% alert title="Default charts" %}}
-Default charts are generated visualizations based on the data logged from your script with `wandb.Run.log()`. For example, consider the following code snippet:
-
-```python
-import wandb
-import math
-
-with wandb.init() as run:
- offset = random.random()
- for run_step in range(20):
- run.log({
- "acc": math.log(1 + random.random() + run_step) + offset,
- "val_acc": math.log(1 + random.random() + run_step) + offset * random.random(),
- })
-```
-
-This code logs two metrics `acc` and `val_acc` at each training step (each `wandb.Run.log()` call). Within the W&B App, the line plots for `acc` and `val_acc` are automatically generated and appear in the `Charts` panel of the **Workspace** tab.
-{{% /alert %}}
-
-
-## Create a custom chart in the W&B App
-
-To create a custom chart in the W&B App, you must first log data to one or more runs. This data can come from key-value pairs logged with `wandb.Run.log()`, or more complex data structures like `wandb.Table`. Once you log data, you can create a custom chart by pulling in the data using a [GraphQL](https://graphql.org) query.
-
-
-Suppose you want to create a line plot that shows the accuracy as a function of training step. To do this, you first log accuracy values to a run using the following code snippet:
-
-```python
-import wandb
-import math
-
-with wandb.init() as run:
- offset = random.random()
- for run_step in range(20):
- run.log({
- "acc": math.log(1 + random.random() + run_step) + offset,
- "val_acc": math.log(1 + random.random() + run_step) + offset * random.random(),
- })
-```
-
-Each call to `wandb.Run.log()` creates a new entry in the run's history. The `step` value is automatically tracked by W&B and increments with each log call.
-
-Once the code snippet completes, navigate to your project's workspace:
-
-
-
-
-
-1. Click the **Add panels** button in the top right corner, then select **Custom chart**.
-2. Select **Line plot** from the list of chart types.
-3. Within the [query editor](#query-editor), select `history` as the [data source](#query-data-sources). Next, select `acc` and type in `_step` as keys.
-4. Within the chart editor, select `_step` for the **X** field and `acc` for the **Y** field. Optionally, set a title for the chart. Your settings should look similar to the following:
-{{< img src="/images/app_ui/custom-charts-query-example.png" alt="Custom line plot settings" max-width="90%" >}}
-
-
-{{% alert %}}
-Note that only the keys you specify in the query editor are available in the chart editor (below the query editor). If you do not see the expected keys, go back to the query editor and ensure you have selected the correct data source and specified the correct keys.
-{{% /alert %}}
-
-Your custom line plot should now appear in the panel, showing the accuracy values over training steps.
-
-{{< img src="/images/app_ui/manual-line-plot.png" alt="Custom line plot example" max-width="90%" >}}
-
-### Query data sources
-
-When you create a custom chart, you can pull in data from your runs using a [GraphQL](https://graphql.org) query. The data you can query comes from:
-
-* `config`: Initial settings of your experiment (your independent variables). This includes any named fields you’ve logged as keys to `wandb.Run.config` at the start of your training. For example: wandb.Run.config.learning_rate = 0.0001
-* `summary`: A single-value metrics that summarize a run. It's populated by the final value of metrics logged with `wandb.Run.log()` or by directly updating the run.summary object. Think of it as a key-value store for your run's final results.
-* `history`: Time series data logged with `wandb.Run.log()`. Each call to `log()` creates a new row in the history table. This is ideal for tracking metrics that change over time, like training and validation loss.
-* `summaryTable`: A table of summary metrics. It's populated by logging a `wandb.Table` to the `summary` field. This is useful for logging metrics that are best represented in a tabular format, like confusion matrices or classification reports.
-* `historyTable`: A table of time series data. It's populated by logging a `wandb.Table` to the `history` field. This is useful for logging complex metrics that change over time, like per-epoch evaluation metrics.
-
-To recap, `summary` and `history` are the general locations for your run data, while `summaryTable` and `historyTable` are the specific query types needed to access tabular data stored in those respective locations.
-
-
-### Query editor
-
-Within the query editor, you can define a [GraphQL](https://graphql.org) query to pull in the data from available [data sources](#query-data-sources). The query editor consists of dropdowns and text fields that allow you to construct the query without needing to write raw GraphQL. You can include any combination of available data sources, depending on the data you want to visualize.
-
-{{% alert %}}
-Pay close attention to the data sources. For example, if you want to visualize time series data logged with `wandb.Run.log()`, you must select the `history` data source. If you select `summary`, you will not see the expected data because `summary` contains only single-value metrics that summarize a run, not time series data.
-{{% /alert %}}
-
-The `keys` argument acts as a filter to specify exactly which pieces of data you want to retrieve from a larger data object such as `summary`. The sub-fields are dictionary-like or key-value pairs.
-
-
-The general structure of the query is as follows:
-
-```graphql
-query {
- runSets: (runSets: "${runSets}", limit: 500 ) {
- id:
- name:
- summary:
- (keys: [""])
- history:
- (keys: [""])
- summaryTable:
- (keys: [""])
- historyTable:
- (keys: [""])
- }
-}
-```
-
-Here's a breakdown of the components:
-
-* `runSets`: Top-level object, representing the set of runs you are currently viewing or have filtered in the UI.
-* `summary(...)`, `history(...)`, `summaryTable(...)`, `historyTable(...)`: This tells the query to fetch data from the respective objects of each run.
-* `keys: [""]`: An array of strings where each string is the name (or key) of the metric or object you want to retrieve.
-
-## Log charts from a script
-
-You can programmatically create a custom chart from your script by logging a `wandb.Table` of the data you want to visualize, then calling `wandb.plot.*` to create the chart.
-
-For example, consider the following code snippet:
-
-```python
-import wandb
-import math
-
-with wandb.init() as run:
- offset = random.random()
-
- # Set up data to log in custom charts
- data = []
- for i in range(100):
- data.append([i, random.random() + math.log(1 + i) + offset + random.random()])
-
- # Create a table with the columns to plot
- table = wandb.Table(data=data, columns=["step", "height"])
-
- # Use the table to populate various custom charts
- line_plot = wandb.plot.line(table, x='step', y='height', title='Line Plot')
- histogram = wandb.plot.histogram(table, value='height', title='Histogram')
- scatter = wandb.plot.scatter(table, x='step', y='height', title='Scatter Plot')
-
- # Log custom tables, which will show up in customizable charts in the UI
- run.log({'line_1': line_plot,
- 'histogram_1': histogram,
- 'scatter_1': scatter})
-```
-
-Within the W&B app, navigate to the **Workspace** tab. Within the `Custom Charts` panel, there are three charts with the following titles: **Scatter Plot**, **Histogram**, and **Line Plot**. These correspond to the three charts created in the script above. The x-axis and y-axis are set to the columns specified in the `wandb.plot.*` function calls (`height` and `step`).
-
-The following image shows the three custom charts created from the script:
-
-{{< img src="/images/app_ui/custom-charts-script-plots.png" alt="Custom charts from script" max-width="90%" >}}
-
-### Built-in chart types
-
-W&B has a number of built-in chart presets that you can log directly from your script. These include line plots, scatter plots, bar charts, histograms, PR curves, and ROC curves. The following tabs show how to log each type of chart.
-
-{{< tabpane text=true >}}
-{{% tab header="Line plot" value="line-plot" %}}
-
- `wandb.plot.line()`
-
- Log a custom line plot—a list of connected and ordered points (x,y) on arbitrary axes x and y.
-
- ```python
- with wandb.init() as run:
- data = [[x, y] for (x, y) in zip(x_values, y_values)]
- table = wandb.Table(data=data, columns=["x", "y"])
- run.log(
- {
- "my_custom_plot_id": wandb.plot.line(
- table, "x", "y", title="Custom Y vs X Line Plot"
- )
- }
- )
- ```
-
- A line plot logs curves on any two dimensions. If you plot two lists of values against each other, the number of values in the lists must match exactly (for example, each point must have an x and a y).
-
- {{< img src="/images/app_ui/line_plot.png" alt="Custom line plot" >}}
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Custom-Line-Plots--VmlldzoyNjk5NTA) or [try an example Google Colab notebook](https://tiny.cc/custom-charts).
-
-{{% /tab %}}
-
-{{% tab header="Scatter plot" value="scatter-plot" %}}
-
- `wandb.plot.scatter()`
-
- Log a custom scatter plot—a list of points (x, y) on a pair of arbitrary axes x and y.
-
- ```python
- with wandb.init() as run:
- data = [[x, y] for (x, y) in zip(class_x_prediction_scores, class_y_prediction_scores)]
- table = wandb.Table(data=data, columns=["class_x", "class_y"])
- run.log({"my_custom_id": wandb.plot.scatter(table, "class_x", "class_y")})
- ```
-
- You can use this to log scatter points on any two dimensions. Note that if you're plotting two lists of values against each other, the number of values in the lists must match exactly (for example, each point must have an x and a y).
-
- {{< img src="/images/app_ui/demo_scatter_plot.png" alt="Scatter plot" >}}
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Custom-Scatter-Plots--VmlldzoyNjk5NDQ) or [try an example Google Colab notebook](https://tiny.cc/custom-charts).
-
-{{% /tab %}}
-
-{{% tab header="Bar chart" value="bar-chart" %}}
-
- `wandb.plot.bar()`
-
- Log a custom bar chart—a list of labeled values as bars—natively in a few lines:
-
- ```python
- with wandb.init() as run:
- data = [[label, val] for (label, val) in zip(labels, values)]
- table = wandb.Table(data=data, columns=["label", "value"])
- run.log(
- {
- "my_bar_chart_id": wandb.plot.bar(
- table, "label", "value", title="Custom Bar Chart"
- )
- }
- )
- ```
-
- You can use this to log arbitrary bar charts. Note that the number of labels and values in the lists must match exactly (for example, each data point must have both).
-
-{{< img src="/images/app_ui/demo_bar_plot.png" alt="Demo bar plot" >}}
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Custom-Bar-Charts--VmlldzoyNzExNzk) or [try an example Google Colab notebook](https://tiny.cc/custom-charts).
-{{% /tab %}}
-
-{{% tab header="Histogram" value="histogram" %}}
-
- `wandb.plot.histogram()`
-
- Log a custom histogram—sort list of values into bins by count/frequency of occurrence—natively in a few lines. Let's say I have a list of prediction confidence scores (`scores`) and want to visualize their distribution:
-
- ```python
- with wandb.init() as run:
- data = [[s] for s in scores]
- table = wandb.Table(data=data, columns=["scores"])
- run.log({"my_histogram": wandb.plot.histogram(table, "scores", title=None)})
- ```
-
- You can use this to log arbitrary histograms. Note that `data` is a list of lists, intended to support a 2D array of rows and columns.
-
- {{< img src="/images/app_ui/demo_custom_chart_histogram.png" alt="Custom histogram" >}}
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Custom-Histograms--VmlldzoyNzE0NzM) or [try an example Google Colab notebook](https://tiny.cc/custom-charts).
-
-{{% /tab %}}
-
-{{% tab header="PR curve" value="pr-curve" %}}
-
- `wandb.plot.pr_curve()`
-
- Create a [Precision-Recall curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve) in one line:
-
- ```python
- with wandb.init() as run:
- plot = wandb.plot.pr_curve(ground_truth, predictions, labels=None, classes_to_plot=None)
-
- run.log({"pr": plot})
- ```
-
- You can log this whenever your code has access to:
-
- * a model's predicted scores (`predictions`) on a set of examples
- * the corresponding ground truth labels (`ground_truth`) for those examples
- * (optionally) a list of the labels/class names (`labels=["cat", "dog", "bird"...]` if label index 0 means cat, 1 = dog, 2 = bird, etc.)
- * (optionally) a subset (still in list format) of the labels to visualize in the plot
-
- {{< img src="/images/app_ui/demo_average_precision_lines.png" alt="Precision-recall curves" >}}
-
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Plot-Precision-Recall-Curves--VmlldzoyNjk1ODY) or [try an example Google Colab notebook](https://colab.research.google.com/drive/1mS8ogA3LcZWOXchfJoMrboW3opY1A8BY?usp=sharing).
-
-{{% /tab %}}
-
-{{% tab header="ROC curve" value="roc-curve" %}}
-
- `wandb.plot.roc_curve()`
-
- Create an [ROC curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve) in one line:
-
- ```python
- with wandb.init() as run:
- # ground_truth is a list of true labels, predictions is a list of predicted scores
- ground_truth = [0, 1, 0, 1, 0, 1]
- predictions = [0.1, 0.4, 0.35, 0.8, 0.7, 0.9]
-
- # Create the ROC curve plot
- # labels is an optional list of class names, classes_to_plot is an optional subset of those labels to visualize
- plot = wandb.plot.roc_curve(
- ground_truth, predictions, labels=None, classes_to_plot=None
- )
-
- run.log({"roc": plot})
- ```
-
- You can log this whenever your code has access to:
-
- * a model's predicted scores (`predictions`) on a set of examples
- * the corresponding ground truth labels (`ground_truth`) for those examples
- * (optionally) a list of the labels/ class names (`labels=["cat", "dog", "bird"...]` if label index 0 means cat, 1 = dog, 2 = bird, etc.)
- * (optionally) a subset (still in list format) of these labels to visualize on the plot
-
- {{< img src="/images/app_ui/demo_custom_chart_roc_curve.png" alt="ROC curve" >}}
-
- [See an example report](https://wandb.ai/wandb/plots/reports/Plot-ROC-Curves--VmlldzoyNjk3MDE) or [try an example Google Colab notebook](https://colab.research.google.com/drive/1_RMppCqsA8XInV_jhJz32NCZG6Z5t1RO?usp=sharing).
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-
-
-
-## Additional resources
-
-
-* Log your first custom chart with this [Colab notebook](https://tiny.cc/custom-charts) or read its companion [W&B Machine Learning Visualization IDE](https://wandb.ai/wandb/posts/reports/The-W-B-Machine-Learning-Visualization-IDE--VmlldzoyNjk3Nzg) report.
-* Try the [Logging custom visualizations with W&B using Keras and Sklearn Colab](https://colab.research.google.com/drive/1g-gNGokPWM2Qbc8p1Gofud0_5AoZdoSD?usp=sharing) notebook.
-* Read the [Visualizing NLP Attention Based Models](https://wandb.ai/kylegoyette/gradientsandtranslation2/reports/Visualizing-NLP-Attention-Based-Models-Using-Custom-Charts--VmlldzoyNjg2MjM) report.
-* Explore the [Visualizing The Effect of Attention on Gradient Flow](https://wandb.ai/kylegoyette/gradientsandtranslation/reports/Visualizing-The-Effect-of-Attention-on-Gradient-Flow-Using-Custom-Charts--VmlldzoyNjg1NDg) report.
-* Read the [Logging arbitrary curves](https://wandb.ai/stacey/presets/reports/Logging-Arbitrary-Curves--VmlldzoyNzQyMzA) report.
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/custom-charts/walkthrough.md b/content/en/guides/models/app/features/custom-charts/walkthrough.md
deleted file mode 100644
index 245e278c33..0000000000
--- a/content/en/guides/models/app/features/custom-charts/walkthrough.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-description: Tutorial of using the custom charts feature in the W&B UI
-menu:
- default:
- identifier: walkthrough
- parent: custom-charts
-title: 'Tutorial: Use custom charts'
----
-
-Use custom charts to control the data you're loading in to a panel and its visualization.
-
-
-## 1. Log data to W&B
-
-First, log data in your script. Use [wandb.Run.config]({{< relref "/guides/models/track/config.md" >}}) for single points set at the beginning of training, like hyperparameters. Use [wandb.Run.log()]({{< relref "/guides/models/track/log/" >}}) for multiple points over time, and log custom 2D arrays with `wandb.Table()`. We recommend logging up to 10,000 data points per logged key.
-
-```python
-with wandb.init() as run:
-
- # Logging a custom table of data
- my_custom_data = [[x1, y1, z1], [x2, y2, z2]]
- run.log(
- {"custom_data_table": wandb.Table(data=my_custom_data, columns=["x", "y", "z"])}
- )
-```
-
-[Try a quick example notebook](https://bit.ly/custom-charts-colab) to log the data tables, and in the next step we'll set up custom charts. See what the resulting charts look like in the [live report](https://app.wandb.ai/demo-team/custom-charts/reports/Custom-Charts--VmlldzoyMTk5MDc).
-
-## 2. Create a query
-
-Once you've logged data to visualize, go to your project page and click the **`+`** button to add a new panel, then select **Custom Chart**. You can follow along in the [custom charts demo workspace](https://app.wandb.ai/demo-team/custom-charts).
-
-{{< img src="/images/app_ui/create_a_query.png" alt="Blank custom chart" >}}
-
-### Add a query
-
-1. Click `summary` and select `historyTable` to set up a new query pulling data from the run history.
-2. Type in the key where you logged the `wandb.Table()`. In the code snippet above, it was `my_custom_table` . In the [example notebook](https://bit.ly/custom-charts-colab), the keys are `pr_curve` and `roc_curve`.
-
-### Set Vega fields
-
-Now that the query is loading in these columns, they're available as options to select in the Vega fields dropdown menus:
-
-{{< img src="/images/app_ui/set_vega_fields.png" alt="Pulling in columns from the query results to set Vega fields" >}}
-
-* **x-axis:** runSets_historyTable_r (recall)
-* **y-axis:** runSets_historyTable_p (precision)
-* **color:** runSets_historyTable_c (class label)
-
-## 3. Customize the chart
-
-Now that looks pretty good, but I'd like to switch from a scatter plot to a line plot. Click **Edit** to change the Vega spec for this built in chart. Follow along in the [custom charts demo workspace](https://app.wandb.ai/demo-team/custom-charts).
-
-{{< img src="/images/general/custom-charts-1.png" alt="Custom chart selection" >}}
-
-I updated the Vega spec to customize the visualization:
-
-* add titles for the plot, legend, x-axis, and y-axis (set “title” for each field)
-* change the value of “mark” from “point” to “line”
-* remove the unused “size” field
-
-{{< img src="/images/app_ui/customize_vega_spec_for_pr_curve.png" alt="PR curve Vega spec" >}}
-
-To save this as a preset that you can use elsewhere in this project, click **Save as** at the top of the page. Here's what the result looks like, along with an ROC curve:
-
-{{< img src="/images/general/custom-charts-2.png" alt="PR curve chart" >}}
-
-## Bonus: Composite Histograms
-
-Histograms can visualize numerical distributions to help us understand larger datasets. Composite histograms show multiple distributions across the same bins, letting us compare two or more metrics across different models or across different classes within our model. For a semantic segmentation model detecting objects in driving scenes, we might compare the effectiveness of optimizing for accuracy versus intersection over union (IOU), or we might want to know how well different models detect cars (large, common regions in the data) versus traffic signs (much smaller, less common regions). In the[ demo Colab](https://bit.ly/custom-charts-colab), you can compare the confidence scores for two of the ten classes of living things.
-
-{{< img src="/images/app_ui/composite_histograms.png" alt="Composite histogram" >}}
-
-To create your own version of the custom composite histogram panel:
-
-1. Create a new Custom Chart panel in your Workspace or Report (by adding a “Custom Chart” visualization). Hit the “Edit” button in the top right to modify the Vega spec starting from any built-in panel type.
-2. Replace that built-in Vega spec with my [MVP code for a composite histogram in Vega](https://gist.github.com/staceysv/9bed36a2c0c2a427365991403611ce21). You can modify the main title, axis titles, input domain, and any other details directly in this Vega spec [using Vega syntax](https://vega.github.io/) (you could change the colors or even add a third histogram :)
-3. Modify the query in the right hand side to load the correct data from your wandb logs. Add the field `summaryTable` and set the corresponding `tableKey` to `class_scores` to fetch the `wandb.Table` logged by your run. This will let you populate the two histogram bin sets (`red_bins` and `blue_bins`) via the dropdown menus with the columns of the `wandb.Table` logged as `class_scores`. For my example, I chose the `animal` class prediction scores for the red bins and `plant` for the blue bins.
-4. You can keep making changes to the Vega spec and query until you’re happy with the plot you see in the preview rendering. Once you’re done, click **Save as** in the top and give your custom plot a name so you can reuse it. Then click **Apply from panel library** to finish your plot.
-
-Here’s what my results look like from a very brief experiment: training on only 1000 examples for one epoch yields a model that’s very confident that most images are not plants and very uncertain about which images might be animals.
-
-{{< img src="/images/general/custom-charts-3.png" alt="Chart configuration" >}}
-
-{{< img src="/images/general/custom-charts-4.png" alt="Chart result" >}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/_index.md b/content/en/guides/models/app/features/panels/_index.md
deleted file mode 100644
index 6da99aeedc..0000000000
--- a/content/en/guides/models/app/features/panels/_index.md
+++ /dev/null
@@ -1,216 +0,0 @@
----
-menu:
- default:
- identifier: intro_panels
- parent: w-b-app-ui-reference
-title: Panels
-weight: 2
-url: guides/app/features/panels
-cascade:
-- url: guides/app/features/panels/:filename
----
-
-Use workspace panel visualizations to explore your [logged data]({{< relref "/ref/python/experiments/run.md/#method-runlog" >}}) by key, visualize the relationships between hyperparameters and output metrics, and more.
-
-## Workspace modes
-
-W&B projects support two different workspace modes. The icon next to the workspace name shows its mode.
-
-| Icon | Workspace mode |
-| --- | --- |
-| {{< img src="/images/app_ui/automated_workspace.svg" alt="automated workspace icon" width="32px" >}} | **Automated workspaces** automatically generate panels for all keys logged in the project. Choose an automatic workspace:
To get started quickly by visualizing all available data for the project.
For a smaller projects that log fewer keys.
For more broad analysis.
If you delete a panel from an automatic workspace, you can use [Quick add]({{< relref "#quick-add" >}}) to recreate it. |
-| {{}} | **Manual workspaces** start as blank slates and display only those panels intentionally added by users. Choose a manual workspace:
When you care mainly about a fraction of the keys logged in the project.
For more focused analysis.
To improve the performance of a workspace, avoiding loading panels that are less useful to you.
Use [Quick add]({{< relref "#quick-add" >}}) to easily populate a manual workspace and its sections with useful visualizations rapidly. |
-
-To change how a workspace generates panels, [reset the workspace]({{< relref "#reset-a-workspace" >}}).
-
-{{% alert title="Undo changes to your workspace" %}}
-To undo changes to your workspace, click the Undo button (arrow that points left) or type **CMD + Z** (macOS) or **CTRL + Z** (Windows / Linux).
-{{% /alert %}}
-
-## Reset a workspace
-
-To reset a workspace:
-
-1. At the top of the workspace, click the action menu `...`.
-1. Click **Reset workspace**.
-
-## Configure the workspace layout {#configure-workspace-layout}
-
-To configure the workspace layout, click **Settings** near the top of the workspace, then click **Workspace layout**.
-
-- **Hide empty sections during search** (turned on by default)
-- **Sort panels alphabetically** (turned off by default)
-- **Section organization** (grouped by first prefix by default). To modify this setting:
- 1. Click the padlock icon.
- 1. Choose how to group panels within a section.
-
-To configure defaults for the workspace's line plots, refer to [Line plots]({{< relref "line-plot/#all-line-plots-in-a-workspace" >}}).
-
-### Configure a section's layout {#configure-section-layout}
-
-To configure the layout of a section, click its gear icon, then click **Display preferences**.
-- **Turn on or off colored run names in tooltips** (turned on by default)
-- **Only show highlighted run in companion chart tooltips** (turned off by default)
-- **Number of runs shown in tooltips** (a single run, all runs, or **Default**)
-- **Display full run names on the primary chart tooltip** (turned off by default)
-
-## View a panel in full-screen mode
-
-In full-screen mode, the run selector displays and panels use full-fidelity sampling mode plots with 10,000 buckets, rather than 1000 buckets otherwise.
-
-To view a panel in full-screen mode:
-
-1. Hover over the panel.
-1. Click the panel's action menu `...`, then click the full-screen button, which looks like a viewfinder or an outline showing the four corners of a square.
- {{< img src="/images/app_ui/panel_fullscreen.png" alt="Full-screen panel" >}}
-1. When you [share the panel]({{< relref "#share-a-panel" >}}) while viewing it in full-screen mode, the resulting link opens in full-screen mode automatically.
-
-To get back to a panel's workspace from full-screen mode, click the left-pointing arrow at the top of the page. To navigate through a section's panels without exiting full-screen mode, use either the **Previous** and **Next** buttons below the panel or the left and right arrow keys.
-
-## Add panels
-
-This section shows various ways to add panels to your workspace.
-
-### Add a panel manually
-
-Add panels to your workspace one at a time, either globally or at the section level.
-
-1. To add a panel globally, click **Add panels** in the control bar near the panel search field.
-1. To add a panel directly to a section instead, click the section's action `...` menu, then click **+ Add panels**.
-1. Select the type of panel to add, such as a chart. The panel's configuration details appear, with defaults selected.
-1. Optionally, customize the panel and its display preferences. Configuration options depend on the type of panel you select. To learn more about the options for each type of panel, refer to the relevant section below, such as [Line plots]({{< relref "line-plot/" >}}) or [Bar plots]({{< relref "bar-plot.md" >}}).
-1. Click **Apply**.
-
-{{< img src="/images/app_ui/add_single_panel.gif" alt="Demo of adding a panel" >}}
-
-### Quick add panels {#quick-add}
-
-Use **Quick add** to add a panel automatically for each key you select, either globally or at the section level.
-
-{{% alert %}}
-For an automated workspace with no deleted panels, the **Quick add** option is not visible because the workspace already includes panels for all logged keys. You can use **Quick add** to re-add a panel that you deleted.
-{{% /alert %}}
-
-1. To use **Quick add** to add a panel globally, click **Add panels** in the control bar near the panel search field, then click **Quick add**.
-1. To use **Quick add** to add a panel directly to a section, click the section's action `...` menu, click **Add panels**, then click **Quick add**.
-1. A list of panels appears. Each panel with a checkmark is already included in the workspace.
- - To add all available panels, click the **Add panels** button at the top of the list. The **Quick Add** list closes and the new panels display in the workspace.
- - To add an individual panel from the list, hover over the panel's row, then click **Add**. Repeat this step for each panel you want to add, then click the **X** at the top right to close the **Quick Add** list. The new panels display in the workspace.
-1. Optionally, customize the panel's settings.
-
-## Share a panel
-
-This section shows how to share a panel using a link.
-
-To share a panel using a link, you can either:
-
-- While viewing the panel in full-screen mode, copy the URL from the browser.
-- Click the action menu `...` and select **Copy panel URL**.
-
-Share the link with the user or team. When they access the link, the panel opens in [full-screen mode]({{< relref "#view-a-panel-in-full-screen-mode" >}}).
-
-To return to a panel's workspace from full-screen mode, click the left-pointing arrow at the top of the page.
-
-### Compose a panel's full-screen link programmatically
-In certain situations, such as when [creating an automation]({{< relref "/guides/core/automations/" >}}), it can be useful to include the panel's full-screen URL. This section shows the format for a panel's full-screen URL. In the proceeding example, replace the entity, project, panel, and section names in brackets.
-
-```text
-https://wandb.ai//?panelDisplayName=&panelSectionName=
-```
-
-If multiple panels in the same section have the same name, this URL opens the first panel with the name.
-
-### Embed or share a panel on social media
-To embed a panel in a website or share it on social media, the panel must be viewable by anyone with the link. If a project is private, only members of the project can view the panel. If the project is public, anyone with the link can view the panel.
-
-To get the code to embed or share a panel on social media:
-
-1. From the workspace, hover over the panel, then click its action menu `...`.
-1. Click the **Share** tab.
-1. Change **Only those who are invited have access** to **Anyone with the link can view**. Otherwise, the choices in the next step are not available.
-1. Choose **Share on Twitter**, **Share on Reddit**, **Share on LinkedIn**, or **Copy embed link**.
-
-### Email a panel report
-To email a single panel as a stand-alone report:
-1. Hover over the panel, then click the panel's action menu `...`.
-1. Click **Share panel in report**.
-1. Select the **Invite** tab.
-1. Enter an email address or username.
-1. Optionally, change **can view** to **can edit**.
-1. Click **Invite**. W&B sends an email to the user with a clickable link to the report that contains only the panel you are sharing.
-
-Unlike when you [share a panel]({{< relref "#share-a-panel" >}}), the recipient cannot get to the workspace from this report.
-
-## Manage panels
-
-### Edit a panel
-
-To edit a panel:
-
-1. Click its pencil icon.
-1. Modify the panel's settings.
-1. To change the panel to a different type, select the type and then configure the settings.
-1. Click **Apply**.
-
-### Move a panel
-
-To move a panel to a different section, you can use the drag handle on the panel. To select the new section from a list instead:
-
-1. If necessary, create a new section by clicking **Add section** after the last section.
-1. Click the action `...` menu for the panel.
-1. Click **Move**, then select a new section.
-
-You can also use the drag handle to rearrange panels within a section.
-
-### Duplicate a panel
-
-To duplicate a panel:
-
-1. At the top of the panel, click the action `...` menu.
-1. Click **Duplicate**.
-
-If desired, you can [customize]({{< relref "#edit-a-panel" >}}) or [move]({{< relref "#move-a-panel" >}}) the duplicated panel.
-
-### Remove panels
-
-To remove a panel:
-
-1. Hover your mouse over the panel.
-1. Select the action `...` menu.
-1. Click **Delete**.
-
-To remove all panels from a manual workspace, click its action `...` menu, then click **Clear all panels**.
-
-To remove all panels from an automatic or manual workspace, you can [reset the workspace]({{< relref "#reset-a-workspace" >}}). Select **Automatic** to start with the default set of panels, or select **Manual** to start with an empty workspace with no panels.
-
-## Manage sections
-
-By default, sections in a workspace reflect the logging hierarchy of your keys. However, in a manual workspace, sections appear only after you start adding panels.
-
-### Add a section
-
-To add a section, click **Add section** after the last section.
-
-To add a new section before or after an existing section, you can instead click the section's action `...` menu, then click **New section below** or **New section above**.
-
-
-### Manage a section's panels
-Sections with a large number of panels are paginated by default. The default number of panels on a page depend on the panel's configuration and on the sizes of the panels in the section.
-
-1. To resize a panel, hover over it, click the drag handle, and drag it to adjust the panel's size. Resizing one panel resizes all panels in the section.
-1. If a section is paginated, you can customize the number of panels to show on a page:
- 1. At the top of the section, click **1 to of **, where `` is the number of visible panels and `` is the total number of panels.
- 1. Choose how many panels to show per page, up to 100.
-1. To delete a panel from a section:
- 1. Hover over the panel, then click its action `...` menu.
- 1. Click **Delete**.
-
-If you reset a workspace to an automated workspace, all deleted panels appear again.
-
-### Rename a section
-
-To rename a section, click its action `...` menu, then click **Rename section**.
-
-### Delete a section
-
-To delete a section, click its `...` menu, then click **Delete section**. This removes the section and its panels.
diff --git a/content/en/guides/models/app/features/panels/bar-plot.md b/content/en/guides/models/app/features/panels/bar-plot.md
deleted file mode 100644
index c3faa86f91..0000000000
--- a/content/en/guides/models/app/features/panels/bar-plot.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-description: Visualize metrics, customize axes, and compare categorical data as bars.
-menu:
- default:
- identifier: bar-plot
- parent: panels
-title: Bar plots
-weight: 20
----
-
-A bar plot presents categorical data with rectangular bars which can be plotted vertically or horizontally. Bar plots show up by default with `wandb.Run.log()` when all logged values are of length one.
-
-{{< img src="/images/app_ui/bar_plot.png" alt="Plotting Box and horizontal Bar plots in W&B" >}}
-
-Customize with chart settings to limit max runs to show, group runs by any config and rename labels.
-
-{{< img src="/images/app_ui/bar_plot_custom.png" alt="Customized bar plot" >}}
-
-## Customize bar plots
-
-You can also create **Box** or **Violin** Plots to combine many summary statistics into one chart type**.**
-
-1. Group runs via runs table.
-2. Click 'Add panel' in the workspace.
-3. Add a standard 'Bar Chart' and select the metric to plot.
-4. Under the 'Grouping' tab, pick 'box plot' or 'Violin', etc. to plot either of these styles.
-
-{{< img src="/images/app_ui/bar_plots.gif" alt="Customize Bar Plots" >}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/code.md b/content/en/guides/models/app/features/panels/code.md
deleted file mode 100644
index dfbb9301c7..0000000000
--- a/content/en/guides/models/app/features/panels/code.md
+++ /dev/null
@@ -1,70 +0,0 @@
----
-menu:
- default:
- identifier: code
- parent: panels
-title: Save and diff code
-weight: 50
----
-
-By default, W&B only saves the latest git commit hash. You can turn on more code features to compare the code between your experiments dynamically in the UI.
-
-Starting with `wandb` version 0.8.28, W&B can save the code from your main training file where you call `wandb.init()`.
-
-## Save library code
-
-When you enable code saving, W&B saves the code from the file that called `wandb.init()`. To save additional library code, you have three options:
-
-### Call `wandb.Run.log_code(".")` after calling `wandb.init()`
-
-```python
-import wandb
-
-with wandb.init() as run:
- run.log_code(".")
-```
-
-### Pass a settings object to `wandb.init()` with `code_dir` set
-
-```python
-import wandb
-
-wandb.init(settings=wandb.Settings(code_dir="."))
-```
-
-This captures all python source code files in the current directory and all subdirectories as an [artifact]({{< relref "/ref/python/experiments/artifact.md" >}}). For more control over the types and locations of source code files that are saved, see the [reference docs]({{< relref "/ref/python/experiments/run.md#log_code" >}}).
-
-### Set code saving in the UI
-
-In addition to setting code saving programmatically, you can also toggle this feature in your W&B account Settings. Note that this will enable code saving for all teams associated with your account.
-
-> By default, W&B disables code saving for all teams.
-
-1. Log in to your W&B account.
-2. Go to **Settings** > **Privacy**.
-3. Under **Project and content security**, toggle **Disable default code saving** on.
-
-## Code comparer
-Compare code used in different W&B runs:
-
-1. Select the **Add panels** button in the top right corner of the page.
-2. Expand **TEXT AND CODE** dropdown and select **Code**.
-
-
-{{< img src="/images/app_ui/code_comparer.png" alt="Code comparer panel" >}}
-
-## Jupyter session history
-
-W&B saves the history of code executed in your Jupyter notebook session. When you call **wandb.init()** inside of Jupyter, W&B adds a hook to automatically save a Jupyter notebook containing the history of code executed in your current session.
-
-
-1. Navigate to your project workspaces that contains your code.
-2. Select the **Artifacts** tab in the left navigation bar.
-3. Expand the **code** artifact.
-4. Select the **Files** tab.
-
-{{< img src="/images/app_ui/jupyter_session_history.gif" alt="Jupyter session history" >}}
-
-This displays the cells that were run in your session along with any outputs created by calling iPython’s display method. This enables you to see exactly what code was run within Jupyter in a given run. When possible W&B also saves the most recent version of the notebook which you would find in the code directory as well.
-
-{{< img src="/images/app_ui/jupyter_session_history_display.png" alt="Jupyter session output" >}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/line-plot/_index.md b/content/en/guides/models/app/features/panels/line-plot/_index.md
deleted file mode 100644
index 018a3b32d0..0000000000
--- a/content/en/guides/models/app/features/panels/line-plot/_index.md
+++ /dev/null
@@ -1,228 +0,0 @@
----
-description: Visualize metrics, customize axes, and compare multiple lines on a plot
-url: guides/app/features/panels/line-plot
-menu:
- default:
- identifier: intro_line_plot
- parent: panels
-cascade:
-- url: guides/app/features/panels/line-plot/:filename
-title: Line plots
-weight: 10
----
-
-Line plots show up by default when you plot metrics over time with `wandb.Run.log()`. Customize with chart settings to compare multiple lines on the same plot, calculate custom axes, and rename labels.
-
-{{< img src="/images/app_ui/line_plot_example.png" alt="Line plot example" >}}
-
-## Edit line plot settings
-
-This section shows how to edit the settings for an individual line plot panel, all line plot panels in a section, or all line plot panels in a workspace.
-
-{{% alert %}}
-If you'd like to use a custom x-axis, make sure it's logged in the same call to `wandb.Run.log()` that you use to log the y-axis.
-{{% /alert %}}
-
-### Individual line plot
-A line plot's individual settings override the line plot settings for the section or the workspace. To customize a line plot:
-
-1. Hover your mouse over the panel, then click the gear icon.
-1. Within the drawer that appears, select a tab to edit its [settings]({{< relref "#line-plot-settings" >}}).
-1. Click **Apply**.
-
-#### Line plot settings
-You can configure these settings for a line plot:
-
-**Date**: Configure the plot's data-display details.
-* **X axis**: Select the value to use for the X axis (defaults to **Step**). You can change the x-axis to **Relative Time** or select a custom axis based on values you log with W&B. You can also configure the X axis scale and range.
- * **Relative Time (Wall)** is clock time since the process started, so if you started a run and resumed it a day later and logged something that would be plotted a 24hrs.
- * **Relative Time (Process)** is time inside the running process, so if you started a run and ran for 10 seconds and resumed a day later that point would be plotted at 10s.
- * **Wall Time** is minutes elapsed since the start of the first run on the graph.
- * **Step** increments by default each time `wandb.Run.log()` is called, and is supposed to reflect the number of training steps you've logged from your model.
-* **Y axis**: Select one or more y-axes from the logged values, including metrics and hyperparameters that change over time. You can also configure the X axis scale and range.
-* **Point aggregation method**. Either **Random sampling** (the default) or **Full fidelity**. Refer to [Sampling]({{< relref "sampling.md" >}}).
-* **Smoothing**: Change the smoothing on the line plot. Defaults to **Time weighted EMA**. Other values include **No smoothing**, **Running average**, and **Gaussian**.
-* **Outliers**: Rescale to exclude outliers from the default plot min and max scale.
-* **Max number of runs or groups**: Show more lines on the line plot at once by increasing this number, which defaults to 10 runs. You'll see the message "Showing first 10 runs" on the top of the chart if there are more than 10 runs available but the chart is constraining the number visible.
-* **Chart type**: Change between a line plot, an area plot, and a percentage area plot.
-
-**Grouping**: Configure whether and how to group and aggregate runs in the plot.
-* **Group by**: Select a column, and all the runs with the same value in that column will be grouped together.
-* **Agg**: Aggregation— the value of the line on the graph. The options are mean, median, min, and max of the group.
-
-**Chart**: Specify titles for the panel, the X axis, and the Y axis, and the -axis, hide or show the legend, and configure its position.
-
-**Legend**: Customize the appearance of the panel's legend, if it is enabled.
-* **Legend**: The field in the legend for each line in the plot in the legend of the plot for each line.
-* **Legend template**: Define a fully customizable template for the legend, specifying exactly what text and variables you want to show up in the template at the top of the line plot as well as the legend that appears when you hover your mouse over the plot.
-
-**Expressions**: Add custom calculated expressions to the panel.
-* **Y Axis Expressions**: Add calculated metrics to your graph. You can use any of the logged metrics as well as configuration values like hyperparameters to calculate custom lines.
-* **X Axis Expressions**: Rescale the x-axis to use calculated values using custom expressions. Useful variables include\*\*_step\*\* for the default x-axis, and the syntax for referencing summary values is `${summary:value}`
-
-### All line plots in a section
-
-To customize the default settings for all line plots in a section, overriding workspace settings for line plots:
-1. Click the section's gear icon to open its settings.
-1. Within the drawer that appears, select the **Data** or **Display preferences** tabs to configure the default settings for the section. For details about each **Data** setting, refer to the preceding section, [Individual line plot]({{< relref "#line-plot-settings" >}}). For details about each display preference, refer to [Configure section layout]({{< relref "../#configure-section-layout" >}}).
-
-### All line plots in a workspace
-To customize the default settings for all line plots in a workspace:
-1. Click the workspace's settings, which has a gear with the label **Settings**.
-1. Click **Line plots**.
-1. Within the drawer that appears, select the **Data** or **Display preferences** tabs to configure the default settings for the workspace.
- - For details about each **Data** setting, refer to the preceding section, [Individual line plot]({{< relref "#line-plot-settings" >}}).
-
- - For details about each **Display preferences** section, refer to [Workspace display preferences]({{< relref "../#configure-workspace-layout" >}}). At the workspace level, you can configure the default **Zooming** behavior for line plots. This setting controls whether to synchronize zooming across line plots with a matching x-axis key. Disabled by default.
-
-
-
-## Visualize average values on a plot
-
-If you have several different experiments and you'd like to see the average of their values on a plot, you can use the Grouping feature in the table. Click "Group" above the run table and select "All" to show averaged values in your graphs.
-
-Here is what the graph looks like before averaging:
-
-{{< img src="/images/app_ui/demo_precision_lines.png" alt="Individual precision lines" >}}
-
-The proceeding image shows a graph that represents average values across runs using grouped lines.
-
-{{< img src="/images/app_ui/demo_average_precision_lines.png" alt="Averaged precision lines" >}}
-
-## Visualize NaN value on a plot
-
-You can also plot `NaN` values including PyTorch tensors on a line plot with `wandb.Run.log()`. For example:
-
-```python
-with wandb.init() as run:
- # Log a NaN value
- run.log({"test": float("nan")})
-```
-
-{{< img src="/images/app_ui/visualize_nan.png" alt="NaN value handling" >}}
-
-## Compare two metrics on one chart
-
-{{< img src="/images/app_ui/visualization_add.gif" alt="Adding visualization panels" >}}
-
-1. Select the **Add panels** button in the top right corner of the page.
-2. From the left panel that appears, expand the Evaluation dropdown.
-3. Select **Run comparer**
-
-
-## Change the color of the line plots
-
-Sometimes the default color of runs is not helpful for comparison. To help overcome this, wandb provides two instances with which one can manually change the colors.
-
-{{< tabpane text=true >}}
-{{% tab header="From the run table" value="run_table" %}}
-
- Each run is given a random color by default upon initialization.
-
- {{< img src="/images/app_ui/line_plots_run_table_random_colors.png" alt="Random colors given to runs" >}}
-
- Upon clicking any of the colors, a color palette appears from which we can manually choose the color we want.
-
- {{< img src="/images/app_ui/line_plots_run_table_color_palette.png" alt="The color palette" >}}
-
-{{% /tab %}}
-
-{{% tab header="From the chart legend settings" value="legend_settings" %}}
-
-1. Hover your mouse over the panel you want to edit its settings for.
-2. Select the pencil icon that appears.
-3. Choose the **Legend** tab.
-
-{{< img src="/images/app_ui/plot_style_line_plot_legend.png" alt="Line plot legend settings" >}}
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-## Visualize on different x axes
-
-If you'd like to see the absolute time that an experiment has taken, or see what day an experiment ran, you can switch the x axis. Here's an example of switching from steps to relative time and then to wall time.
-
-{{< img src="/images/app_ui/howto_use_relative_time_or_wall_time.gif" alt="X-axis time options" >}}
-
-## Area plots
-
-In the line plot settings, in the advanced tab, click on different plot styles to get an area plot or a percentage area plot.
-
-{{< img src="/images/app_ui/line_plots_area_plots.gif" alt="Area plot styles" >}}
-
-## Zoom
-
-Click and drag a rectangle to zoom vertically and horizontally at the same time. This changes the x-axis and y-axis zoom.
-
-{{< img src="/images/app_ui/line_plots_zoom.gif" alt="Plot zoom functionality" >}}
-
-## Hide chart legend
-
-Turn off the legend in the line plot with this simple toggle:
-
-{{< img src="/images/app_ui/demo_hide_legend.gif" alt="Hide legend toggle" >}}
-
-## Create a run metrics notification
-Use [Automations]({{< relref "/guides/core/automations" >}}) to notify your team when a run metric meets a condition you specify. An automation can post to a Slack channel or run a webhook.
-
-From a line plot, you can quickly create a [run metrics notification]({{< relref "/guides/core/automations/automation-events.md#run-events" >}}) for the metric it shows:
-
-1. Hover over the panel, then click the bell icon.
-1. Configure the automation using the basic or advanced configuration controls. For example, apply a run filter to limit the scope of the automation, or configure an absolute threshold.
-
-Learn more about [Automations]({{< relref "/guides/core/automations" >}}).
-
-## Visualize CoreWeave infrastructure alerts
-
-Observe infrastructure alerts such as GPU failures, thermal violations, and more during machine learning experiments you log to W&B. During a [W&B run]({{< relref "/guides/models/track/runs/_index" >}}), [CoreWeave Mission Control](https://www.coreweave.com/mission-control) monitors your compute infrastructure.
-
-{{< alert>}}
-This feature is in Preview and only available when training on a CoreWeave cluster. Contact your W&B representative for access.
-{{< /alert >}}
-
-If an error occurs, CoreWeave sends that information to W&B. W&B populates infrastructure information onto your run's plots in your project's workspace. CoreWeave attempts to automatically resolve some issues, and W&B surfaces that information in the run's page.
-
-### Find infrastructure issues in a run
-
-W&B surfaces both SLURM job issues and cluster node issues. View infrastructure errors in a run:
-
-1. Navigate to your project on the W&B App.
-2. Select the **Workspace** tab to view your project's workspace.
-3. Search and select the name of the run that contains an infrastructure issue. If CoreWeave detected an infrastructure issue, one or more red vertical lines with an exclamation mark overlay the run's plots.
-4. Select an issue on a plot or select the **Issues** button in the top right of the page. A drawer appears that lists each issue reported by CoreWeave.
-
-{{< alert title="Tip" >}}
-To views runs with infrastructure issues at a glance, pin the **Issues** column to your W&B Workspace to view runs that logged an issue at a glance. For more information about how to pin a column, see [Customize how runs are displayed]({{< relref "/guides/models/track/runs/#customize-how-runs-are-displayed" >}}).
-{{< /alert >}}
-
-The **Overall Grafana view** at the top of the drawer redirects you to the SLURM job's Grafana dashboard, which contains system-level details about the run. The **Issues summary** describes the root error that the SLURM job reported to CoreWeave Mission Control. The summary section also describes any attempts to automatically resolve the error made by CoreWeave.
-
-{{< img src="/images/app_ui/cw_wb_observability.png" >}}
-
-The **All Issues** list all issues that occurs during the run in chronological order, with the most recent issue at the top. The list contains the job issue and node issue alerts. Within each issue alert is the name of the issue, the timestamp when the issue occurred, a link to the Grafana dashboard for that issue, and a brief summary that describes the issue.
-
-The following table shows example alerts for each category of infrastructure issues:
-
-| Category | Example alerts |
-| -------- | ------------- |
-| Node Availability & Readiness | `KubeNodeNotReadyHGX`, `NodeExtendedDownTime` |
-| GPU/Accelerator Errors | `GPUFallenOffBusHGX`, `GPUFaultHGX`, `NodeTooFewGPUs` |
-| Hardware Errors | `HardwareErrorFatal`, `NodeRAIDMemberDegraded` |
-| Networking & DNS | `NodeDNSFailureHGX`, `NodeEthFlappingLegacyNonGPU` |
-| Power, Cooling, and Management | `NodeCPUHZThrottle`, `RedfishDown` |
-| DPU & NVSwitch | `DPUNcoreVersionBelowDesired`, `NVSwitchFaultHGX` |
-| Miscellaneous | `NodePCISpeedRootGBT`, `NodePCIWidthRootSMC` |
-
-For detailed information on error types, see the [SLURM Job Metrics on the CoreWeave Docs](https://docs.coreweave.com/docs/observability/managed-grafana/sunk/slurm-job-metrics#job-info-alerts#job-info-alerts).
-
-### Debug infrastructure issues
-
-Each run that you create in W&B corresponds to a single SLURM job in CoreWeave. You can view a failed job's [Grafana](https://grafana.com/) dashboard or discover more information about a single node. The link within the **Overview** section of the **Issues** drawer links to the SLURM job Grafana dashboard. Expand the **All Issues** dropdown to view both job and node issues and their respective Grafana dashboards.
-
-{{< alert title="Note" >}}
-The Grafana dashboard is only available for W&B users with a CoreWeave account. Contact W&B to configure Grafana with your W&B organization.
-{{< /alert >}}
-
-Depending on the issue, you may need to adjust the SLURM job configuration, investigate the node's status, restart the job, or take other actions as needed.
-
-For more information about CoreWeave SLURM jobs in Grafana, see Slurm/Job Metrics on the [CoreWeave Docs](https://docs.coreweave.com/docs/observability/managed-grafana/sunk/slurm-job-metrics#job-info-alerts). See [Job info: alerts](https://docs.coreweave.com/docs/observability/managed-grafana/sunk/slurm-job-metrics#job-info-alerts#job-info-alerts) for detailed information about job alerts.
diff --git a/content/en/guides/models/app/features/panels/line-plot/reference.md b/content/en/guides/models/app/features/panels/line-plot/reference.md
deleted file mode 100644
index 771898e032..0000000000
--- a/content/en/guides/models/app/features/panels/line-plot/reference.md
+++ /dev/null
@@ -1,97 +0,0 @@
----
-menu:
- default:
- identifier: reference
- parent: line-plot
-title: Line plot reference
-weight: 10
----
-
-## X-Axis
-
-{{< img src="/images/app_ui/reference_x_axis.png" alt="Selecting X-Axis" >}}
-
-You can set the x-axis of a line plot to any value that you have logged with W&B.log as long as it's always logged as a number.
-
-## Y-Axis variables
-
-You can set the y-axis variables to any value you have logged with wandb.log as long as you were logging numbers, arrays of numbers or a histogram of numbers. If you logged more than 1500 points for a variable, W&B samples down to 1500 points.
-
-{{% alert %}}
-You can change the color of your y axis lines by changing the color of the run in the runs table.
-{{% /alert %}}
-
-## X range and Y range
-
-You can change the maximum and minimum values of X and Y for the plot.
-
-X range default is from the smallest value of your x-axis to the largest.
-
-Y range default is from the smallest value of your metrics and zero to the largest value of your metrics.
-
-## Max runs/groups
-
-By default you will only plot 10 runs or groups of runs. The runs will be taken from the top of your runs table or run set, so if you sort your runs table or run set you can change the runs that are shown.
-
-{{% alert %}}
-A workspace is limited to displaying a maximum of 1000 runs, regardless of its configuration.
-{{% /alert %}}
-
-## Legend
-
-You can control the legend of your chart to show for any run any config value that you logged and meta data from the runs such as the created at time or the user who created the run.
-
-Example:
-
-`${run:displayName} - ${config:dropout}` will make the legend name for each run something like `royal-sweep - 0.5` where `royal-sweep` is the run name and `0.5` is the config parameter named `dropout`.
-
-You can set value inside`[[ ]]` to display point specific values in the crosshair when hovering over a chart. For example `\[\[ $x: $y ($original) ]]` would display something like "2: 3 (2.9)"
-
-Supported values inside `[[ ]]` are as follows:
-
-| Value | Meaning |
-| ------------ | ------------------------------------------ |
-| `${x}` | X value |
-| `${y}` | Y value (Including smoothing adjustment) |
-| `${original}` | Y value not including smoothing adjustment |
-| `${mean}` | Mean of grouped runs |
-| `${stddev}` | Standard Deviation of grouped runs |
-| `${min}` | Min of grouped runs |
-| `${max}` | Max of grouped runs |
-| `${percent}` | Percent of total (for stacked area charts) |
-
-## Grouping
-
-You can aggregate all of the runs by turning on grouping, or group over an individual variable. You can also turn on grouping by grouping inside the table and the groups will automatically populate into the graph.
-
-## Smoothing
-
-You can set the [smoothing coefficient]({{< relref "/support/kb-articles/formula_smoothing_algorithm.md" >}}) to be between 0 and 1 where 0 is no smoothing and 1 is maximum smoothing.
-
-
-## Ignore outliers
-
-Rescale the plot to exclude outliers from the default plot min and max scale. The setting's impact on the plot depends on the plot's sampling mode.
-
-- For plots that use [random sampling mode]({{< relref "sampling.md#random-sampling" >}}), when you enable **Ignore outliers**, only points from 5% to 95% are shown. When outliers are shown, they are not formatted differently from other points.
-- For plots that use [full fidelity mode]({{< relref "sampling.md#full-fidelity" >}}), all points are always shown, condensed down to the last value in each bucket. When **Ignore outliers** is enabled, the minimum and maximum bounds of each bucket are shaded. Otherwise, no area is shaded.
-
-## Expression
-
-Expression lets you plot values derived from metrics like 1-accuracy. It currently only works if you are plotting a single metric. You can do simple arithmetic expressions, +, -, \*, / and % as well as \*\* for powers.
-
-## Plot style
-
-Select a style for your line plot.
-
-**Line plot:**
-
-{{< img src="/images/app_ui/plot_style_line_plot.png" alt="Line plot style" >}}
-
-**Area plot:**
-
-{{< img src="/images/app_ui/plot_style_area_plot.png" alt="Area plot style" >}}
-
-**Percentage area plot:**
-
-{{< img src="/images/app_ui/plot_style_percentage_plot.png" alt="Percentage plot style" >}}
diff --git a/content/en/guides/models/app/features/panels/line-plot/sampling.md b/content/en/guides/models/app/features/panels/line-plot/sampling.md
deleted file mode 100644
index dc3776e73c..0000000000
--- a/content/en/guides/models/app/features/panels/line-plot/sampling.md
+++ /dev/null
@@ -1,135 +0,0 @@
----
-menu:
- default:
- identifier: sampling
- parent: line-plot
-title: Point aggregation
-weight: 20
----
-
-
-Use point aggregation methods within your line plots for improved data visualization accuracy and performance. There are two types of point aggregation modes: [full fidelity]({{< relref "#full-fidelity" >}}) and [random sampling]({{< relref "#random-sampling" >}}). W&B uses full fidelity mode by default.
-
-## Full fidelity
-
-When you use full fidelity mode, W&B breaks the x-axis into dynamic buckets based on the number of data points. It then calculates the minimum, maximum, and average values within each bucket while rendering a point aggregation for the line plot.
-
-There are three main advantages to using full fidelity mode for point aggregation:
-
-* Preserve extreme values and spikes: retain extreme values and spikes in your data
-* Configure how minimum and maximum points render: use the W&B App to interactively decide whether you want to show extreme (min/max) values as a shaded area.
-* Explore your data without losing data fidelity: W&B recalculates x-axis bucket sizes when you zoom into specific data points. This helps ensure that you can explore your data without losing accuracy. Caching is used to store previously computed aggregations to help reduce loading times which is particularly useful if you are navigating through large datasets.
-
-### Configure how minimum and maximum points render
-
-Show or hide minimum and maximum values with shaded areas around your line plots.
-
-The proceeding image shows a blue line plot. The light blue shaded area represents the minimum and maximum values for each bucket.
-
-{{< img src="/images/app_ui/shaded-areas.png" alt="Shaded confidence areas" >}}
-
-There are three ways to render minimum and maximum values in your line plots:
-
-* **Never**: The min/max values are not displayed as a shaded area. Only show the aggregated line across the x-axis bucket.
-* **On hover**: The shaded area for min/max values appears dynamically when you hover over the chart. This option keeps the view uncluttered while allowing you to inspect ranges interactively.
-* **Always**: The min/max shaded area is consistently displayed for every bucket in the chart, helping you visualize the full range of values at all times. This can introduce visual noise if there are many runs visualized in the chart.
-
-By default, the minimum and maximum values are not displayed as shaded areas. To view one of the shaded area options, follow these steps:
-
-{{< tabpane text=true >}}
-{{% tab header="All charts in a workspace" value="all_charts" %}}
-1. Navigate to your W&B project
-2. Select on the **Workspace** icon on the left tab
-3. Select the gear icon on the top right corner of the screen next to the left of the **Add panels** button.
-4. From the UI slider that appears, select **Line plots**
-5. Within the **Point aggregation** section, choose **On over** or **Always** from the **Show min/max values as a shaded area** dropdown menu.
-{{% /tab %}}
-
-{{% tab header="Individual chart in a workspace" value="single_chart"%}}
-1. Navigate to your W&B project
-2. Select on the **Workspace** icon on the left tab
-3. Select the line plot panel you want to enable full fidelity mode for
-4. Within the modal that appears, select **On hover** or **Always** from the **Show min/max values as a shaded area** dropdown menu.
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-### Explore your data without losing data fidelity
-
-Analyze specific regions of the dataset without missing critical points like extreme values or spikes. When you zoom in on a line plot, W&B adjusts the buckets sizes used to calculate the minimum, maximum, and average values within each bucket.
-
-
-{{< img src="/images/app_ui/zoom_in.gif" alt="Plot zoom functionality" >}}
-
-
-W&B divides the x-axis is dynamically into 1000 buckets by default. For each bucket, W&B calculates the following values:
-
-- **Minimum**: The lowest value in that bucket.
-- **Maximum**: The highest value in that bucket.
-- **Average**: The mean value of all points in that bucket.
-
-W&B plots values in buckets in a way that preserves full data representation and includes extreme values in every plot. When zoomed in to 1,000 points or fewer, full fidelity mode renders every data point without additional aggregation.
-
-
-To zoom in on a line plot, follow these steps:
-
-1. Navigate to your W&B project
-2. Select on the **Workspace** icon on the left tab
-3. Optionally add a line plot panel to your workspace or navigate to an existing line plot panel.
-4. Click and drag to select a specific region to zoom in on.
-
-{{% alert title="Line plot grouping and expressions" %}}
-When you use Line Plot Grouping, W&B applies the following based on the mode selected:
-
-- **Non-windowed sampling (grouping)**: Aligns points across runs on the x-axis. The average is taken if multiple points share the same x-value; otherwise, they appear as discrete points.
-- **Windowed sampling (grouping and expressions)**: Divides the x-axis either into 250 buckets or the number of points in the longest line (whichever is smaller). W&B takes an average of points within each bucket.
-- **Full fidelity (grouping and expressions)**: Similar to non-windowed sampling, but fetches up to 500 points per run to balance performance and detail.
-{{% /alert %}}
-
-
-## Random sampling
-
-Random sampling uses 1500 randomly sampled points to render line plots. Random sampling is useful for performance reasons when you have a large number of data points.
-
-{{% alert color="warning" %}}
-Random sampling samples non-deterministically. This means that random sampling sometimes excludes important outliers or spikes in the data and therefore reduces data accuracy.
-{{% /alert %}}
-
-
-### Enable random sampling
-By default, W&B uses full fidelity mode. To enable random sampling, follow these steps:
-
-{{< tabpane text=true >}}
-{{% tab header="All charts in a workspace" value="all_charts" %}}
-1. Navigate to your W&B project
-2. Select on the **Workspace** icon on the left tab
-3. Select the gear icon on the top right corner of the screen next to the left of the **Add panels** button.
-4. From the UI slider that appears, select **Line plots**
-5. Choose **Random sampling** from the **Point aggregation** section
-{{% /tab %}}
-
-{{% tab header="Individual chart in a workspace" value="single_chart"%}}
-1. Navigate to your W&B project
-2. Select on the **Workspace** icon on the left tab
-3. Select the line plot panel you want to enable random sampling for
-4. Within the modal that appears, select **Random sampling** from the **Point aggregation method** section
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-
-### Access non sampled data
-
-You can access the complete history of metrics logged during a run using the [W&B Run API]({{< relref "/ref/python/public-api/runs.md" >}}). The following example demonstrates how to retrieve and process the loss values from a specific run:
-
-
-```python
-# Initialize the W&B API
-run = api.run("l2k2/examples-numpy-boston/i0wt6xua")
-
-# Retrieve the history of the 'Loss' metric
-history = run.scan_history(keys=["Loss"])
-
-# Extract the loss values from the history
-losses = [row["Loss"] for row in history]
-```
diff --git a/content/en/guides/models/app/features/panels/line-plot/smoothing.md b/content/en/guides/models/app/features/panels/line-plot/smoothing.md
deleted file mode 100644
index 9d43b012ce..0000000000
--- a/content/en/guides/models/app/features/panels/line-plot/smoothing.md
+++ /dev/null
@@ -1,102 +0,0 @@
----
-description: In line plots, use smoothing to see trends in noisy data.
-menu:
- default:
- identifier: smoothing
- parent: line-plot
-title: Smooth line plots
-weight: 30
----
-
-W&B supports several types of smoothing:
-
-- [Time weighted exponential moving average (TWEMA) smoothing]({{< relref "#time-weighted-exponential-moving-average-twema-smoothing-default" >}})
-- [Gaussian smoothing]({{< relref "#gaussian-smoothing" >}})
-- [Running average]({{< relref "#running-average-smoothing" >}})
-- [Exponential moving average (EMA) smoothing]({{< relref "#exponential-moving-average-ema-smoothing" >}})
-
-See these live in an [interactive W&B report](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc).
-
-{{< img src="/images/app_ui/beamer_smoothing.gif" alt="Demo of various smoothing algorithms" >}}
-
-## Time Weighted Exponential Moving Average (TWEMA) smoothing (Default)
-
-The Time Weighted Exponential Moving Average (TWEMA) smoothing algorithm is a technique for smoothing time series data by exponentially decaying the weight of previous points. For details about the technique, see [Exponential Smoothing](https://www.wikiwand.com/en/Exponential_smoothing). The range is 0 to 1. There is a de-bias term added so that early values in the time series are not biased towards zero.
-
-The TWEMA algorithm takes the density of points on the line (the number of `y` values per unit of range on x-axis) into account. This allows consistent smoothing when displaying multiple lines with different characteristics simultaneously.
-
-Here is sample code for how this works under the hood:
-
-```javascript
-const smoothingWeight = Math.min(Math.sqrt(smoothingParam || 0), 0.999);
-let lastY = yValues.length > 0 ? 0 : NaN;
-let debiasWeight = 0;
-
-return yValues.map((yPoint, index) => {
- const prevX = index > 0 ? index - 1 : 0;
- // VIEWPORT_SCALE scales the result to the chart's x-axis range
- const changeInX =
- ((xValues[index] - xValues[prevX]) / rangeOfX) * VIEWPORT_SCALE;
- const smoothingWeightAdj = Math.pow(smoothingWeight, changeInX);
-
- lastY = lastY * smoothingWeightAdj + yPoint;
- debiasWeight = debiasWeight * smoothingWeightAdj + 1;
- return lastY / debiasWeight;
-});
-```
-
-Here's what this looks like [in the app](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc):
-
-{{< img src="/images/app_ui/weighted_exponential_moving_average.png" alt="Demo of TWEMA smoothing" >}}
-
-## Gaussian smoothing
-
-Gaussian smoothing (or Gaussian kernel smoothing) computes a weighted average of the points, where the weights correspond to a gaussian distribution with the standard deviation specified as the smoothing parameter. The smoothed value is calculated for every input x value, based on the points occurring both before and after it.
-
-Here's what this looks like [in the app](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc#3.-gaussian-smoothing):
-
-{{< img src="/images/app_ui/gaussian_smoothing.png" alt="Demo of gaussian smoothing" >}}
-
-## Running average smoothing
-
-Running average is a smoothing algorithm that replaces a point with the average of points in a window before and after the given x value. See ["Boxcar Filter" on Wikipedia](https://en.wikipedia.org/wiki/Moving_average). The selected parameter for running average tells Weights and Biases the number of points to consider in the moving average.
-
-Consider using Gaussian Smoothing instead if your points are spaced unevenly on the x-axis.
-
-Here's what this looks like [in the app](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc#4.-running-average):
-
-{{< img src="/images/app_ui/running_average.png" alt="Demo of running average smoothing" >}}
-
-## Exponential Moving Average (EMA) smoothing
-
-The Exponential Moving Average (EMA) smoothing algorithm is a rule of thumb technique for smoothing time series data using the exponential window function. For details about the technique, see [Exponential Smoothing](https://www.wikiwand.com/en/Exponential_smoothing). The range is 0 to 1. A debias term is added so that early values in the time series are not biases towards zero.
-
-In many situations, EMA smoothing is applied to a full scan of history, rather than bucketing first before smoothing. This often produces more accurate smoothing.
-
-In the following situations, EMA smoothing is after bucketing instead:
-- Sampling
-- Grouping
-- Expressions
-- Non-monotonic x-axes
-- Time-based x-axes
-
-Here is sample code for how this works under the hood:
-
-```javascript
- data.forEach(d => {
- const nextVal = d;
- last = last * smoothingWeight + (1 - smoothingWeight) * nextVal;
- numAccum++;
- debiasWeight = 1.0 - Math.pow(smoothingWeight, numAccum);
- smoothedData.push(last / debiasWeight);
-```
-
-Here's what this looks like [in the app](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc):
-
-{{< img src="/images/app_ui/exponential_moving_average.png" alt="Demo of EMA smoothing" >}}
-
-## Hide original data
-
-By default, the original unsmoothed data displays in the plot as a faint line in the background. Click **Show Original** to turn this off.
-
-{{< img src="/images/app_ui/demo_wandb_smoothing_turn_on_and_off_original_data.gif" alt="Turn on or off original data" >}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/media.md b/content/en/guides/models/app/features/panels/media.md
deleted file mode 100644
index fa7c426e26..0000000000
--- a/content/en/guides/models/app/features/panels/media.md
+++ /dev/null
@@ -1,95 +0,0 @@
----
-menu:
- default:
- identifier: media-panels
- parent: panels
-title: Media panels
-weight: 50
----
-
-A media panel visualizes [logged keys for media objects]({{< relref "/guides/models/track/log/media.md" >}}), including 3D objects, audio, images, video, or point clouds. This page shows how to add and manage media panels in a workspace.
-
-{{< img src="/images/app_ui/demo-media-panel.png" alt="Demo of a media panel" >}}
-
-## Add a media panel
-To add a media panel for a logged key using the default configuration, use Quick Add. You can add a media panel globally or to a specific section.
-
-1. **Global**: Click **Add panels** in the control bar near the panel search field.
-1. **Section**: Click the section's action `...` menu, then click **Add panels**.
-1. In the list of available panels, find the key for the panel, then click **Add**. Repeat this step for each media panel you want to add, then click the **X** at the top right to close the **Quick Add** list.
-1. Optionally, [configure the panel]({{< relref "#configure-a-media-panel" >}}).
-
-You can add a media panel globally or to a specific section:
-1. **Global**: Click **Add panels** in the control bar near the panel search field.
-1. **Section**: Click the section's action `...` menu, then click **Add panels**.
-1. Click the **Media** section to expand it.
-1. Select the type of media the panel visualizes, 3d objects, images, video, or audio. The panel configuration screen displays. Configure the panel, then click **Apply**. Refer to [Configure a media panel]({{< relref "#configure-a-media-panel" >}}).
-
-## Configure a media panel
-Panels for all media types have the same options.
-
-When you add a media panel manually, its configuration page opens after you select the type of media. To update the configuration for an existing panel, hover over the panel, then click the gear icon that appears at the top right. This section describes the settings available in each tab.
-
-### Overlays
-This tab appears for images and point clouds logged with segmentation masks or bounding boxes.
-- Search and filter overlays by name.
-- Customize overlay colors.
-
-### Display
-Customize the panel's overall appearance and behavior.
-- Configure the panel's title.
-- Select the media keys to visualize.
-- Customize the panel's slider and playback behavior.
- - Configure the slider key, which defaults to **Step**.
- - Set **Stride length** to the number of steps to advance for each click of the slider.
- - Turn on or off **Snap to existing step**. If it is turned on, the stepper advances to the next existing step after **Stride length**. Otherwise, it advances by **Stride length** even if that does not align with an existing step.
-- **Images**: Turn on or off smoothing.
-- **3d objects**: Configure the background color and point color.
-
-### Layout
-Customize the display of the panel's individual items.
-- Turn on or off **Grid mode**.
- - When it is turned on, you can choose a custom X and Y axis to plot on top of each item. More than one item displays in each row, and you limit how many rows to show.
- - When it is turned off, you can customize the number of columns to use for the panel's content, and you can configure the panel's content, which defaults to **Run**.
-- Optionally limit the **Max runs to include** in the panel.
-- Optionally specify a **Media display limit** to limit the number of media items to include per run.
-- **Images and videos**: Turn on or off display of full-size media.
-- **Images**: When **Fit media** is turned on, resize the panel's media to fit the panel's size.
-- **Point clouds**: Optionally turn on the right-handed system for plotting points, rather than the default left-handed system.
-
-### All media panels in a section
-To customize the default settings for all media panels in a section, overriding workspace settings for media panels:
-1. Click the section's gear icon to open its settings.
-1. Click **Media settings**.
-1. Within the drawer that appears, click the **Display**, **Layout**, or **Sync** tab to configure the default media settings for the section. You can configure settings for images, videos, audio, and 3d objects. The settings that appear depend on the section's current media panels.
-
-Refer to [Configure a media panel]({{< relref "#configure-a-media-panel" >}}) for details about a specific setting for **Display** or **Layout** media setting. The **Sync** tab is available only at the section or workspace level, not for individual media panels.
-
-When **Step slider syncing** is turned on, the section's media panels with the same step slider are kept in sync. To turn on step slider syncing:
-
- 1. Click the **Sync** tab.
- 1. Turn on **Sync slider by key (Step)**.
-
-### All media panels in a workspace
-To customize the default settings for all media panels in a workspace:
-1. Click the workspace's settings, which has a gear with the label **Settings**.
-1. Click **Media settings**.
-1. Within the drawer that appears, click the **Display** or **Layout** tab to configure the default media settings for the workspace. You can configure settings for images, videos, audio, and 3d objects. The settings that appear depend on the workspace's current media panels.
-
-With the exception of the **Sync** tab, refer to [Configure a media panel]({{< relref "#configure-a-media-panel" >}}) for details about a setting. The **Sync** tab is available only at the section or workspace level, not for individual media panels.
-
-When **Step slider syncing** is turned on, the section's media panels with the same step slider are kept in sync. To turn on step slider syncing:
-
-1. Click the **Sync** tab.
-1. Turn on **Sync slider by key (Step)**.
-
-For details about each setting, refer to [Configure a media panel]({{< relref "#configure-a-media-panel" >}}).
-
-## Interact with a media panel
-- Click a media panel to view it in full-screen mode. Click the arrow button at the top of the panel to exit full-screen mode.
-- To navigate through a section's panels without exiting full-screen mode, use either the **Previous** and **Next** buttons below the panel or the left and right arrow keys.
-- To move a media panel's step slider, use **CMD + left or right arrow key** (macOS) or **Ctrl + left or right arrow key** (Windows / Linux). If **Sync slider by key (Step)** is turned on for the section or workspace, moving the step slider in one media panel also moves the step slider in other media panels with the same step slider key.
-- Use the stepper at the top of a media panel to step through media runs. To move the step slider, use the UI controls or
-- To configure a media panel, hover over it and click the gear icon at the top.
-- For an image that was logged with segmentation masks, you can customize their appearance or turn each one on or off. Hover over the panel, then click the lower gear icon.
-- For an image or point cloud that was logged with bounding boxes, you can customize their appearance or turn each one on or off. Hover over the panel, then click the lower gear icon.
diff --git a/content/en/guides/models/app/features/panels/parallel-coordinates.md b/content/en/guides/models/app/features/panels/parallel-coordinates.md
deleted file mode 100644
index c8a2134846..0000000000
--- a/content/en/guides/models/app/features/panels/parallel-coordinates.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-description: Compare results across machine learning experiments
-menu:
- default:
- identifier: parallel-coordinates
- parent: panels
-title: Parallel coordinates
-weight: 30
----
-
-Parallel coordinates charts summarize the relationship between large numbers of hyperparameters and model metrics at a glance.
-
-{{< img src="/images/app_ui/parallel_coordinates.gif" alt="Parallel coordinates plot" >}}
-
-* **Axes**: Different hyperparameters from [`wandb.Run.config`]({{< relref "/guides/models/track/config.md" >}}) and metrics from [`wandb.Run.log()`]({{< relref "/guides/models/track/log/" >}}).
-* **Lines**: Each line represents a single run. Mouse over a line to see a tooltip with details about the run. All lines that match the current filters will be shown, but if you turn off the eye, lines will be grayed out.
-
-## Create a parallel coordinates panel
-
-1. Go to the landing page for your workspace
-2. Click **Add Panels**
-3. Select **Parallel coordinates**
-
-## Panel Settings
-
-To configure the panel, click the edit button in the upper right corner of the panel.
-
-* **Tooltip**: On hover, a legend shows up with info on each run
-* **Titles**: Edit the axis titles to be more readable
-* **Gradient**: Customize the gradient to be any color range you like
-* **Log scale**: Each axis can be set to view on a log scale independently
-* **Flip axis**: Switch the axis direction— this is useful when you have both accuracy and loss as columns
-
-[Interact with a live parallel coordinates panel](https://app.wandb.ai/example-team/sweep-demo/reports/Zoom-in-on-Parallel-Coordinates-Charts--Vmlldzo5MTQ4Nw)
diff --git a/content/en/guides/models/app/features/panels/parameter-importance.md b/content/en/guides/models/app/features/panels/parameter-importance.md
deleted file mode 100644
index 991af6a827..0000000000
--- a/content/en/guides/models/app/features/panels/parameter-importance.md
+++ /dev/null
@@ -1,74 +0,0 @@
----
-description: Visualize the relationships between your model's hyperparameters and
- output metrics
-menu:
- default:
- identifier: parameter-importance
- parent: panels
-title: Parameter importance
-weight: 60
----
-
-Discover which of your hyperparameters were the best predictors of, and highly correlated to desirable values of your metrics.
-
-
-{{< img src="/images/general/parameter-importance-1.png" alt="Parameter importance panel" >}}
-
-**Correlation** is the linear correlation between the hyperparameter and the chosen metric (in this case val_loss). So a high correlation means that when the hyperparameter has a higher value, the metric also has higher values and vice versa. Correlation is a great metric to look at but it can't capture second order interactions between inputs and it can get messy to compare inputs with wildly different ranges.
-
-Therefore W&B also calculates an **importance** metric. W&B trains a random forest with the hyperparameters as inputs and the metric as the target output and report the feature importance values for the random forest.
-
-The idea for this technique was inspired by a conversation with [Jeremy Howard](https://twitter.com/jeremyphoward) who has pioneered the use of random forest feature importances to explore hyperparameter spaces at [Fast.ai](https://fast.ai). W&B highly recommends you check out this [lecture](https://course18.fast.ai/lessonsml1/lesson4.html) (and these [notes](https://forums.fast.ai/t/wiki-lesson-thread-lesson-4/7540)) to learn more about the motivation behind this analysis.
-
-Hyperparameter importance panel untangles the complicated interactions between highly correlated hyperparameters. In doing so, it helps you fine tune your hyperparameter searches by showing you which of your hyperparameters matter the most in terms of predicting model performance.
-
-## Creating a hyperparameter importance panel
-
-1. Navigate to your W&B project.
-2. Select **Add panels** button.
-3. Expand the **CHARTS** dropdown, choose **Parallel coordinates** from the dropdown.
-
-
-{{% alert %}}
-If an empty panel appears, make sure that your runs are ungrouped
-{{% /alert %}}
-
-
-{{< img src="/images/app_ui/hyperparameter_importance_panel.gif" alt="Automatic parameter visualization" >}}
-
-With the parameter manager, we can manually set the visible and hidden parameters.
-
-{{< img src="/images/app_ui/hyperparameter_importance_panel_manual.gif" alt="Manually setting the visible and hidden fields" >}}
-
-## Interpreting a hyperparameter importance panel
-
-{{< img src="/images/general/parameter-importance-4.png" alt="Feature importance analysis" >}}
-
-This panel shows you all the parameters passed to the [wandb.Run.config]({{< relref "/guides/models/track/config/" >}}) object in your training script. Next, it shows the feature importances and correlations of these config parameters with respect to the model metric you select (`val_loss` in this case).
-
-### Importance
-
-The importance column shows you the degree to which each hyperparameter was useful in predicting the chosen metric. Imagine a scenario were you start tuning a plethora of hyperparameters and using this plot to hone in on which ones merit further exploration. The subsequent sweeps can then be limited to the most important hyperparameters, thereby finding a better model faster and cheaper.
-
-{{% alert %}}
-W&B calculate importances using a tree based model rather than a linear model as the former are more tolerant of both categorical data and data that's not normalized.
-{{% /alert %}}
-
-In the preceding image, you can see that `epochs, learning_rate, batch_size` and `weight_decay` were fairly important.
-
-### Correlations
-
-Correlations capture linear relationships between individual hyperparameters and metric values. They answer the question of whether there a significant relationship between using a hyperparameter, such as the SGD optimizer, and the `val_loss` (the answer in this case is yes). Correlation values range from -1 to 1, where positive values represent positive linear correlation, negative values represent negative linear correlation and a value of 0 represents no correlation. Generally a value greater than 0.7 in either direction represents strong correlation.
-
-You might use this graph to further explore the values that are have a higher correlation to our metric (in this case you might pick stochastic gradient descent or adam over rmsprop or nadam) or train for more epochs.
-
-
-{{% alert %}}
-* correlations show evidence of association, not necessarily causation.
-* correlations are sensitive to outliers, which might turn a strong relationship to a moderate one, specially if the sample size of hyperparameters tried is small.
-* and finally, correlations only capture linear relationships between hyperparameters and metrics. If there is a strong polynomial relationship, it won't be captured by correlations.
-{{% /alert %}}
-
-The disparities between importance and correlations result from the fact that importance accounts for interactions between hyperparameters, whereas correlation only measures the affects of individual hyperparameters on metric values. Secondly, correlations capture only the linear relationships, whereas importances can capture more complex ones.
-
-As you can see both importance and correlations are powerful tools for understanding how your hyperparameters influence model performance.
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/query-panels/_index.md b/content/en/guides/models/app/features/panels/query-panels/_index.md
deleted file mode 100644
index e69253cc2f..0000000000
--- a/content/en/guides/models/app/features/panels/query-panels/_index.md
+++ /dev/null
@@ -1,164 +0,0 @@
----
-description: Some features on this page are in beta, hidden behind a feature flag.
- Add `weave-plot` to your bio on your profile page to unlock all related features.
-url: guides/app/features/panels/query-panels
-menu:
- default:
- identifier: intro_query_panel
- parent: panels
-cascade:
-- url: guides/app/features/panels/query-panels/:filename
-title: Query panels
----
-
-
-{{% alert %}}
-Looking for W&B Weave? W&B's suite of tools for Generative AI application building? Find the docs for weave here: [wandb.me/weave](https://wandb.github.io/weave/?utm_source=wandb_docs&utm_medium=docs&utm_campaign=weave-nudge).
-{{% /alert %}}
-
-Use query panels to query and interactively visualize your data.
-
-{{< img src="/images/weave/pretty_panel.png" alt="Query panel" >}}
-
-
-
-## Create a query panel
-
-Add a query to your workspace or within a report.
-
-{{< tabpane text=true >}}
-{{% tab header="Project workspace" value="workspace" %}}
-
- 1. Navigate to your project's workspace.
- 2. In the upper right hand corner, click `Add panel`.
- 3. From the dropdown, select `Query panel`.
- {{< img src="/images/weave/add_weave_panel_workspace.png" alt="Add panel dropdown" >}}
-
-{{% /tab %}}
-
-{{% tab header="W&B Report" value="report" %}}
-
-Type and select `/Query panel`.
-
-{{< img src="/images/weave/add_weave_panel_report_1.png" alt="Query panel option" >}}
-
-Alternatively, you can associate a query with a set of runs:
-1. Within your report, type and select `/Panel grid`.
-2. Click the `Add panel` button.
-3. From the dropdown, select `Query panel`.
-
-{{% /tab %}}
-{{< /tabpane >}}
-
-
-## Query components
-
-### Expressions
-
-Use query expressions to query your data stored in W&B such as runs, artifacts, models, tables, and more.
-
-#### Example: Query a table
-Suppose you want to query a W&B Table. In your training code you log a table called `"cifar10_sample_table"`:
-
-```python
-import wandb
-with wandb.init() as run:
- run.log({"cifar10_sample_table":})
-```
-
-Within the query panel you can query your table with:
-```python
-runs.summary["cifar10_sample_table"]
-```
-{{< img src="/images/weave/basic_weave_expression.png" alt="Table query expression" >}}
-
-Breaking this down:
-
-* `runs` is a variable automatically injected in Query Panel Expressions when the Query Panel is in a Workspace. Its "value" is the list of runs which are visible for that particular Workspace. [Read about the different attributes available within a run here]({{< relref "../../../../track/public-api-guide.md#understanding-the-different-attributes" >}}).
-* `summary` is an op which returns the Summary object for a Run. Ops are _mapped_, meaning this op is applied to each Run in the list, resulting in a list of Summary objects.
-* `["cifar10_sample_table"]` is a Pick op (denoted with brackets), with a parameter of `predictions`. Since Summary objects act like dictionaries or maps, this operation picks the `predictions` field off of each Summary object.
-
-To learn how to write your own queries interactively, see the [Query panel demo](https://wandb.ai/luis_team_test/weave_example_queries/reports/Weave-queries---Vmlldzo1NzIxOTY2?accessToken=bvzq5hwooare9zy790yfl3oitutbvno2i6c2s81gk91750m53m2hdclj0jvryhcr).
-
-### Configurations
-
-Select the gear icon on the upper left corner of the panel to expand the query configuration. This allows the user to configure the type of panel and the parameters for the result panel.
-
-{{< img src="/images/weave/weave_panel_config.png" alt="Panel configuration menu" >}}
-
-### Result panels
-
-Finally, the query result panel renders the result of the query expression, using the selected query panel, configured by the configuration to display the data in an interactive form. The following images shows a Table and a Plot of the same data.
-
-{{< img src="/images/weave/result_panel_table.png" alt="Table result panel" >}}
-
-{{< img src="/images/weave/result_panel_plot.png" alt="Plot result panel" >}}
-
-## Basic operations
-The following common operations you can make within your query panels.
-### Sort
-Sort from the column options:
-{{< img src="/images/weave/weave_sort.png" alt="Column sort options" >}}
-
-### Filter
-You can either filter directly in the query or using the filter button in the top left corner (second image)
-{{< img src="/images/weave/weave_filter_1.png" alt="Query filter syntax" >}}
-{{< img src="/images/weave/weave_filter_2.png" alt="Filter button" >}}
-
-### Map
-Map operations iterate over lists and apply a function to each element in the data. You can do this directly with a panel query or by inserting a new column from the column options.
-{{< img src="/images/weave/weave_map.png" alt="Map operation query" >}}
-{{< img src="/images/weave/weave_map.gif" alt="Map column insertion" >}}
-
-### Groupby
-You can groupby using a query or from the column options.
-{{< img src="/images/weave/weave_groupby.png" alt="Group by query" >}}
-{{< img src="/images/weave/weave_groupby.gif" alt="Group by column options" >}}
-
-### Concat
-The concat operation allows you to concatenate 2 tables and concatenate or join from the panel settings
-{{< img src="/images/weave/weave_concat.gif" alt="Table concatenation" >}}
-
-### Join
-It is also possible to join tables directly in the query. Consider the following query expression:
-```python
-project("luis_team_test", "weave_example_queries").runs.summary["short_table_0"].table.rows.concat.join(\
-project("luis_team_test", "weave_example_queries").runs.summary["short_table_1"].table.rows.concat,\
-(row) => row["Label"],(row) => row["Label"], "Table1", "Table2",\
-"false", "false")
-```
-{{< img src="/images/weave/weave_join.png" alt="Table join operation" >}}
-
-The table on the left is generated from:
-```python
-project("luis_team_test", "weave_example_queries").\
-runs.summary["short_table_0"].table.rows.concat.join
-```
-The table in the right is generated from:
-```python
-project("luis_team_test", "weave_example_queries").\
-runs.summary["short_table_1"].table.rows.concat
-```
-Where:
-* `(row) => row["Label"]` are selectors for each table, determining which column to join on
-* `"Table1"` and `"Table2"` are the names of each table when joined
-* `true` and `false` are for left and right inner/outer join settings
-
-
-## Runs object
-Use query panels to access the `runs` object. Run objects store records of your experiments. You can find more details in [Accessing runs object](https://wandb.ai/luis_team_test/weave_example_queries/reports/Weave-queries---Vmlldzo1NzIxOTY2?accessToken=bvzq5hwooare9zy790yfl3oitutbvno2i6c2s81gk91750m53m2hdclj0jvryhcr#3.-accessing-runs-object) but, as quick overview, `runs` object has available:
-* `summary`: A dictionary of information that summarizes the run's results. This can be scalars like accuracy and loss, or large files. By default, `wandb.Run.log()` sets the summary to the final value of a logged time series. You can set the contents of the summary directly. Think of the summary as the run's outputs.
-* `history`: A list of dictionaries meant to store values that change while the model is training such as loss. The command `wandb.Run.log()` appends to this object.
-* `config`: A dictionary of the run's configuration information, such as the hyperparameters for a training run or the preprocessing methods for a run that creates a dataset Artifact. Think of these as the run's "inputs"
-{{< img src="/images/weave/weave_runs_object.png" alt="Runs object structure" >}}
-
-## Access Artifacts
-
-Artifacts are a core concept in W&B. They are a versioned, named collection of files and directories. Use Artifacts to track model weights, datasets, and any other file or directory. Artifacts are stored in W&B and can be downloaded or used in other runs. You can find more details and examples in [Accessing artifacts](https://wandb.ai/luis_team_test/weave_example_queries/reports/Weave-queries---Vmlldzo1NzIxOTY2?accessToken=bvzq5hwooare9zy790yfl3oitutbvno2i6c2s81gk91750m53m2hdclj0jvryhcr#4.-accessing-artifacts). Artifacts are normally accessed from the `project` object:
-* `project.artifactVersion()`: returns the specific artifact version for a given name and version within a project
-* `project.artifact("")`: returns the artifact for a given name within a project. You can then use `.versions` to get a list of all versions of this artifact
-* `project.artifactType()`: returns the `artifactType` for a given name within a project. You can then use `.artifacts` to get a list of all artifacts with this type
-* `project.artifactTypes`: returns a list of all artifact types under the project
-{{< img src="/images/weave/weave_artifacts.png" alt="Artifact access methods" >}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/query-panels/embedding-projector.md b/content/en/guides/models/app/features/panels/query-panels/embedding-projector.md
deleted file mode 100644
index 8f86c2f0f9..0000000000
--- a/content/en/guides/models/app/features/panels/query-panels/embedding-projector.md
+++ /dev/null
@@ -1,104 +0,0 @@
----
-description: W&B's Embedding Projector allows users to plot multi-dimensional embeddings
- on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE.
-menu:
- default:
- identifier: embedding-projector
- parent: query-panels
-title: Embed objects
----
-
-{{< img src="/images/weave/embedding_projector.png" alt="Embedding projector" >}}
-
-[Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture) are used to represent objects (people, images, posts, words, etc...) with a list of numbers - sometimes referred to as a _vector_. In machine learning and data science use cases, embeddings can be generated using a variety of approaches across a range of applications. This page assumes the reader is familiar with embeddings and is interested in visually analyzing them inside of W&B.
-
-## Embedding Examples
-
-- [Live Interactive Demo Report](https://wandb.ai/timssweeney/toy_datasets/reports/Feature-Report-W-B-Embeddings-Projector--VmlldzoxMjg2MjY4?accessToken=bo36zrgl0gref1th5nj59nrft9rc4r71s53zr2qvqlz68jwn8d8yyjdz73cqfyhq)
-- [Example Colab](https://colab.research.google.com/drive/1DaKL4lZVh3ETyYEM1oJ46ffjpGs8glXA#scrollTo=D--9i6-gXBm_).
-
-### Hello World
-
-W&B allows you to log embeddings using the `wandb.Table` class. Consider the following example of 3 embeddings, each consisting of 5 dimensions:
-
-```python
-import wandb
-
-with wandb.init(project="embedding_tutorial") as run:
- embeddings = [
- # D1 D2 D3 D4 D5
- [0.2, 0.4, 0.1, 0.7, 0.5], # embedding 1
- [0.3, 0.1, 0.9, 0.2, 0.7], # embedding 2
- [0.4, 0.5, 0.2, 0.2, 0.1], # embedding 3
- ]
- run.log(
- {"embeddings": wandb.Table(columns=["D1", "D2", "D3", "D4", "D5"], data=embeddings)}
- )
- run.finish()
-```
-
-After running the above code, the W&B dashboard will have a new Table containing your data. You can select `2D Projection` from the upper right panel selector to plot the embeddings in 2 dimensions. Smart default will be automatically selected, which can be easily overridden in the configuration menu accessed by clicking the gear icon. In this example, we automatically use all 5 available numeric dimensions.
-
-{{< img src="/images/app_ui/weave_hello_world.png" alt="2D projection example" >}}
-
-### Digits MNIST
-
-While the above example shows the basic mechanics of logging embeddings, typically you are working with many more dimensions and samples. Let's consider the MNIST Digits dataset ([UCI ML hand-written digits dataset](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)[s](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)) made available via [SciKit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html). This dataset has 1797 records, each with 64 dimensions. The problem is a 10 class classification use case. We can convert the input data to an image for visualization as well.
-
-```python
-import wandb
-from sklearn.datasets import load_digits
-
-with wandb.init(project="embedding_tutorial") as run:
-
- # Load the dataset
- ds = load_digits(as_frame=True)
- df = ds.data
-
- # Create a "target" column
- df["target"] = ds.target.astype(str)
- cols = df.columns.tolist()
- df = df[cols[-1:] + cols[:-1]]
-
- # Create an "image" column
- df["image"] = df.apply(
- lambda row: wandb.Image(row[1:].values.reshape(8, 8) / 16.0), axis=1
- )
- cols = df.columns.tolist()
- df = df[cols[-1:] + cols[:-1]]
-
- run.log({"digits": df})
-```
-
-After running the above code, again we are presented with a Table in the UI. By selecting `2D Projection` we can configure the definition of the embedding, coloring, algorithm (PCA, UMAP, t-SNE), algorithm parameters, and even overlay (in this case we show the image when hovering over a point). In this particular case, these are all "smart defaults" and you should see something very similar with a single click on `2D Projection`. ([Interact with this embedding tutorial example](https://wandb.ai/timssweeney/embedding_tutorial/runs/k6guxhum?workspace=user-timssweeney)).
-
-{{< img src="/images/weave/embedding_projector.png" alt="MNIST digits projection" >}}
-
-## Logging Options
-
-You can log embeddings in a number of different formats:
-
-1. **Single Embedding Column:** Often your data is already in a "matrix"-like format. In this case, you can create a single embedding column - where the data type of the cell values can be `list[int]`, `list[float]`, or `np.ndarray`.
-2. **Multiple Numeric Columns:** In the above two examples, we use this approach and create a column for each dimension. We currently accept python `int` or `float` for the cells.
-
-{{< img src="/images/weave/logging_options.png" alt="Single embedding column" >}}
-{{< img src="/images/weave/logging_option_image_right.png" alt="Multiple numeric columns" >}}
-
-Furthermore, just like all tables, you have many options regarding how to construct the table:
-
-1. Directly from a **dataframe** using `wandb.Table(dataframe=df)`
-2. Directly from a **list of data** using `wandb.Table(data=[...], columns=[...])`
-3. Build the table **incrementally row-by-row** (great if you have a loop in your code). Add rows to your table using `table.add_data(...)`
-4. Add an **embedding column** to your table (great if you have a list of predictions in the form of embeddings): `table.add_col("col_name", ...)`
-5. Add a **computed column** (great if you have a function or model you want to map over your table): `table.add_computed_columns(lambda row, ndx: {"embedding": model.predict(row)})`
-
-## Plotting Options
-
-After selecting `2D Projection`, you can click the gear icon to edit the rendering settings. In addition to selecting the intended columns (see above), you can select an algorithm of interest (along with the desired parameters). Below you can see the parameters for UMAP and t-SNE respectively.
-
-{{< img src="/images/weave/plotting_options_left.png" alt="UMAP parameters" >}}
-{{< img src="/images/weave/plotting_options_right.png" alt="t-SNE parameters" >}}
-
-{{% alert %}}
-Note: we currently downsample to a random subset of 1000 rows and 50 dimensions for all three algorithms.
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/models/app/features/panels/run-comparer.md b/content/en/guides/models/app/features/panels/run-comparer.md
deleted file mode 100644
index 6c0ba00ada..0000000000
--- a/content/en/guides/models/app/features/panels/run-comparer.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-description: Compare metrics across multiple runs
-menu:
- default:
- identifier: run-comparer
- parent: panels
-title: Compare run metrics
-weight: 70
----
-
-Use the Run Comparer to see differences and similarities across runs in your project.
-
-## Add a Run Comparer panel
-
-1. Select the **Add panels** button in the top right corner of the page.
-1. From the **Evaluation** section, select **Run comparer**.
-
-## Use Run Comparer
-Run Comparer shows the configuration and logged metrics for the 10 first visible runs in the project, one column per run.
-
-- To change the runs to compare, you can search, filter, group, or sort the list of runs on the left-hand side. The Run Comparer updates automatically.
-- Use the search field at the top of the Run Comparer to filter or search for a configuration key or a metadata key such as the Python version or the run's creation time.
-- To quickly see differences and hide identical values, toggle **Diff only** at the top of the panel.
-- To adjust the column width or row height, use the formatting buttons at the top of the panel.
-- To copy any configuration or metric's value, hover your mouse over the value, then click the copy button. The entire value is copied, even if it is too long to display on the screen.
-
-{{% alert %}}
-By default, Run Comparer does not differentiate runs with different values for [`job_type`]({{< relref "/ref/python/functions/init.md" >}}). This means that it is possible to compare runs that are not comparable within a project. For example, you could compare a training run to a model evaluation run. A training run could contain run logs, hyperparameters, training loss metrics, and the model itself. An evaluation run could use the model to check the model's performance on new training data.
-
-When you search, filter, group, or sort the list of runs in the Runs Table, the Run Comparer automatically updates to compare the first 10 runs. Filter or search within the Runs Table to compare similar runs, such as by filtering or sorting the list by `job_type`. Learn more about [filtering runs]({{< relref "/guides/models/track/runs/filter-runs.md" >}}).
-{{% /alert %}}
diff --git a/content/en/guides/models/app/features/panels/scatter-plot.md b/content/en/guides/models/app/features/panels/scatter-plot.md
deleted file mode 100644
index b5848585e8..0000000000
--- a/content/en/guides/models/app/features/panels/scatter-plot.md
+++ /dev/null
@@ -1,40 +0,0 @@
----
-menu:
- default:
- identifier: scatter-plot
- parent: panels
-title: Scatter plots
-weight: 40
----
-
-This page shows how to use scatter plots in W&B.
-
-## Use case
-
-Use scatter plots to compare multiple runs and visualize the performance of an experiment:
-
-- Plot lines for minimum, maximum, and average values.
-- Customize metadata tooltips.
-- Control point colors.
-- Adjust axis ranges.
-- Use a log scale for the axes.
-
-## Example
-
-The following example shows a scatter plot displaying validation accuracy for different models over several weeks of experimentation. The tooltip includes batch size, dropout, and axis values. A line also shows the running average of validation accuracy.
-
-[See a live example →](https://app.wandb.ai/l2k2/l2k/reports?view=carey%2FScatter%20Plot)
-
-{{< img src="/images/general/scatter-plots-1.png" alt="Validation accuracy scatter plot" >}}
-
-## Create a scatter plot
-
-To create a scatter plot in the W&B UI:
-
-1. Navigate to the **Workspaces** tab.
-2. In the **Charts** panel, click the action menu `...`.
-3. From the pop-up menu, select **Add panels**.
-4. In the **Add panels** menu, select **Scatter plot**.
-5. Set the `x` and `y` axes to plot the data you want to view. Optionally, set maximum and minimum ranges for your axes or add a `z` axis.
-6. Click **Apply** to create the scatter plot.
-7. View the new scatter plot in the Charts panel.
diff --git a/content/en/guides/models/app/keyboard-shortcuts.md b/content/en/guides/models/app/keyboard-shortcuts.md
deleted file mode 100644
index 8f75e9ac25..0000000000
--- a/content/en/guides/models/app/keyboard-shortcuts.md
+++ /dev/null
@@ -1,50 +0,0 @@
----
-description: Learn about the keyboard shortcuts available in the W&B App.
-menu:
- default:
- identifier: keyboard-shortcuts
- parent: app
-title: Keyboard shortcuts
-weight: 1
----
-
-The W&B App supports keyboard shortcuts to help you navigate and interact with experiments, workspaces, and data more efficiently. This reference guide covers all available keyboard shortcuts organized by functional area.
-
-## Workspace Management
-
-| Shortcut | Description |
-|----------|-------------|
-| **Cmd+Z** (macOS) / **Ctrl+Z** (Windows/Linux) | Undo a change you've made in the UI, such as a modification to the workspace or a panel. |
-| **Cmd+Shift+Z** (macOS) / **Ctrl+Y** (Windows/Linux) | Redo a change you previously undid in the workspace. |
-
-## Navigation
-
-| Shortcut | Description |
-|----------|-------------|
-| **Tab** | Navigate between interactive elements. |
-| **Cmd+J** (macOS) / **Ctrl+J** (Windows/Linux) | Switch between the Workspaces and Runs tabs in the sidebar. |
-| **Cmd+K** (macOS) / **Ctrl+K** (Windows/Linux) | Open the quick search dialog to search across projects, runs, and other resources. |
-| **Esc** | Throughout the W&B App, exit full-screen panel views, close settings drawers, dismiss the quick search dialog, close an editor, or dismiss other overlays. |
-
-## Panel Navigation
-
-| Shortcut | Description |
-|----------|-------------|
-| **Left Arrow / Right Arrow** | Step through panels in a section when in full screen mode. |
-| **Esc** | Exit full-screen panel view and return to the workspace. |
-| **Cmd+Left Arrow / Cmd+Right Arrow** (macOS) **Ctrl+Left Arrow / Ctrl+Right Arrow** (Windows/Linux) | When viewing a media panel in full screen mode,move the step slider. |
-
-## Reports
-
-| Shortcut | Description |
-|----------|-------------|
-| **Delete / Backspace** | Remove the selected panel grid from the report. |
-| **Enter** | Insert a Markdown block after typing "/mark" in a report. |
-| **Esc** | Exit the report editor. |
-| **Tab** | Navigate between interactive elements in a report. |
-
-## Notes
-
-- Most keyboard shortcuts use **Cmd** on macOS and **Ctrl** on Windows/Linux
-- The W&B App implements custom handling for some browser default shortcuts.
-- Some shortcuts are context-sensitive and only work in specific areas of the application
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/_index.md b/content/en/guides/models/app/settings-page/_index.md
deleted file mode 100644
index e19c534e21..0000000000
--- a/content/en/guides/models/app/settings-page/_index.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-description: Use the Weights and Biases Settings Page to customize your individual
- user profile or team settings.
-menu:
- default:
- identifier: settings
- parent: w-b-app-ui-reference
-title: Settings
-weight: 4
-cascade:
-- url: guides/app/settings-page/:filename
----
-
-Within your individual user account you can edit: your profile picture, display name, geography location, biography information, emails associated to your account, and manage alerts for runs. You can also use the settings page to link your GitHub repository and delete your account. For more information, see [User settings]({{< relref "./user-settings.md" >}}).
-
-Use the team settings page to invite or remove new members to a team, manage alerts for team runs, change privacy settings, and view and manage storage usage. For more information about team settings, see [Team settings]({{< relref "./teams.md" >}}).
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/anon.md b/content/en/guides/models/app/settings-page/anon.md
deleted file mode 100644
index 777d1c33ab..0000000000
--- a/content/en/guides/models/app/settings-page/anon.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-description: Log and visualize data without a W&B account
-menu:
- default:
- identifier: anon
- parent: settings
-title: Anonymous mode
-weight: 80
----
-
-Are you publishing code that you want anyone to be able to run easily? Use anonymous mode to let someone run your code, see a W&B dashboard, and visualize results without needing to create a W&B account first.
-
-Allow results to be logged in anonymous mode with:
-
-```python
-import wandb
-
-wandb.init(anonymous="allow")
-```
-
-For example, the proceeding code snippet shows how to create and log an artifact with W&B:
-
-```python
-import wandb
-
-run = wandb.init(anonymous="allow")
-
-artifact = wandb.Artifact(name="art1", type="foo")
-artifact.add_file(local_path="path/to/file")
-run.log_artifact(artifact)
-
-run.finish()
-```
-
-[Try the example notebook](https://colab.research.google.com/drive/1nQ3n8GD6pO-ySdLlQXgbz4wA3yXoSI7i) to see how anonymous mode works.
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/billing-settings.md b/content/en/guides/models/app/settings-page/billing-settings.md
deleted file mode 100644
index aee64aee19..0000000000
--- a/content/en/guides/models/app/settings-page/billing-settings.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-description: Manage your organization's billing settings
-menu:
- default:
- parent: settings
-title: Manage billing settings
-weight: 20
----
-
-Navigate to your user profile page and select your user icon on the top right corner. From the dropdown, choose **Billing**, or choose **Settings** and then select the **Billing** tab.
-
-## Plan details
-
-The **Plan details** section summarizes your organization's current plan, charges, limits, and usage.
-
-- For details and a list of users, click **Manage users**.
-- For details about usage, click **View usage**.
-- Amount of storage your organization uses, both free and paid. From here, you can purchase additional storage and manage storage that is currently in use. Learn more about [storage settings]({{< relref "storage.md" >}}).
-
-From here, you can compare plans or talk to Sales.
-
-## Plan usage
-This section visually summarizes current usage and displays upcoming usage charges. For detailed insights into usage by month, click **View usage** on an individual tile. To export usage by calendar month, team, or project, click **Export CSV**.
-
-### Usage alerts
-
-{{% alert %}}
-Usage alerts are not available on the [Enterprise plan](https://wandb.ai/site/pricing/).
-{{% /alert %}}
-
-For organizations on paid plans, admins receive alerts via email **once per billing period** when certain thresholds are met, along with details about how to increase your organization's limits if you are a [billing admin]({{< relref "#billing-admin" >}}) and how to contact a billing admin otherwise. On the [Pro plan](https://wandb.ai/site/pricing/), only the billing admin receives usage alerts.
-
-These alerts are not configurable, and are sent when:
-
-- Your organization is approaching a monthly limit of a category of usage (85% of hours used) and when it reaches 100% of the limit, according to your plan.
-- Your organization's accumulated average charges for a billing period exceed these thresholds: $200, $450, $700, and $1000. These overage charges are incurred when your organization accumulates more usage than your plan includes for tracked hours, storage, or W&B Weave data ingestion.
-
-For questions about usage or billing, contact your account team or Support.
-
-## Payment methods
-This section shows the payment methods on file for your organization. If you have not added a payment method, you will be prompted to do so when you upgrade your plan or add paid storage.
-
-## Billing admin
-This section shows the current billing admin. The billing admin is an organization admin, receives all billing-related emails, and can view and manage payment methods.
-
-{{% alert %}}
-In W&B Dedicated Cloud, multiple users can be billing admins. In W&B Multi-tenant Cloud, only one user at a time can be the billing admin.
-{{% /alert %}}
-
-To change the billing admin or assign the role to additional users:
-
-1. Click **Manage roles**.
-1. Search for a user.
-1. Click the **Billing admin** field in that user's row.
-1. Read the summary, then click **Change billing user**.
-
-## Invoices
-If you pay using a credit card, this section allows you to view monthly invoices.
-- For Enterprise accounts that pay via wire transfer, this section is blank. For questions, contact your account team.
-- If your organization incurs no charges, no invoice is generated.
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/emails.md b/content/en/guides/models/app/settings-page/emails.md
deleted file mode 100644
index 997e90503a..0000000000
--- a/content/en/guides/models/app/settings-page/emails.md
+++ /dev/null
@@ -1,48 +0,0 @@
----
-description: Manage emails from the Settings page.
-menu:
- default:
- identifier: emails
- parent: settings
-title: Manage email settings
-weight: 40
----
-
-
-Add, delete, manage email types and primary email addresses in your W&B Profile Settings page. Select your profile icon in the upper right corner of the W&B dashboard. From the dropdown, select **Settings**. Within the Settings page, scroll down to the Emails dashboard:
-
-{{< img src="/images/app_ui/manage_emails.png" alt="Email management dashboard" >}}
-
-## Manage primary email
-
-The primary email is marked with a 😎 emoji. The primary email is automatically defined with the email you provided when you created a W&B account.
-
-Select the kebab dropdown to change the primary email associated with your Weights And Biases account:
-
-{{% alert %}}
-Only verified emails can be set as primary
-{{% /alert %}}
-
-{{< img src="/images/app_ui/primary_email.png" alt="Primary email dropdown" >}}
-
-## Add emails
-
-Select **+ Add Email** to add an email. This will take you to an Auth0 page. You can enter in the credentials for the new email or connect using single sign-on (SSO).
-
-## Delete emails
-
-Select the kebab dropdown and choose **Delete Emails** to delete an email that is registered to your W&B account
-
-{{% alert %}}
-Primary emails cannot be deleted. You need to set a different email as a primary email before deleting.
-{{% /alert %}}
-
-## Log in methods
-
-The Log in Methods column displays the log in methods that are associated with your account.
-
-A verification email is sent to your email account when you create a W&B account. Your email account is considered unverified until you verify your email address. Unverified emails are displayed in red.
-
-Attempt to log in with your email address again to retrieve a second verification email if you no longer have the original verification email that was sent to your email account.
-
-Contact support@wandb.com for account log in issues.
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/storage.md b/content/en/guides/models/app/settings-page/storage.md
deleted file mode 100644
index 66189d96f6..0000000000
--- a/content/en/guides/models/app/settings-page/storage.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-description: Ways to manage W&B data storage.
-menu:
- default:
- identifier: app_storage
- parent: settings
-title: Manage storage
-weight: 60
----
-
-If you are approaching or exceeding your storage limit, there are multiple paths forward to manage your data. The path that's best for you will depend on your account type and your current project setup.
-
-## Manage storage consumption
-W&B offers different methods of optimizing your storage consumption:
-
-- Use [reference artifacts]({{< relref "/guides/core/artifacts/track-external-files.md" >}}) to track files saved outside the W&B system, instead of uploading them to W&B storage.
-- Use an [external cloud storage bucket]({{< relref "teams.md" >}}) for storage. *(Enterprise only)*
-
-## Delete data
-You can also choose to delete data to remain under your storage limit. There are several ways to do this:
-
-- Delete data interactively with the app UI.
-- [Set a TTL policy]({{< relref "/guides/core/artifacts/manage-data/ttl.md" >}}) on Artifacts so they are automatically deleted.
\ No newline at end of file
diff --git a/content/en/guides/models/app/settings-page/teams.md b/content/en/guides/models/app/settings-page/teams.md
deleted file mode 100644
index 5970e70ac1..0000000000
--- a/content/en/guides/models/app/settings-page/teams.md
+++ /dev/null
@@ -1,199 +0,0 @@
----
-description: Collaborate with your colleagues, share results, and track all the experiments across your team. Manage team settings including members, alerts, and privacy.
-menu:
- default:
- identifier: teams
- parent: settings
-title: Team settings
-weight: 30
----
-
-Use W&B Teams as a central workspace for your ML team to build better models faster. Organization admins and team admins can view and edit a team's settings.
-
-* **Track all the experiments** your team has tried so you never duplicate work.
-* **Save and reproduce** previously trained models.
-* **Share progress** and results with your boss and collaborators.
-* **Catch regressions** and immediately get alerted when performance drops.
-* **Benchmark model performance** and compare model versions.
-
-{{< img src="/images/app_ui/teams_overview.webp" alt="Teams workspace overview" >}}
-
-{{% alert %}}
-Only Administration account types can change team settings or remove a member from a team.
-{{% /alert %}}
-
-## Create a collaborative team
-
-1. [Sign up or log in](https://app.wandb.ai/login?signup=true) to your free W&B account.
-2. Click **Invite Team** in the navigation bar.
-3. Create your team and invite collaborators.
-4. Configure your team using the settings described below.
-
-{{% alert %}}
-**Note**: Only the admin of an organization can create a new team.
-{{% /alert %}}
-
-## Team configuration
-You can configure various settings for your team, including its membership, alerts, and privacy settings.
-
-To manage your team's settings, navigate to the **Teams** section in the left-menu and click the team you want to change settings for. You can update the following settings:
-### Members
-
-The Members section shows a list of all pending invitations and the members who have accepted the invitation to join the team. The list displays each member's name, username, email, team role, and access privileges to Models and W&B Weave, which are inherited from the organization. You can choose from the standard team roles **Admin**, **Member**, and **View-only**. If your organization has created [custom roles]({{< relref "/guides/hosting/iam/access-management/manage-organization.md#create-custom-roles" >}}), you can assign a custom role instead.
-
-See [Add and Manage teams]({{< relref "/guides/hosting/iam/access-management/manage-organization.md#add-and-manage-teams" >}}) for information on how to create a team, manage teams, and manage team membership and roles. To configure who can invite new members and configure other privacy settings for the team, refer to [Privacy]({{< relref "#privacy" >}}).
-
-To remove a team member, admins can open the team settings page and click the delete button next to the departing member's name. Any runs logged to the team remain after a user leaves.
-
-### Avatar
-
-To set an avatar for your team:
-
-1. Navigate to the **Teams** section in the left-menu and click the team you want to add an avatar for. This opens the team's overview page.
-2. Hover over the team's default avatar image in the upper-left corner of the page and click the **Upload photo** button. This opens a file prompt.
-3. From the file prompt, select the image you want to use and then click **Open**. This uploads the photo to your team and sets it as your team's avatar.
-
-### Alerts
-
-You can set up alerts to notify your team when runs crash and finish. Alerts can be sent through either email or Slack, and you can customize them to meet your needs.
-
-In the **Team alerts** section, toggle the switch next to the event type you want to receive alerts from. Weights and Biases provides the following event type options:
-
-* **Runs finished**: whether a Weights and Biases run successfully finished.
-* **Run crashed**: if a run has failed to finish.
-* **wandb.alert()**: A custom scriptable alert. See [Send alerts with `wandb.Run.alert()`]({{< relref "/guides/models/track/runs/alert.md" >}}) for more information.
-
-
-### Slack notifications
-
-Configure Slack destinations where your team's [automations]({{< relref "/guides/core/automations/" >}}) can send notifications when an event occurs in a registry or a project, such as when a new artifact is created or when a run metric meets a defined threshold. Refer to [Create a Slack automation]({{< relref "/guides/core/automations/create-automations/slack.md" >}}).
-
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-only.md" >}}
-{{% /pageinfo %}}
-
-### Webhooks
-
-Configure webhooks that your team's [automations]({{< relref "/guides/core/automations/" >}}) can run when an event occurs in a registry or a project, such as when a new artifact is created or when a run metric meets a defined threshold. Refer to [Create a webhook automation]({{< relref "/guides/core/automations/create-automations/webhook.md" >}}).
-
-{{% pageinfo color="info" %}}
-{{< readfile file="/_includes/enterprise-only.md" >}}
-{{% /pageinfo %}}
-
-### Privacy
-
-Team privacy settings allow you to manage visibility and sharing abilities on your team. Only organization admins can modify privacy settings.
-
-You can change the following privacy settings:
-
-- Hide this team from all non-members.
-- Make all future team projects private (public sharing not allowed).
-- Allow any team member to invite other team members (not just admins).
-- Disable public sharing to outside of team for reports in private projects. This disables existing magic links.
-- Automatically recommend new users with matching email domains join this team upon signup.
-- Enable code saving by default.
-
-You can see the privacy settings of all team projects on the team settings page: `app.wandb.ai/teams/your-team-name`
-
-### Usage
-
-The **Usage** section describes the total memory usage the team has consumed on the Weights and Biases servers. The default storage plan is 100GB. For more information about storage and pricing, see the [Pricing](https://wandb.ai/site/pricing) page.
-
-### Storage
-
-The **Storage** section describes the cloud storage bucket configuration that is being used for the team's data. For more information, see [Secure Storage Connector]({{< relref "#secure-storage-connector" >}}) or check out our [W&B Server]({{< relref "/guides/hosting/data-security/secure-storage-connector.md" >}}) docs if you are self-hosting.
-
-## Create a team profile
-
-You can customize your team's profile page to show an introduction and showcase reports and projects that are visible to the public or team members. Present reports, projects, and external links.
-
-* **Highlight your best research** to visitors by showcasing your best public reports
-* **Showcase the most active projects** to make it easier for teammates to find them
-* **Find collaborators** by adding external links to your company or research lab's website and any papers you've published
-
-
-
-## Team roles and permissions
-Select a team role when you invite colleagues to join a team. There are following team role options:
-
-- **Admin**: Team admins can add and remove other admins or team members. They have permissions to modify all projects and full deletion permissions. This includes, but is not limited to, deleting runs, projects, artifacts, and sweeps.
-- **Member**: A regular member of the team. By default, only an admin can invite a team member. To change this behavior, refer to [Privacy settings]({{< relref "#privacy" >}}).
-- **View-Only (Enterprise-only feature)**: View-Only members can view assets within the team such as runs, reports, and workspaces. They can follow and comment on reports, but they can not create, edit, or delete project overview, reports, or runs.
-- **Custom roles (Enterprise-only feature)**: Custom roles allow organization admins to compose new roles based on either of the **View-Only** or **Member** roles, together with additional permissions to achieve fine-grained access control. Team admins can then assign any of those custom roles to users in their respective teams. Refer to [Introducing Custom Roles for W&B Teams](https://wandb.ai/wandb_fc/announcements/reports/Introducing-Custom-Roles-for-W-B-Teams--Vmlldzo2MTMxMjQ3) for details.
-
-A team member can delete only runs they created. Suppose you have two members A and B. Member B moves a run from team B's project to a different project owned by Member A. Member A cannot delete the run Member B moved to Member A's project. An admin can manage runs and sweep runs created by any team member.
-
-### Service accounts
-
-In addition to user roles, teams can also use **service accounts** for automation. Service accounts are not users, but rather non-human identities used for automated workflows. Refer to [Use service accounts to automate workflows]({{< relref "/guides/hosting/iam/authentication/service-accounts.md" >}}) for detailed information.
-
-{{% alert %}}
-W&B recommends assigning more than one admin in a team to ensure that admin operations can continue when the primary admin is not available.
-{{% /alert %}}
-
-### Team settings
-Team settings allow you to manage the settings for your team and its members. With these privileges, you can effectively oversee and organize your team within W&B.
-
-| Permissions | View-Only | Team Member | Team Admin |
-| ------------------- | --------- | ----------- | ---------- |
-| Add team members | | | X |
-| Remove team members | | | X |
-| Manage team settings| | | X |
-
-### Reports
-Report permissions grant access to create, view, and edit reports. The proceeding table lists permissions that apply to all reports across a given team.
-
-| Permissions | View-Only | Team Member | Team Admin |
-| ----------- | --------- | ----------------------------------------------- | ---------- |
-|View reports | X | X | X |
-|Create reports | | X | X |
-|Edit reports | | X (team members can only edit their own reports)| X |
-|Delete reports | | X (team members can only edit their own reports)| X |
-
-### Experiments
-The proceeding table lists permissions that apply to all experiments across a given team.
-
-| Permissions | View-Only | Team Member | Team Admin |
-| ------------------------------------------------------------------------------------ | --------- | ----------- | ---------- |
-| View experiment metadata (includes history metrics, system metrics, files, and logs) | X | X | X |
-| Edit experiment panels and workspaces | | X | X |
-| Log experiments | | X | X |
-| Delete experiments | | X (team members can only delete experiments they created) | X |
-|Stop experiments | | X (team members can only stop experiments they created) | X |
-
-### Artifacts
-The proceeding table lists permissions that apply to all artifacts across a given team.
-
-| Permissions | View-Only | Team Member | Team Admin |
-| ---------------- | --------- | ----------- | ---------- |
-| View artifacts | X | X | X |
-| Create artifacts | | X | X |
-| Delete artifacts | | X | X |
-| Edit metadata | | X | X |
-| Edit aliases | | X | X |
-| Delete aliases | | X | X |
-| Download artifact| | X | X |
-
-### System settings (W&B Server only)
-Use system permissions to create and manage teams and their members and to adjust system settings. These privileges enable you to effectively administer and maintain the W&B instance.
-
-| Permissions | View-Only | Team Member | Team Admin | System Admin |
-| ------------------------ | --------- | ----------- | ---------- | ------------ |
-| Configure system settings| | | | X |
-| Create/delete teams | | | | X |
-
-### Team service account behavior
-
-* When you configure a team in your training environment, you can use a service account from that team to log runs in either of private or public projects within that team. Additionally, you can attribute those runs to a user if **WANDB_USERNAME** or **WANDB_USER_EMAIL** variable exists in your environment and the referenced user is part of that team.
-* When you **do not** configure a team in your training environment and use a service account, the runs log to the named project within that service account's parent team. In this case as well, you can attribute the runs to a user if **WANDB_USERNAME** or **WANDB_USER_EMAIL** variable exists in your environment and the referenced user is part of the service account's parent team.
-* A service account can not log runs to a private project in a team different from its parent team. A service account can log to runs to project only if the project is set to `Open` project visibility.
-
-## Team trials
-
-See the [pricing page](https://wandb.ai/site/pricing) for more information on W&B plans. You can download all your data at any time, either using the dashboard UI or the [Export API]({{< relref "/ref/python/public-api/index.md" >}}).
-
-## Advanced configuration
-
-### Secure storage connector
-
-The team-level secure storage connector allows teams to use their own cloud storage bucket with W&B. This provides greater data access control and data isolation for teams with highly sensitive data or strict compliance requirements. Refer to [Secure Storage Connector]({{< relref "/guides/hosting/data-security/secure-storage-connector.md" >}}) for more information.
diff --git a/content/en/guides/models/app/settings-page/user-settings.md b/content/en/guides/models/app/settings-page/user-settings.md
deleted file mode 100644
index 10a1680bae..0000000000
--- a/content/en/guides/models/app/settings-page/user-settings.md
+++ /dev/null
@@ -1,98 +0,0 @@
----
-description: Manage your profile information, account defaults, alerts, participation
- in beta products, GitHub integration, storage usage, account activation, and create
- teams in your user settings.
-menu:
- default:
- identifier: user-settings
- parent: settings
-title: Manage user settings
-weight: 10
----
-
-Navigate to your user profile page and select your user icon on the top right corner. From the dropdown, choose **Settings**.
-
-## Profile
-
-Within the **Profile** section you can manage and modify your account name and institution. You can optionally add a biography, location, link to a personal or your institution’s website, and upload a profile image.
-
-## Edit your intro
-
-To edit your intro, click **Edit** at the top of your profile. The WYSIWYG editor that opens supports Markdown.
-1. To edit a line, click it. To save time, you can type `/` and choose Markdown from the list.
-1. Use an item's drag handles to move it.
-1. To delete a block, click the drag handle, then click **Delete**.
-1. To save your changes, click **Save**.
-
-### Add social badges
-
-To add a follow badge for the `@weights_biases` account on X, you could add a Markdown-style link with an HTML `` tag that points to the badge image:
-
-```markdown
-[](https://x.com/intent/follow?screen_name=weights_biases)
-```
-In an `` tag, you can specify `width`, `height`, or both. If you specify only one of them, the image's proportions are maintained.
-
-## Default team
-If you are a member of more than one team, the **Default team** section allows you to configure the default team to use when a run or a Weave trace does not specify a team. If you are a member of only one team, that team is the default and this section does not appear.
-
-Select a tab to continue.
-
-{{< tabpane text=true >}}
-{{% tab header="Multi-tenant Cloud" %}}
-Next to **Default location to create new projects in**, click thew drop-down, then select your default team.
-{{% /tab %}}
-{{% tab header="Dedicated Cloud / Self-Managed" %}}
-1. Next to **Default location to create new projects in**, click thew drop-down, then select your default team or your personal entity.
-1. (**Optional**) If an admin has turned on public projects in in **Account** > **Settings** > **Privacy**, configure the default visibility for your new projects. Click the button next to **Default project privacy in your personal account**, then select **Private** (the default) or **Public**.
-1. (**Optional**) If an admin has turned on [default saving and diffing code]({{< relref "/guides/models/app/features/panels/code.md" >}}) in **Account** > **Settings** > **Privacy**, to turn it on for your runs, click **Enable code saving in your personal account**.
-{{% /tab %}}
-{{< /tabpane >}}
-
-{{% alert %}}
-To specify the default team when you’re running a script in an automated environment, you can specify the default location using the `WANDB_ENTITY` [environment variable]({{< relref "https://docs.wandb.ai/guides/models/track/environment-variables.md" >}}).
-{{% /alert %}}
-
-## Teams
-The **Teams** section lists all of your teams.
-
-1. Click a team name to go to the team page.
-1. If you have permission to join additional teams, click **View teams** next to **We found teams for you to join**.
-1. Optionally, turn on **Hide teams in public profile**.
-
-{{% alert %}}
-To create or manage a team, see [Manage teams]({{< relref "/guides/models/app/settings-page/teams/" >}}).
-{{% /alert %}}
-
-## Beta features
-
-Within the **Beta Features** section you can optionally enable fun add-ons and sneak previews of new products in development. Select the toggle switch next to the beta feature you want to enable.
-
-## Alerts
-
-Get notified when your runs crash, finish, or set custom alerts with [wandb.Run.alert()]({{< relref "/guides/models/track/runs/alert.md" >}}). Receive notifications either through Email or Slack. Toggle the switch next to the event type you want to receive alerts from.
-
-* **Runs finished**: whether a Weights and Biases run successfully finished.
-* **Run crashed**: notification if a run has failed to finish.
-
-For more information about how to set up and manage alerts, see [Send alerts with wandb.Run.alert()]({{< relref "/guides/models/track/runs/alert.md" >}}).
-
-## Personal GitHub integration
-
-Connect a personal Github account. To connect a Github account:
-
-1. Select the **Connect Github** button. This will redirect you to an open authorization (OAuth) page.
-2. Select the organization to grant access in the **Organization access** section.
-3. Select **Authorize** **wandb**.
-
-## Delete your account
-
-Select the **Delete Account** button to delete your account.
-
-{{% alert color="secondary" %}}
-Account deletion can not be reversed.
-{{% /alert %}}
-
-## Storage
-
-The **Storage** section describes the total memory usage the your account has consumed on the Weights and Biases servers. The default storage plan is 100GB. For more information about storage and pricing, see the [Pricing](https://wandb.ai/site/pricing) page.
\ No newline at end of file
diff --git a/content/en/guides/models/evaluate-models.md b/content/en/guides/models/evaluate-models.md
deleted file mode 100644
index b1ecdf53dd..0000000000
--- a/content/en/guides/models/evaluate-models.md
+++ /dev/null
@@ -1,241 +0,0 @@
----
-title: Evaluate models
-description:
-menu:
- default:
- identifier: eval-weave
- parent: w-b-models
----
-
-## Evaluate models with Weave
-
-[W&B Weave](https://weave-docs.wandb.ai/) is a purpose-built toolkit for evaluating LLMs and GenAI applications. It provides comprehensive evaluation capabilities including scorers, judges, and detailed tracing to help you understand and improve model performance. Weave integrates with W&B Models, allowing you to evaluate models stored in your Model Registry.
-
-{{< img src="/images/weave/evals.png" alt="Weave evaluation dashboard showing model performance metrics and traces" >}}
-
-### Key features for model evaluation
-
-* **Scorers and judges**: Pre-built and custom evaluation metrics for accuracy, relevance, coherence, and more
-* **Evaluation datasets**: Structured test sets with ground truth for systematic evaluation
-* **Model versioning**: Track and compare different versions of your models
-* **Detailed tracing**: Debug model behavior with complete input/output traces
-* **Cost tracking**: Monitor API costs and token usage across evaluations
-
-### Getting started: Evaluate a model from W&B Registry
-
-Download a model from W&B Models Registry and evaluate it using Weave:
-
-```python
-import weave
-import wandb
-from typing import Any
-
-# Initialize Weave
-weave.init("your-entity/your-project")
-
-# Define a ChatModel that loads from W&B Registry
-class ChatModel(weave.Model):
- model_name: str
-
- def model_post_init(self, __context):
- # Download model from W&B Models Registry
- run = wandb.init(project="your-project", job_type="model_download")
- artifact = run.use_artifact(self.model_name)
- self.model_path = artifact.download()
- # Initialize your model here
-
- @weave.op()
- async def predict(self, query: str) -> str:
- # Your model inference logic
- return self.model.generate(query)
-
-# Create evaluation dataset
-dataset = weave.Dataset(name="eval_dataset", rows=[
- {"input": "What is the capital of France?", "expected": "Paris"},
- {"input": "What is 2+2?", "expected": "4"},
-])
-
-# Define scorers
-@weave.op()
-def exact_match_scorer(expected: str, output: str) -> dict:
- return {"correct": expected.lower() == output.lower()}
-
-# Run evaluation
-model = ChatModel(model_name="wandb-entity/registry-name/model:version")
-evaluation = weave.Evaluation(
- dataset=dataset,
- scorers=[exact_match_scorer]
-)
-results = await evaluation.evaluate(model)
-```
-
-### Integrate Weave evaluations with W&B Models
-
-The [Models and Weave Integration Demo](https://weave-docs.wandb.ai/reference/gen_notebooks/Models_and_Weave_Integration_Demo) shows the complete workflow for:
-
-1. **Load models from Registry**: Download fine-tuned models stored in W&B Models Registry
-2. **Create evaluation pipelines**: Build comprehensive evaluations with custom scorers
-3. **Log results back to W&B**: Connect evaluation metrics to your model runs
-4. **Version evaluated models**: Save improved models back to the Registry
-
-Log evaluation results to both Weave and W&B Models:
-
-```python
-# Run evaluation with W&B tracking
-with weave.attributes({"wandb-run-id": wandb.run.id}):
- summary, call = await evaluation.evaluate.call(evaluation, model)
-
-# Log metrics to W&B Models
-wandb.run.log(summary)
-wandb.run.config.update({
- "weave_eval_url": f"https://wandb.ai/{entity}/{project}/r/call/{call.id}"
-})
-```
-
-### Advanced Weave features
-
-#### Custom scorers and judges
-Create sophisticated evaluation metrics tailored to your use case:
-
-```python
-@weave.op()
-def llm_judge_scorer(expected: str, output: str, judge_model) -> dict:
- prompt = f"Is this answer correct? Expected: {expected}, Got: {output}"
- judgment = await judge_model.predict(prompt)
- return {"judge_score": judgment}
-```
-
-#### Batch evaluations
-Evaluate multiple model versions or configurations:
-
-```python
-models = [
- ChatModel(model_name="model:v1"),
- ChatModel(model_name="model:v2"),
-]
-
-for model in models:
- results = await evaluation.evaluate(model)
- print(f"{model.model_name}: {results}")
-```
-
-### Next steps
-
-* [Complete Weave evaluation tutorial](https://weave-docs.wandb.ai/tutorial-eval/)
-* [Models and Weave integration example](https://weave-docs.wandb.ai/reference/gen_notebooks/Models_and_Weave_Integration_Demo)
-
-
-
-## Evaluate models with tables
-
-Use W&B Tables to:
-* **Compare model predictions**: View side-by-side comparisons of how different models perform on the same test set
-* **Track prediction changes**: Monitor how predictions evolve across training epochs or model versions
-* **Analyze errors**: Filter and query to find commonly misclassified examples and error patterns
-* **Visualize rich media**: Display images, audio, text, and other media types alongside predictions and metrics
-
-
-
-### Basic example: Log evaluation results
-
-```python
-import wandb
-
-# Initialize a run
-run = wandb.init(project="model-evaluation")
-
-# Create a table with evaluation results
-columns = ["id", "input", "ground_truth", "prediction", "confidence", "correct"]
-eval_table = wandb.Table(columns=columns)
-
-# Add evaluation data
-for idx, (input_data, label) in enumerate(test_dataset):
- prediction = model(input_data)
- confidence = prediction.max()
- predicted_class = prediction.argmax()
-
- eval_table.add_data(
- idx,
- wandb.Image(input_data), # Log images or other media
- label,
- predicted_class,
- confidence,
- label == predicted_class
- )
-
-# Log the table
-run.log({"evaluation_results": eval_table})
-```
-
-### Advanced table workflows
-
-#### Compare multiple models
-Log evaluation tables from different models to the same key for direct comparison:
-
-```python
-# Model A evaluation
-with wandb.init(project="model-comparison", name="model_a") as run:
- eval_table_a = create_eval_table(model_a, test_data)
- run.log({"test_predictions": eval_table_a})
-
-# Model B evaluation
-with wandb.init(project="model-comparison", name="model_b") as run:
- eval_table_b = create_eval_table(model_b, test_data)
- run.log({"test_predictions": eval_table_b})
-```
-
-
-
-#### Track predictions over time
-Log tables at different training epochs to visualize improvement:
-
-```python
-for epoch in range(num_epochs):
- train_model(model, train_data)
-
- # Evaluate and log predictions for this epoch
- eval_table = wandb.Table(columns=["image", "truth", "prediction"])
- for image, label in test_subset:
- pred = model(image)
- eval_table.add_data(wandb.Image(image), label, pred.argmax())
-
- wandb.log({f"predictions_epoch_{epoch}": eval_table})
-```
-
-### Interactive analysis in the W&B UI
-
-Once logged, you can:
-1. **Filter results**: Click on column headers to filter by prediction accuracy, confidence thresholds, or specific classes
-2. **Compare tables**: Select multiple table versions to see side-by-side comparisons
-3. **Query data**: Use the query bar to find specific patterns (for example, `"correct" = false AND "confidence" > 0.8`)
-4. **Group and aggregate**: Group by predicted class to see per-class accuracy metrics
-
-
-
-### Example: Error analysis with enriched tables
-
-```python
-# Create a mutable table to add analysis columns
-eval_table = wandb.Table(
- columns=["id", "image", "label", "prediction"],
- log_mode="MUTABLE" # Allows adding columns later
-)
-
-# Initial predictions
-for idx, (img, label) in enumerate(test_data):
- pred = model(img)
- eval_table.add_data(idx, wandb.Image(img), label, pred.argmax())
-
-run.log({"eval_analysis": eval_table})
-
-# Add confidence scores for error analysis
-confidences = [model(img).max() for img, _ in test_data]
-eval_table.add_column("confidence", confidences)
-
-# Add error types
-error_types = classify_errors(eval_table.get_column("label"),
- eval_table.get_column("prediction"))
-eval_table.add_column("error_type", error_types)
-
-run.log({"eval_analysis": eval_table})
-```
diff --git a/content/en/guides/models/sweeps/_index.md b/content/en/guides/models/sweeps/_index.md
deleted file mode 100644
index 6f8f9e29bf..0000000000
--- a/content/en/guides/models/sweeps/_index.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-description: Hyperparameter search and model optimization with W&B Sweeps
-menu:
- default:
- identifier: sweeps
- parent: w-b-models
-title: Sweeps
-url: guides/sweeps
-weight: 2
-cascade:
-- url: guides/sweeps/:filename
----
-{{< cta-button productLink="https://wandb.ai/stacey/deep-drive/workspace?workspace=user-lavanyashukla" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch/Organizing_Hyperparameter_Sweeps_in_PyTorch_with_W%26B.ipynb" >}}
-
-
-Use W&B Sweeps to automate hyperparameter search and visualize rich, interactive experiment tracking. Pick from popular search methods such as Bayesian, grid search, and random to search the hyperparameter space. Scale and parallelize sweep across one or more machines.
-
-{{< img src="/images/sweeps/intro_what_it_is.png" alt="Hyperparameter tuning insights" >}}
-
-### How it works
-Create a sweep with two [W&B CLI]({{< relref "/ref/cli/" >}}) commands:
-
-
-1. Initialize a sweep
-
-```bash
-wandb sweep --project
-```
-
-2. Start the sweep agent
-
-```bash
-wandb agent
-```
-
-{{% alert %}}
-The preceding code snippet, and the colab linked on this page, show how to initialize and create a sweep with wht W&B CLI. See the [Sweeps walkthrough]({{< relref "./walkthrough.md" >}}) for a step-by-step outline of the W&B Python SDK commands to use to define a sweep configuration, initialize a sweep, and start a sweep.
-{{% /alert %}}
-
-
-
-### How to get started
-
-Depending on your use case, explore the following resources to get started with W&B Sweeps:
-
-* Read through the [sweeps walkthrough]({{< relref "./walkthrough.md" >}}) for a step-by-step outline of the W&B Python SDK commands to use to define a sweep configuration, initialize a sweep, and start a sweep.
-* Explore this chapter to learn how to:
- * [Add W&B to your code]({{< relref "./add-w-and-b-to-your-code.md" >}})
- * [Define sweep configuration]({{< relref "/guides/models/sweeps/define-sweep-configuration/" >}})
- * [Initialize sweeps]({{< relref "./initialize-sweeps.md" >}})
- * [Start sweep agents]({{< relref "./start-sweep-agents.md" >}})
- * [Visualize sweep results]({{< relref "./visualize-sweep-results.md" >}})
-* Explore a [curated list of Sweep experiments]({{< relref "./useful-resources.md" >}}) that explore hyperparameter optimization with W&B Sweeps. Results are stored in W&B Reports.
-
-For a step-by-step video, see: [Tune Hyperparameters Easily with W&B Sweeps](https://www.youtube.com/watch?v=9zrmUIlScdY\&ab_channel=Weights%26Biases).
-
-
\ No newline at end of file
diff --git a/content/en/guides/models/sweeps/add-w-and-b-to-your-code.md b/content/en/guides/models/sweeps/add-w-and-b-to-your-code.md
deleted file mode 100644
index fc8dbe03ad..0000000000
--- a/content/en/guides/models/sweeps/add-w-and-b-to-your-code.md
+++ /dev/null
@@ -1,297 +0,0 @@
----
-description: Add W&B to your Python code script or Jupyter Notebook.
-menu:
- default:
- identifier: add-w-and-b-to-your-code
- parent: sweeps
-title: Add W&B (wandb) to your code
-weight: 2
----
-
-This guide provides recommendations on how to integrate W&B into your Python training script or notebook.
-
-## Original training script
-
-Suppose you have a Python script that trains a model (see below). Your goal is to find the hyperparameters that maxmimizes the validation accuracy(`val_acc`).
-
-In your Python script, you define two functions: `train_one_epoch` and `evaluate_one_epoch`. The `train_one_epoch` function simulates training for one epoch and returns the training accuracy and loss. The `evaluate_one_epoch` function simulates evaluating the model on the validation data set and returns the validation accuracy and loss.
-
-You define a configuration dictionary (`config`) that contains hyperparameter values such as the learning rate (`lr`), batch size (`batch_size`), and number of epochs (`epochs`). The values in the configuration dictionary control the training process.
-
-Next you define a function called `main` that mimics a typical training loop. For each epoch, the accuracy and loss is computed on the training and validation data sets.
-
-{{< alert >}}
-This code is a mock training script. It does not train a model, but simulates the training process by generating random accuracy and loss values. The purpose of this code is to demonstrate how to integrate W&B into your training script.
-{{< /alert >}}
-
-```python
-import random
-import numpy as np
-
-def train_one_epoch(epoch, lr, batch_size):
- acc = 0.25 + ((epoch / 30) + (random.random() / 10))
- loss = 0.2 + (1 - ((epoch - 1) / 10 + random.random() / 5))
- return acc, loss
-
-def evaluate_one_epoch(epoch):
- acc = 0.1 + ((epoch / 20) + (random.random() / 10))
- loss = 0.25 + (1 - ((epoch - 1) / 10 + random.random() / 6))
- return acc, loss
-
-# config variable with hyperparameter values
-config = {"lr": 0.0001, "batch_size": 16, "epochs": 5}
-
-def main():
- lr = config["lr"]
- batch_size = config["batch_size"]
- epochs = config["epochs"]
-
- for epoch in np.arange(1, epochs):
- train_acc, train_loss = train_one_epoch(epoch, lr, batch_size)
- val_acc, val_loss = evaluate_one_epoch(epoch)
-
- print("epoch: ", epoch)
- print("training accuracy:", train_acc, "training loss:", train_loss)
- print("validation accuracy:", val_acc, "validation loss:", val_loss)
-
-if __name__ == "__main__":
- main()
-```
-
-In the next section, you will add W&B to your Python script to track hyperparameters and metrics during training. You want to use W&B to find the best hyperparameters that maximize the validation accuracy (`val_acc`).
-
-
-## Training script with W&B Python SDK
-
-How you integrate W&B to your Python script or notebook depends on how you manage sweeps. You can start a sweep job within a Python notebook or script or from the command line.
-
-{{< tabpane text=true >}}
-{{% tab header="Python script or notebook" %}}
-
-Add the following to your Python script:
-
-1. Create a dictionary object where the key-value pairs define a [sweep configuration]({{< relref "/guides/models/sweeps/define-sweep-configuration/" >}}). The sweep configuration defines the hyperparameters you want W&B to explore on your behalf along with the metric you want to optimize. Continuing from the previous example, the batch size (`batch_size`), epochs (`epochs`), and the learning rate (`lr`) are the hyperparameters to vary during each sweep. You want to maximize the accuracy of the validation score so you set `"goal": "maximize"` and the name of the variable you want to optimize for, in this case `val_acc` (`"name": "val_acc"`).
-2. Pass the sweep configuration dictionary to [`wandb.sweep()`]({{< relref "/ref/python/functions/sweep.md" >}}). This initializes the sweep and returns a sweep ID (`sweep_id`). For more information, see [Initialize sweeps]({{< relref "./initialize-sweeps.md" >}}).
-3. At the top of your script, import the W&B Python SDK (`wandb`).
-4. Within your `main` function, use the [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) API to generate a background process to sync and log data as a [W&B Run]({{< relref "/ref/python/experiments/run.md" >}}). Pass the project name as a parameter to the `wandb.init()` method. If you do not pass a project name, W&B uses the default project name.
-5. Fetch the hyperparameter values from the `wandb.Run.config` object. This allows you to use the hyperparameter values defined in the sweep configuration dictionary instead of hard coded values.
-6. Log the metric you are optimizing for to W&B using [`wandb.Run.log()`]({{< relref "/ref/python/experiments/run.md/#method-runlog" >}}). You must log the metric defined in your configuration. For example, if you define the metric to optimize as `val_acc`, you must log `val_acc`. If you do not log the metric, W&B does not know what to optimize for. Within the configuration dictionary (`sweep_configuration` in this example), you define the sweep to maximize the `val_acc` value.
-7. Start the sweep with [`wandb.agent()`]({{< relref "/ref/python/functions/agent.md" >}}). Provide the sweep ID and the name of the function the sweep will execute (`function=main`), and specify the maximum number of runs to try to four (`count=4`).
-
-
-Putting this all together, your script might look similar to the following:
-
-```python
-import wandb # Import the W&B Python SDK
-import numpy as np
-import random
-import argparse
-
-def train_one_epoch(epoch, lr, batch_size):
- acc = 0.25 + ((epoch / 30) + (random.random() / 10))
- loss = 0.2 + (1 - ((epoch - 1) / 10 + random.random() / 5))
- return acc, loss
-
-def evaluate_one_epoch(epoch):
- acc = 0.1 + ((epoch / 20) + (random.random() / 10))
- loss = 0.25 + (1 - ((epoch - 1) / 10 + random.random() / 6))
- return acc, loss
-
-def main(args=None):
- # When called by sweep agent, args will be None,
- # so we use the project from sweep config
- project = args.project if args else None
-
- with wandb.init(project=project) as run:
- # Fetches the hyperparameter values from `wandb.Run.config` object
- lr = run.config["lr"]
- batch_size = run.config["batch_size"]
- epochs = run.config["epochs"]
-
- # Execute the training loop and log the performance values to W&B
- for epoch in np.arange(1, epochs):
- train_acc, train_loss = train_one_epoch(epoch, lr, batch_size)
- val_acc, val_loss = evaluate_one_epoch(epoch)
- run.log(
- {
- "epoch": epoch,
- "train_acc": train_acc,
- "train_loss": train_loss,
- "val_acc": val_acc, # Metric optimized
- "val_loss": val_loss,
- }
- )
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--project", type=str, default="sweep-example", help="W&B project name")
- args = parser.parse_args()
-
- # Define a sweep config dictionary
- sweep_configuration = {
- "method": "random",
- "name": "sweep",
- # Metric that you want to optimize
- # For example, if you want to maximize validation
- # accuracy set "goal": "maximize" and the name of the variable
- # you want to optimize for, in this case "val_acc"
- "metric": {
- "goal": "maximize",
- "name": "val_acc"
- },
- "parameters": {
- "batch_size": {"values": [16, 32, 64]},
- "epochs": {"values": [5, 10, 15]},
- "lr": {"max": 0.1, "min": 0.0001},
- },
- }
-
- # Initialize the sweep by passing in the config dictionary
- sweep_id = wandb.sweep(sweep=sweep_configuration, project=args.project)
-
- # Start the sweep job
- wandb.agent(sweep_id, function=main, count=4)
-```
-
-
-
-{{% /tab %}} {{% tab header="CLI" %}}
-
-Create a YAML configuration file with your sweep configuration. The
-configuration file contains the hyperparameters you want the sweep to explore. In
-the following example, the batch size (`batch_size`), epochs (`epochs`), and
-the learning rate (`lr`) hyperparameters are varied during each sweep.
-
-```yaml
-# config.yaml
-program: train.py
-method: random
-name: sweep
-metric:
- goal: maximize
- name: val_acc
-parameters:
- batch_size:
- values: [16, 32, 64]
- lr:
- min: 0.0001
- max: 0.1
- epochs:
- values: [5, 10, 15]
-```
-
-For more information on how to create a W&B Sweep configuration, see [Define sweep configuration]({{< relref "/guides/models/sweeps/define-sweep-configuration/" >}}).
-
-You must provide the name of your Python script for the `program` key
-in your YAML file.
-
-Next, add the following to the code example:
-
-1. Import the W&B Python SDK (`wandb`) and PyYAML (`yaml`). PyYAML is used to read in our YAML configuration file.
-2. Read in the configuration file.
-3. Use the [`wandb.init()`]({{< relref "/ref/python/functions/init.md" >}}) API to generate a background process to sync and log data as a [W&B Run]({{< relref "/ref/python/experiments/run.md" >}}). Pass the config object to the config parameter.
-4. Define hyperparameter values from `wandb.Run.config` instead of using hard coded values.
-5. Log the metric you want to optimize with [`wandb.Run.log()`]({{< relref "/ref/python/experiments/run.md/#method-runlog" >}}). You must log the metric defined in your configuration. Within the configuration dictionary (`sweep_configuration` in this example) you define the sweep to maximize the `val_acc` value.
-
-```python
-import wandb
-import yaml
-import random
-import numpy as np
-
-
-def train_one_epoch(epoch, lr, batch_size):
- acc = 0.25 + ((epoch / 30) + (random.random() / 10))
- loss = 0.2 + (1 - ((epoch - 1) / 10 + random.random() / 5))
- return acc, loss
-
-
-def evaluate_one_epoch(epoch):
- acc = 0.1 + ((epoch / 20) + (random.random() / 10))
- loss = 0.25 + (1 - ((epoch - 1) / 10 + random.random() / 6))
- return acc, loss
-
-
-def main():
- # Set up your default hyperparameters
- with open("./config.yaml") as file:
- config = yaml.load(file, Loader=yaml.FullLoader)
-
- with wandb.init(config=config) as run:
- for epoch in np.arange(1, run.config['epochs']):
- train_acc, train_loss = train_one_epoch(epoch, run.config['lr'], run.config['batch_size'])
- val_acc, val_loss = evaluate_one_epoch(epoch)
- run.log(
- {
- "epoch": epoch,
- "train_acc": train_acc,
- "train_loss": train_loss,
- "val_acc": val_acc,
- "val_loss": val_loss,
- }
- )
-
-# Call the main function.
-main()
-```
-
-In your CLI, set a maximum number of runs for the sweep
-agent to try. This is optional. This example we set the
-maximum number to 5.
-
-```bash
-NUM=5
-```
-
-Next, initialize the sweep with the [`wandb sweep`]({{< relref "/ref/cli/wandb-sweep.md" >}}) command. Provide the name of the YAML file. Optionally provide the name of the project for the project flag (`--project`):
-
-```bash
-wandb sweep --project sweep-demo-cli config.yaml
-```
-
-This returns a sweep ID. For more information on how to initialize sweeps, see
-[Initialize sweeps]({{< relref "./initialize-sweeps.md" >}}).
-
-Copy the sweep ID and replace `sweepID` in the following code snippet to start
-the sweep job with the [`wandb agent`]({{< relref "/ref/cli/wandb-agent.md" >}})
-command:
-
-```bash
-wandb agent --count $NUM your-entity/sweep-demo-cli/sweepID
-```
-
-For more information, see [Start sweep jobs]({{< relref "./start-sweep-agents.md" >}}).
-
-{{% /tab %}} {{< /tabpane >}}
-
-
-{{% alert title="Logging metrics to W&B in a sweep" %}}
-You must log the metric you define and are optimizing for in both your sweep configuration and with `wandb.Run.log()`. For example, if you define the metric to optimize as `val_acc` within your sweep configuration, you must also log `val_acc` to W&B. If you do not log the metric, W&B does not know what to optimize for.
-
-```python
-with wandb.init() as run:
- val_loss, val_acc = train()
- run.log(
- {
- "val_loss": val_loss,
- "val_acc": val_acc
- }
- )
-```
-
-The following is an incorrect example of logging the metric to W&B. The metric that is optimized for in the sweep configuration is `val_acc`, but the code logs `val_acc` within a nested dictionary under the key `validation`. You must log the metric directly, not within a nested dictionary.
-
-```python
-with wandb.init() as run:
- val_loss, val_acc = train()
- run.log(
- {
- "validation": {
- "val_loss": val_loss,
- "val_acc": val_acc
- }
- }
- )
-```
-
-{{% /alert %}}
\ No newline at end of file
diff --git a/content/en/guides/models/sweeps/define-sweep-configuration/_index.md b/content/en/guides/models/sweeps/define-sweep-configuration/_index.md
deleted file mode 100644
index 8f1c37cf9d..0000000000
--- a/content/en/guides/models/sweeps/define-sweep-configuration/_index.md
+++ /dev/null
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----
-description: Learn how to create configuration files for sweeps.
-menu:
- default:
- identifier: define-sweep-configuration
- parent: sweeps
-url: guides/sweeps/define-sweep-configuration
-title: Define a sweep configuration
-weight: 3
----
-
-
-A W&B Sweep combines a strategy for exploring hyperparameter values with the code that evaluates them. The strategy can be as simple as trying every option or as complex as Bayesian Optimization and Hyperband ([BOHB](https://arxiv.org/abs/1807.01774)).
-
-Define a sweep configuration either in a [Python dictionary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) or a [YAML](https://yaml.org/) file. How you define your sweep configuration depends on how you want to manage your sweep.
-
-{{% alert %}}
-Define your sweep configuration in a YAML file if you want to initialize a sweep and start a sweep agent from the command line. Define your sweep in a Python dictionary if you initialize a sweep and start a sweep entirely within a Python script or notebook.
-{{% /alert %}}
-
-The following guide describes how to format your sweep configuration. See [Sweep configuration options]({{< relref "./sweep-config-keys.md" >}}) for a comprehensive list of top-level sweep configuration keys.
-
-## Basic structure
-
-Both sweep configuration format options (YAML and Python dictionary) utilize key-value pairs and nested structures.
-
-Use top-level keys within your sweep configuration to define qualities of your sweep search such as the name of the sweep ([`name`]({{< relref "./sweep-config-keys.md" >}}) key), the parameters to search through ([`parameters`]({{< relref "./sweep-config-keys.md#parameters" >}}) key), the methodology to search the parameter space ([`method`]({{< relref "./sweep-config-keys.md#method" >}}) key), and more.
-
-
-For example, the proceeding code snippets show the same sweep configuration defined within a YAML file and within a Python dictionary. Within the sweep configuration there are five top level keys specified: `program`, `name`, `method`, `metric` and `parameters`.
-
-
-{{< tabpane text=true >}}
- {{% tab header="CLI" %}}
-Define a sweep configuration in a YAML file if you want to manage sweeps interactively from the command line (CLI)
-
-```yaml title="config.yaml"
-program: train.py
-name: sweepdemo
-method: bayes
-metric:
- goal: minimize
- name: validation_loss
-parameters:
- learning_rate:
- min: 0.0001
- max: 0.1
- batch_size:
- values: [16, 32, 64]
- epochs:
- values: [5, 10, 15]
- optimizer:
- values: ["adam", "sgd"]
-```
- {{% /tab %}}
- {{% tab header="Python script or notebook" %}}
-Define a sweep in a Python dictionary data structure if you define training algorithm in a Python script or notebook.
-
-The proceeding code snippet stores a sweep configuration in a variable named `sweep_configuration`:
-
-```python title="train.py"
-sweep_configuration = {
- "name": "sweepdemo",
- "method": "bayes",
- "metric": {"goal": "minimize", "name": "validation_loss"},
- "parameters": {
- "learning_rate": {"min": 0.0001, "max": 0.1},
- "batch_size": {"values": [16, 32, 64]},
- "epochs": {"values": [5, 10, 15]},
- "optimizer": {"values": ["adam", "sgd"]},
- },
-}
-```
- {{% /tab %}}
-{{< /tabpane >}}
-
-
-Within the top level `parameters` key, the following keys are nested: `learning_rate`, `batch_size`, `epoch`, and `optimizer`. For each of the nested keys you specify, you can provide one or more values, a distribution, a probability, and more. For more information, see the [parameters]({{< relref "./sweep-config-keys.md#parameters" >}}) section in [Sweep configuration options]({{< relref "./sweep-config-keys.md" >}}).
-
-
-## Double nested parameters
-
-Sweep configurations support nested parameters. To delineate a nested parameter, use an additional `parameters` key under the top level parameter name. Sweep configs support multi-level nesting.
-
-Specify a probability distribution for your random variables if you use a Bayesian or random hyperparameter search. For each hyperparameter:
-
-1. Create a top level `parameters` key in your sweep config.
-2. Within the `parameters`key, nest the following:
- 1. Specify the name of hyperparameter you want to optimize.
- 2. Specify the distribution you want to use for the `distribution` key. Nest the `distribution` key-value pair underneath the hyperparameter name.
- 3. Specify one or more values to explore. The value (or values) should be inline with the distribution key.
- 1. (Optional) Use an additional parameters key under the top level parameter name to delineate a nested parameter.
-
-
-
-
-
-
-
-
-{{% alert color="secondary" %}}
-Nested parameters defined in sweep configuration overwrite keys specified in a W&B run configuration.
-
-For example, suppose you initialize a W&B run with the following configuration in a `train.py` Python script (see Lines 1-2). Next, you define a sweep configuration in a dictionary called `sweep_configuration` (see Lines 4-13). You then pass the sweep config dictionary to `wandb.sweep` to initialize a sweep config (see Line 16).
-
-
-```python title="train.py"
-def main():
- run = wandb.init(config={"nested_param": {"manual_key": 1}})
-
-
-sweep_configuration = {
- "top_level_param": 0,
- "nested_param": {
- "learning_rate": 0.01,
- "double_nested_param": {"x": 0.9, "y": 0.8},
- },
-}
-
-# Initialize sweep by passing in config.
-sweep_id = wandb.sweep(sweep=sweep_configuration, project="")
-
-# Start sweep job.
-wandb.agent(sweep_id, function=main, count=4)
-```
-The `nested_param.manual_key` that is passed when the W&B run is initialized is not accessible. The `wandb.Run.config` only possess the key-value pairs that are defined in the sweep configuration dictionary.
-{{% /alert %}}
-
-
-## Sweep configuration template
-
-
-The following template shows how you can configure parameters and specify search constraints. Replace `hyperparameter_name` with the name of your hyperparameter and any values enclosed in `<>`.
-
-```yaml title="config.yaml"
-program:
-method:
-parameter:
- hyperparameter_name0:
- value: 0
- hyperparameter_name1:
- values: [0, 0, 0]
- hyperparameter_name:
- distribution:
- value:
- hyperparameter_name2:
- distribution:
}} | **Manual workspaces** start as blank slates and display only those panels intentionally added by users. Choose a manual workspace: