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Fix critical numbered list formatting issues (#1718)
Fixed 23 high-severity list formatting issues across 15 files: - Split inline bold text that should be on separate lines - Fixed sub-bullet indentation (2 spaces → 3 spaces for proper nesting) - Properly indented code blocks under list items - Fixed continuation text indentation Most affected files: - platform/hosting/self-managed/azure-tf.mdx - guides/launch/setup-launch-sagemaker.mdx - Various ja/ and ko/ translations These formatting issues were breaking list rendering in Mintlify and making documentation harder to read. Issues likely predate the migration.
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‎guides/launch/setup-launch-sagemaker.mdx‎

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@@ -50,94 +50,94 @@ Make a note of the ARNs for these resources. You will need the ARNs when you def
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<Tabs>
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<Tab title="Agent submits pre-built Docker image">
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```json
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Effect": "Allow",
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"Action": [
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"logs:DescribeLogStreams",
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"SageMaker:AddTags",
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"SageMaker:CreateTrainingJob",
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"SageMaker:DescribeTrainingJob"
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],
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"Resource": "arn:aws:sagemaker:<region>:<account-id>:*"
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},
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{
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"Effect": "Allow",
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"Action": "iam:PassRole",
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"Resource": "arn:aws:iam::<account-id>:role/<RoleArn-from-queue-config>"
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},
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{
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"Effect": "Allow",
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"Action": "kms:CreateGrant",
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"Resource": "<ARN-OF-KMS-KEY>",
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"Condition": {
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"StringEquals": {
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"kms:ViaService": "SageMaker.<region>.amazonaws.com",
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"kms:GrantIsForAWSResource": "true"
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}
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}
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}
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]
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}
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```
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```json
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Effect": "Allow",
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"Action": [
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"logs:DescribeLogStreams",
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"SageMaker:AddTags",
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"SageMaker:CreateTrainingJob",
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"SageMaker:DescribeTrainingJob"
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],
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"Resource": "arn:aws:sagemaker:<region>:<account-id>:*"
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},
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{
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"Effect": "Allow",
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"Action": "iam:PassRole",
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"Resource": "arn:aws:iam::<account-id>:role/<RoleArn-from-queue-config>"
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},
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{
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"Effect": "Allow",
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"Action": "kms:CreateGrant",
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"Resource": "<ARN-OF-KMS-KEY>",
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"Condition": {
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"StringEquals": {
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"kms:ViaService": "SageMaker.<region>.amazonaws.com",
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"kms:GrantIsForAWSResource": "true"
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}
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}
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}
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]
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}
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```
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</Tab>
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<Tab title="Agent builds and submits Docker image">
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```json
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Effect": "Allow",
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"Action": [
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"logs:DescribeLogStreams",
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"SageMaker:AddTags",
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"SageMaker:CreateTrainingJob",
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"SageMaker:DescribeTrainingJob"
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],
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"Resource": "arn:aws:sagemaker:<region>:<account-id>:*"
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},
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{
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"Effect": "Allow",
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"Action": "iam:PassRole",
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"Resource": "arn:aws:iam::<account-id>:role/<RoleArn-from-queue-config>"
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},
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```json
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{
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"Version": "2012-10-17",
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"Statement": [
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{
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"Effect": "Allow",
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"Action": [
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"logs:DescribeLogStreams",
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"SageMaker:AddTags",
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"SageMaker:CreateTrainingJob",
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"SageMaker:DescribeTrainingJob"
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],
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"Resource": "arn:aws:sagemaker:<region>:<account-id>:*"
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},
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{
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"Effect": "Allow",
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"Action": "iam:PassRole",
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"Resource": "arn:aws:iam::<account-id>:role/<RoleArn-from-queue-config>"
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},
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{
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"Effect": "Allow",
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"Action": [
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"ecr:CreateRepository",
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"ecr:UploadLayerPart",
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"ecr:PutImage",
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"ecr:CompleteLayerUpload",
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"ecr:InitiateLayerUpload",
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"ecr:DescribeRepositories",
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"ecr:DescribeImages",
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"ecr:BatchCheckLayerAvailability",
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"ecr:BatchDeleteImage"
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],
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"Resource": "arn:aws:ecr:<region>:<account-id>:repository/<repository>"
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},
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{
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"Effect": "Allow",
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"Action": "ecr:GetAuthorizationToken",
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"Resource": "*"
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},
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{
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"Effect": "Allow",
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"Action": [
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"ecr:CreateRepository",
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"ecr:UploadLayerPart",
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"ecr:PutImage",
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"ecr:CompleteLayerUpload",
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"ecr:InitiateLayerUpload",
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"ecr:DescribeRepositories",
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"ecr:DescribeImages",
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"ecr:BatchCheckLayerAvailability",
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"ecr:BatchDeleteImage"
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],
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"Resource": "arn:aws:ecr:<region>:<account-id>:repository/<repository>"
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},
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{
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"Effect": "Allow",
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"Action": "ecr:GetAuthorizationToken",
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"Resource": "*"
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},
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{
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"Effect": "Allow",
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"Action": "kms:CreateGrant",
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"Resource": "<ARN-OF-KMS-KEY>",
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"Condition": {
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"StringEquals": {
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"kms:ViaService": "SageMaker.<region>.amazonaws.com",
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"kms:GrantIsForAWSResource": "true"
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}
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}
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}
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]
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}
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```
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"Effect": "Allow",
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"Action": "kms:CreateGrant",
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"Resource": "<ARN-OF-KMS-KEY>",
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"Condition": {
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"StringEquals": {
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"kms:ViaService": "SageMaker.<region>.amazonaws.com",
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"kms:GrantIsForAWSResource": "true"
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}
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}
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}
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]
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}
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```
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</Tab>
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</Tabs>
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@@ -176,22 +176,22 @@ Next, create a queue in the W&B App that uses SageMaker as its compute resource:
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5. Provide a name for your queue in the **Name** field.
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6. Select **SageMaker** as the **Resource**.
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7. Within the **Configuration** field, provide information about your SageMaker job. By default, W&B will populate a YAML and JSON `CreateTrainingJob` request body:
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```json
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{
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"RoleArn": "<REQUIRED>",
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"ResourceConfig": {
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"InstanceType": "ml.m4.xlarge",
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"InstanceCount": 1,
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"VolumeSizeInGB": 2
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},
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"OutputDataConfig": {
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"S3OutputPath": "<REQUIRED>"
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},
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"StoppingCondition": {
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"MaxRuntimeInSeconds": 3600
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}
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}
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```
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```json
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{
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"RoleArn": "<REQUIRED>",
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"ResourceConfig": {
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"InstanceType": "ml.m4.xlarge",
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"InstanceCount": 1,
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"VolumeSizeInGB": 2
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},
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"OutputDataConfig": {
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"S3OutputPath": "<REQUIRED>"
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},
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"StoppingCondition": {
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"MaxRuntimeInSeconds": 3600
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}
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}
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```
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You must at minimum specify:
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- `RoleArn` : ARN of the SageMaker execution IAM role (see [prerequisites](#prerequisites)). Not to be confused with the launch **agent** IAM role.
@@ -237,33 +237,37 @@ Navigate to **Instances** within the left panel of the EC2 Dashboard on AWS. Ens
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1. Select **Connect**.
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2. Select the **SSH client** tab and following the instructions outlined to connect to your EC2 instance.
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3. Within your EC2 instance, install the following packages:
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```bash
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sudo yum install python311 -y && python3 -m ensurepip --upgrade && pip3 install wandb && pip3 install wandb[launch]
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```
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```bash
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sudo yum install python311 -y && python3 -m ensurepip --upgrade && pip3 install wandb && pip3 install wandb[launch]
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```
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4. Next, install and start Docker within your EC2 instance:
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```bash
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sudo yum update -y && sudo yum install -y docker python3 && sudo systemctl start docker && sudo systemctl enable docker && sudo usermod -a -G docker ec2-user
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newgrp docker
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```
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```bash
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sudo yum update -y && \
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sudo yum install -y docker python3 && \
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sudo systemctl start docker && \
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sudo systemctl enable docker && \
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sudo usermod -a -G docker ec2-user
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newgrp docker
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```
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Now you can proceed to setting up the Launch agent config.
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</Tab>
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<Tab title="Local machine">
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Use the AWS config files located at `~/.aws/config` and `~/.aws/credentials` to associate a role with an agent that is polling on a local machine. Provide the IAM role ARN that you created for the launch agent in the previous step.
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```yaml title="~/.aws/config"
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[profile SageMaker-agent]
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role_arn = arn:aws:iam::<account-id>:role/<agent-role-name>
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source_profile = default
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```
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```yaml title="~/.aws/credentials"
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[default]
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aws_access_key_id=<access-key-id>
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aws_secret_access_key=<secret-access-key>
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aws_session_token=<session-token>
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```
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```yaml title="~/.aws/config"
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[profile SageMaker-agent]
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role_arn = arn:aws:iam::<account-id>:role/<agent-role-name>
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source_profile = default
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```
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```yaml title="~/.aws/credentials"
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[default]
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aws_access_key_id=<access-key-id>
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aws_secret_access_key=<secret-access-key>
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aws_session_token=<session-token>
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```
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Note that session tokens have a [max length](https://docs.aws.amazon.com/cli/latest/reference/sts/get-session-token.html#description) of 1 hour or 3 days depending on the principal they are associated with.
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</Tab>

‎guides/launch/walkthrough.mdx‎

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@@ -55,9 +55,9 @@ Before you get started, ensure you have satisfied the following prerequisites:
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1. Sign up for an account at https://wandb.ai/site and then log in to your W&B account.
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2. This walkthrough requires terminal access to a machine with a working Docker CLI and engine. See the [Docker installation guide](https://docs.docker.com/engine/install/) for more information.
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3. Install W&B Python SDK version `0.17.1` or higher:
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```bash
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pip install wandb>=0.17.1
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```
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```bash
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pip install wandb>=0.17.1
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```
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4. Within your terminal, execute `wandb login` or set the `WANDB_API_KEY` environment variable to authenticate with W&B.
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<Tabs>
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```
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</Tab>
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<Tab title="Environment variable">
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```bash
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WANDB_API_KEY=<your-api-key>
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```
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```bash
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WANDB_API_KEY=<your-api-key>
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```
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Replace `<your-api-key>` with your W&B API key.
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</Tab>
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Create an empty directory and add a Python script named `train.py` with the following content:
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```python
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import wandb
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with wandb.init() as run:
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run.log({"hello": "world"})
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```
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```python
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import wandb
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with wandb.init() as run:
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run.log({"hello": "world"})
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```
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Add a file `requirements.txt` with the following content:
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```text
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wandb>=0.17.1
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```
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```text
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wandb>=0.17.1
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```
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From within the directory, run the following command:
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```bash
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wandb launch --uri . --job-name hello-world-code --project launch-quickstart --entry-point "python train.py"
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```
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```bash
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wandb launch --uri . --job-name hello-world-code --project launch-quickstart --entry-point "python train.py"
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```
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The command does the following:
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1. Log the contents of the current directory to W&B as a Code Artifact.
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The `wandb launch` command can push jobs to the queue directly by specifying the `--queue` argument. For example, to submit the hello-world container job to the queue, run the following command:
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```bash
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wandb launch --docker-image wandb/job_hello_world:main --project launch-quickstart --queue <queue-name>
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```
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```bash
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wandb launch --docker-image wandb/job_hello_world:main --project launch-quickstart --queue <queue-name>
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```

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