From d1be19a0217921bffa71715b2ca0f43d50ca08df Mon Sep 17 00:00:00 2001 From: Erwan Granger <32554862+erwangranger@users.noreply.github.com> Date: Fri, 26 Jan 2024 07:43:07 -0500 Subject: [PATCH 01/58] adding pre-puller for pipeline images (#139) * adding pre-puller for pipeline images --- .github/.wordlist.txt | 2 + .../ic-rhoai-configuration/images-puller.yaml | 59 +++++++++++++++++++ 2 files changed, 61 insertions(+) diff --git a/.github/.wordlist.txt b/.github/.wordlist.txt index 945d1c8e..6ef3197c 100644 --- a/.github/.wordlist.txt +++ b/.github/.wordlist.txt @@ -4,6 +4,7 @@ amazonaws Anbalagan ansible Ansible +antora apiVersion applicationset ApplicationSet @@ -241,6 +242,7 @@ Webpack www yaml YAML +yml yolo YOLO yolov diff --git a/bootstrap/ic-rhoai-configuration/images-puller.yaml b/bootstrap/ic-rhoai-configuration/images-puller.yaml index af290b1e..b5e56abc 100644 --- a/bootstrap/ic-rhoai-configuration/images-puller.yaml +++ b/bootstrap/ic-rhoai-configuration/images-puller.yaml @@ -96,3 +96,62 @@ spec: requests: cpu: 10m memory: 10Mi +--- +apiVersion: apps/v1 +kind: DaemonSet +metadata: + name: image-puller-pipelines + namespace: rhods-notebooks + labels: + k8s-app: image-puller +spec: + selector: + matchLabels: + name: image-puller + template: + metadata: + labels: + name: image-puller + spec: + containers: + - name: mariadb + image: registry.redhat.io/rhel8/mariadb-103@sha256:2370ca7fd91c0d35910006c8ba40bb9651244b2a42050cb9b2dd9d5163155e15 + command: ["tail"] + args: ["-f", "/dev/null"] + resources: + limits: + memory: 20Mi + requests: + cpu: 10m + memory: 10Mi + - name: ds-pipeline-scheduledworkflow-pipelines-definition + image: registry.redhat.io/rhods/odh-ml-pipelines-scheduledworkflow-rhel8@sha256:17cb168262d64409c613d09815a122958e69c29c1b78218f5f8a47af0a76b3f7 + command: ["tail"] + args: ["-f", "/dev/null"] + resources: + limits: + memory: 20Mi + requests: + cpu: 10m + memory: 10Mi + - name: ds-pipeline-pipelines-definition + image: registry.redhat.io/rhods/odh-ml-pipelines-api-server-rhel8@sha256:e1a5ceff2354b5ea787838143338848727a0dba4240fe8ed84350128603399e9 + command: ["tail"] + args: ["-f", "/dev/null"] + resources: + limits: + memory: 20Mi + requests: + cpu: 10m + memory: 10Mi + - name: ds-pipeline-persistenceagent-pipelines-definition + image: registry.redhat.io/rhods/odh-ml-pipelines-persistenceagent-rhel8@sha256:e87c4aab19435d4d3ded11acf53506a586127a4ec670db65bd25c72ed66c3537 + command: ["tail"] + args: ["-f", "/dev/null"] + resources: + limits: + memory: 20Mi + requests: + cpu: 10m + memory: 10Mi + From 19d607a45242e92c46d81947d44b4208fe3708ac Mon Sep 17 00:00:00 2001 From: RHRolun Date: Wed, 7 Feb 2024 09:07:48 +0100 Subject: [PATCH 02/58] Predicting the same values --- lab-materials/04/04-04-accident-recog.ipynb | 55 +++++++++++++++------ lab-materials/04/04-05-model-serving.ipynb | 2 +- lab-materials/04/remote_infer.py | 18 +++---- 3 files changed, 48 insertions(+), 27 deletions(-) diff --git a/lab-materials/04/04-04-accident-recog.ipynb b/lab-materials/04/04-04-accident-recog.ipynb index 6efab0d9..12019b23 100644 --- a/lab-materials/04/04-04-accident-recog.ipynb +++ b/lab-materials/04/04-04-accident-recog.ipynb @@ -21,7 +21,12 @@ "# !pip install --no-cache-dir --no-dependencies -r requirements.txt\n", "\n", "from ultralytics import YOLO\n", - "from PIL import Image" + "from PIL import Image\n", + "import numpy as np\n", + "import cv2\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import remote_infer" ] }, { @@ -54,41 +59,61 @@ "outputs": [], "source": [ "# Test the model against a car accident image\n", + "image_path = \"images/carImage3.jpg\" # We know that Image3 represents a severe accident with over 86% certainty.\n", + "\n", + "_, scale, original_image = remote_infer.preprocess(image_path)\n", "\n", - "results = model.predict(\"images/carImage3.jpg\") # We know that Image3 represents a severe accident with over 86% certainty." + "original_image: np.ndarray = cv2.imread(image_path)\n", + "blob = cv2.dnn.blobFromImage(original_image, size=(640, 640), swapRB=False)\n", + "blob = np.ascontiguousarray(blob[0].transpose((1,2,0)))\n", + "results = model.predict(blob)" ] }, { "cell_type": "code", "execution_count": null, - "id": "88eb63c2-a479-4fdd-b194-c36935badd07", - "metadata": {}, + "id": "04757541-e559-4a12-9f99-98ca183452d7", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Extract all the information from the result (type, bounding box, probability)\n", "\n", + "detections = []\n", "result = results[0]\n", "for box in result.boxes:\n", - " class_id = result.names[box.cls[0].item()]\n", - " cords = box.xyxy[0].tolist()\n", - " cords = [round(x) for x in cords]\n", - " conf = round(box.conf[0].item(), 2)\n", - " print(\"Object type:\", class_id)\n", - " print(\"Coordinates:\", cords)\n", - " print(\"Probability:\", conf)\n", - " print(\"---\")" + " class_id = int(box.cls.item())\n", + " score = box.conf.item()\n", + " unscaled_cords = box.xyxy.squeeze().tolist()\n", + " cords = [round(unscaled_cords[0] * scale[1]), round(unscaled_cords[1] * scale[0]), round(unscaled_cords[2] * scale[1]), round(unscaled_cords[3] * scale[0])]\n", + " detection = {\n", + " 'class_id': class_id,\n", + " 'class_name': result.names[class_id],\n", + " 'confidence': score,\n", + " 'box': cords,\n", + " 'scale': scale}\n", + " detections.append(detection)\n", + " print(detection)\n", + " remote_infer.draw_bounding_box(original_image, class_id, score, cords[0], cords[1], cords[2], cords[3])" ] }, { "cell_type": "code", "execution_count": null, - "id": "970ea08c-9419-47bb-9df1-2914d91e8c8f", - "metadata": {}, + "id": "cefc9691-0cd5-446f-b4b6-fc02112ef2fa", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# On the image, draw the box, the name of the class, and the probability (how much the model is sure about the detection).\n", "\n", - "Image.fromarray(result.plot()[:,:,::-1])" + "img = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(6, 3)\n", + "plt.axis('off')\n", + "plt.imshow(img)" ] }, { diff --git a/lab-materials/04/04-05-model-serving.ipynb b/lab-materials/04/04-05-model-serving.ipynb index 54369552..41964749 100644 --- a/lab-materials/04/04-05-model-serving.ipynb +++ b/lab-materials/04/04-05-model-serving.ipynb @@ -129,7 +129,7 @@ "fig = plt.gcf()\n", "fig.set_size_inches(6, 3)\n", "plt.axis('off')\n", - "plt.imshow(img);" + "plt.imshow(img)" ] } ], diff --git a/lab-materials/04/remote_infer.py b/lab-materials/04/remote_infer.py index 38a981dd..dee8117b 100644 --- a/lab-materials/04/remote_infer.py +++ b/lab-materials/04/remote_infer.py @@ -13,17 +13,13 @@ def preprocess(image_path): original_image: np.ndarray = cv2.imread(image_path) [height, width, _] = original_image.shape - - # Prepare a square image for inference - length = max((height, width)) - image = np.zeros((length, length, 3), np.uint8) - image[0:height, 0:width] = original_image - + # Calculate scale factor - scale = length / 640 - + scale = (height/640, width/640) + # Preprocess the image and prepare blob for model - blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) + blob = cv2.dnn.blobFromImage(original_image, scalefactor=1 / 255, size=(640, 640), swapRB=True) + return blob, scale, original_image @@ -82,8 +78,8 @@ def postprocess(response, scale, original_image): 'box': box, 'scale': scale} detections.append(detection) - draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale), round(box[1] * scale), - round((box[0] + box[2]) * scale), round((box[1] + box[3]) * scale)) + draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale[1]), round(box[1] * scale[0]), + round((box[0] + box[2]) * scale[1]), round((box[1] + box[3]) * scale[0])) return original_image From 6fdbf143c143f26be98b91877f12432f3d269f3a Mon Sep 17 00:00:00 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.../images/05/05-toggle-off-workbench.jpg | Bin 0 -> 86102 bytes .../modules/ROOT/pages/05-01-application.adoc | 11 +++- .../05-03-web-app-deploy-application.adoc | 6 ++- .../ROOT/pages/05-04-web-app-validating.adoc | 9 +++- .../ROOT/pages/05-05-process-claims.adoc | 47 ++++++++++++++---- 18 files changed, 57 insertions(+), 16 deletions(-) create mode 100644 content/modules/ROOT/assets/images/05/05-change-to-vscode.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-create-pvc-button.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-create-run.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-import-pipeline-highlighted.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-import-pipeline.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-new-app-claim-unprocessed.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-new-app-claims.jpg create mode 100644 content/modules/ROOT/assets/images/05/05-open-url.jpg create 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zT-c8DZ1O3YVwEam3hK(xFFqgGnUF@u$D^Z?d_3n5OvNP&j7|Rh{RA83wsAkfxS(1j zXaDVLq?`2+Jkn)xyY$Z}vIY7dd^$go1?b+kL|P9G5|l1=WN6C;y$ z!o9hw_Majy5{UT&T7S;tz|%#rEZbZtvu+*szG$ACk8Rx=*%|6g`rTPpv?u&FhSq~w TEH$G~0k1pi_to-MOauNuM&)%5 literal 0 HcmV?d00001 diff --git a/content/modules/ROOT/pages/05-01-application.adoc b/content/modules/ROOT/pages/05-01-application.adoc index baea214a..774329ce 100644 --- a/content/modules/ROOT/pages/05-01-application.adoc +++ b/content/modules/ROOT/pages/05-01-application.adoc @@ -24,8 +24,15 @@ If you want to have a look at the code of the application, you can do it directl - Close the different Jupyter tabs that may still be opened in your browser. - Go back to the OpenShift AI dashboard. - Stop your Workbench by using the toggle. + ++ +[.bordershadow] +image::05/05-toggle-off-workbench.jpg[] + + + -- You should probably give it 5 or 10 seconds to fully stop. -- Edit the Workbench configuration by clicking on the menu item at the left, then **Edit workbench** button. +- Edit the Workbench configuration by clicking the 3 vertical dots on the right, then choose the **Edit workbench** button. + [.bordershadow] image::05/stop-workbench.png[] @@ -33,7 +40,7 @@ image::05/stop-workbench.png[] - In the image section, change the image to `CUSTOM - VSCode for Insurance Claim Processsing Lab`: + [.bordershadow] -image::05/vscode-image.png[] +image::05/05-change-to-vscode.jpg[] - Note that this is a Custom-built image added in this particular lab to illustrate the flexibility of the OpenShift AI platform. Not everyone wants to use Jupyter Notebooks for everything. - Then click on `Update workbench` at the bottom of the page: diff --git a/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc b/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc index 415c2ba1..385f8a82 100644 --- a/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc +++ b/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc @@ -119,4 +119,8 @@ image::05/jobs-completed.png[] [source, text] echo " Application URL : https://$(oc get route ic-app -ojsonpath='{.status.ingress[0].host}')/ " -Open the URL in your browser to access the application and head to the next step. ++ +[.bordershadow] +image::05/05-open-url.jpg[] + +Open the the URL in your browser to access the application and head to the next step. diff --git a/content/modules/ROOT/pages/05-04-web-app-validating.adoc b/content/modules/ROOT/pages/05-04-web-app-validating.adoc index 41522a56..77106193 100644 --- a/content/modules/ROOT/pages/05-04-web-app-validating.adoc +++ b/content/modules/ROOT/pages/05-04-web-app-validating.adoc @@ -3,7 +3,7 @@ include::_attributes.adoc[] == Original App -The first item menu on the left, Original App, gives a view of what the older application used at the insurance company looks like. You can navigate each claim and see that the interface was very crude, with a simple copy of the originally received email with the pictures that were attached to it. +The first item menu on the left, Original App, gives a view of what the older application used at the insurance company looks like. You can navigate each claim (click on tabs 'Claim 1', 'Claim 2' and 'Claim 3') and see that the interface was very crude, with a simple copy of the originally received email with the pictures that were attached to it. [.bordershadow] image::05/original-app-1.png[] @@ -12,7 +12,12 @@ image::05/original-app-2.png[] == Prototype App -The second item menu on the left, New App, gives a view of what the prototype of the new application looks like. You can navigate each claim and see that the interface is much more user friendly, with a nice interface to navigate the claim and see the pictures that were attached to it. +The second item menu on the left, New App, gives a view of what the prototype of the new application looks like. + +[.bordershadow] +image::05/05-new-app-claims.jpg[] + +You can navigate each claim (click the subject line) and see that the interface is much more user friendly, with a nice interface to navigate the claim and see the pictures that were attached to it. Moreover, for the claims that have been processed, you can see the summary of this claim as well as the customer sentiment analysis, the location and time of the event that have been extracted from the claim, and the pictures that have been processed by the AI. diff --git a/content/modules/ROOT/pages/05-05-process-claims.adoc b/content/modules/ROOT/pages/05-05-process-claims.adoc index cbd52d1c..723de39f 100644 --- a/content/modules/ROOT/pages/05-05-process-claims.adoc +++ b/content/modules/ROOT/pages/05-05-process-claims.adoc @@ -2,7 +2,12 @@ include::_attributes.adoc[] == What will the pipeline do? -Now that we have the web app deployed, we can see that some claims are unprocessed yet. Of course, we want to execute this processing, and it's even better if it can be fully automated! +Now that we have the web app deployed, we can see that some claims are still unprocessed. + +[.bordershadow] +image::05/05-new-app-claim-unprocessed.jpg[] + +Of course, we want to execute this processing, and it's even better if it can be fully automated! For that, we will use a pipeline that can either be run ad-hoc or scheduled just like, the sanity check pipeline. @@ -12,6 +17,10 @@ This pipeline is also a good starting point for creating an ArgoCD or Tekton pip If you navigate to `insurance-claim-processing/lab-materials/05/05-05` you can see a variety of files. + +[.bordershadow] +image::05/05-process-claims-yaml.jpg[] + + This time, we will use the **yaml definition** of a **pipeline**, `process_claims.yaml`, to process the claims. + Here are the main files of the pipeline and what they do: @@ -29,7 +38,12 @@ NOTE: In the folder, we still have for reference an Elyra version of the pipelin == Create a new PVC Before we can run the pipeline, we need to create a PVC that will be used to store intermediary files and results in. + -Go to the OpenShift Console and navigate to Storage -> PersistentVolumeClaims. +Go to the OpenShift Console. Make sure and change your view from 'Developer' to 'Administrator. + +[.bordershadow] +image::05/05-switch-to-admin-view.jpg[] + +Under the Administrator view, Navigate to Storage -> PersistentVolumeClaims. [.bordershadow] image::05/05-PVC.png[go to PVC] @@ -37,7 +51,7 @@ image::05/05-PVC.png[go to PVC] Make sure you are in the right project (your username) and then press `Create PersistentVolumeClaim`. [.bordershadow] -image::05/05-create-pvc.png[Create PVC] +image::05/05-create-pvc-button.jpg[Create PVC] * Use these settings: ** StorageClass: @@ -71,24 +85,26 @@ image::05/05-PVC-settings.png[PVC settings] Then press `Create`. == Run the pipeline -To run the pipeline, start by downloading the `process_claims.yaml` file locally. + -Then go to your Data Science project and press Import Pipeline. +To run the pipeline, start by downloading the `process_claims.yaml` file locally to your pc. + + +Then go to the 'Red Hat OpenShift Data Science' dashboard. Select your Data Science project, scroll down until you see the 'Data Connections' service, and press Import Pipeline. [.bordershadow] -image::05/05-import-pipeline-button.png[import pipeline] +image::05/05-import-pipeline.jpg[import pipeline] Now upload process_claims.yaml file, either by drag-and-dropping or using the Upload button. Then make sure to give your pipeline a good name like `Process Claims Pipeline`. + + It should look something like this afterwards: [.bordershadow] -image::05/05-imported-pipeline.png[imported pipeline] +image::05/05-import-pipeline-highlighted.jpg[imported pipeline] Press `Import Pipeline` and you should see it pop up under your pipelines. Now go into the settings at the right side and press Create Run to create a new run of the pipeline you just added. [.bordershadow] -image::05/05-create-run.png[create run] +image::05/05-create-run.jpg[create run] * Use these settings: ** Name: @@ -126,7 +142,16 @@ Note that by changing claim_id you can change which claim to process. If you set Press `Create` and watch it go. +[.bordershadow] +image::05/05-process-claims.jpg[process] + == Check the results -After the pipeline has finished running, you can go to the app and take a look at the claims. + -Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field. + -You can also see that we have new image(s) which have bounding boxes where the damage is. \ No newline at end of file +After the pipeline has finished running, you can go to the app and take a look at the claims. You will see that claim 3 is now processed. + +[.bordershadow] +image::05/05-process-claim3-app.jpg[claim3processed] + +Click on claim3. Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field. 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**Create PersistentVolumeClaim**. ++ [.bordershadow] image::05/05-create-pvc-button.jpg[Create PVC] @@ -79,30 +81,38 @@ Shared access (RWX) [subs=attributes+] 1 GiB +* it should look like: ++ [.bordershadow] image::05/05-PVC-settings.png[PVC settings] -Then press `Create`. +* Then press **Create** == Run the pipeline -To run the pipeline, start by downloading the `process_claims.yaml` file locally to your pc. + - -Then go to the 'Red Hat OpenShift Data Science' dashboard. Select your Data Science project, scroll down until you see the 'Data Connections' service, and press Import Pipeline. +* Start by downloading the `process_claims.yaml` file locally to your laptop. +** In your VSCode Workbench, right click on the file, and select **Download**. +** Save the file somewhere on your laptop +* Then go to the Red Hat OpenShift Data Science dashboard +* Select your Data Science project +* Scroll down until you see the **Pipelines** section +* Click **Import Pipeline** ++ [.bordershadow] image::05/05-import-pipeline.jpg[import pipeline] -Now upload process_claims.yaml file, either by drag-and-dropping or using the Upload button. Then make sure to give your pipeline a good name like `Process Claims Pipeline`. + - -It should look something like this afterwards: - +* Now upload the `process_claims.yaml` file, either by drag-and-dropping or using the Upload button. +* Then make sure to give your pipeline a good name like `Process Claims Pipeline`. +* It should look something like this afterwards: ++ [.bordershadow] image::05/05-import-pipeline-highlighted.jpg[imported pipeline] -Press `Import Pipeline` and you should see it pop up under your pipelines. - -Now go into the settings at the right side and press Create Run to create a new run of the pipeline you just added. +* Click **Import Pipeline** and you should see it pop up under your pipelines. +* Now go into the settings at the right side +* Click **Create Run** to create a new run of the pipeline you just added. ++ [.bordershadow] image::05/05-create-run.jpg[create run] @@ -132,26 +142,31 @@ Run once immediately after creation [subs=attributes+] http://modelmesh-serving.{user}:8008 -This is the same route to the object detection endpoint that was used earlier in the workshop. + -When done it should look something like this: +This is the same route to the object detection endpoint that was used earlier in the workshop. +* When done it should look something like this: ++ [.bordershadow] image::05/05-run-settings-create-pipeline.png[run settings] -Note that by changing claim_id you can change which claim to process. If you set it to 0 it will process all unprocessed claims. - -Press `Create` and watch it go. +* Note that by changing claim_id you can change which claim to process. If you set it to 0 it will process all unprocessed claims. +* Click **Create** and watch it run. ++ [.bordershadow] image::05/05-process-claims.jpg[process] == Check the results -After the pipeline has finished running, you can go to the app and take a look at the claims. You will see that claim 3 is now processed. +* After the pipeline has finished running, you can go to the app and take a look at the claims. +* You will see that claim 3 is now processed. ++ [.bordershadow] image::05/05-process-claim3-app.jpg[claim3processed] -Click on claim3. Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field. You can also see that we have new image(s) which have bounding boxes where the damage is. - +* Click on claim3. +* Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field. +* You can also see that we have new image(s) which have bounding boxes where the damage is. ++ [.bordershadow] image::05/05-processed-claim.jpg[claim3processe] \ No newline at end of file From e6296d1d741561082facce073005a24235f3eb8e Mon Sep 17 00:00:00 2001 From: Erwan Granger <32554862+erwangranger@users.noreply.github.com> Date: Sun, 18 Feb 2024 13:07:45 -0300 Subject: [PATCH 05/58] cleaning trailing dots --- .../ROOT/pages/05-05-process-claims.adoc | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/modules/ROOT/pages/05-05-process-claims.adoc b/content/modules/ROOT/pages/05-05-process-claims.adoc index 284edfe1..387159bd 100644 --- a/content/modules/ROOT/pages/05-05-process-claims.adoc +++ b/content/modules/ROOT/pages/05-05-process-claims.adoc @@ -25,13 +25,13 @@ This time, we will use the **yaml definition** of a **pipeline**, `process_claim Here are the main files of the pipeline and what they do: -* *get_claims* - Will connect to the database, fetch any unprocessed claims, and add them to a list that will be passed to the other tasks through a file: `claims.json`. +* *get_claims* - Will connect to the database, fetch any unprocessed claims, and add them to a list that will be passed to the other tasks through a file: `claims.json` * The following scripts will go through all the claims that need to be processed, and use the full body of the text to try and find some important feature, then push the results to the database: -** *get_location* - Finds the location of the accident. -** *get_accident_time* - Finds the time of the accident. -** *summarize_text* - Makes a short summary of the text. -** *get_sentiment* - Gets the sentiment of the text. -* *detect_objects* - Downloads the images of the claim and uses the served object-detection model to classify the damages in the image. +** *get_location* - Finds the location of the accident +** *get_accident_time* - Finds the time of the accident +** *summarize_text* - Makes a short summary of the text +** *get_sentiment* - Gets the sentiment of the text +* *detect_objects* - Downloads the images of the claim and uses the served object-detection model to classify the damages in the image NOTE: In the folder, we still have for reference an Elyra version of the pipeline (`process_claims.pipeline`), but you cannot really use it from VSCode, which is the environment you should still be in. @@ -39,18 +39,18 @@ NOTE: In the folder, we still have for reference an Elyra version of the pipelin Before we can run the pipeline, we need to create a PVC that will be used to store intermediary files and results in. + -* Go to the OpenShift Console. +* Go to the OpenShift Console * Make sure and change your view from **Developer** to **Administrator** + [.bordershadow] image::05/05-switch-to-admin-view.jpg[] -* Under the Administrator view, navigate to **Storage** -> **PersistentVolumeClaims**. +* Under the Administrator view, navigate to **Storage** -> **PersistentVolumeClaims** + [.bordershadow] image::05/05-PVC.png[go to PVC] -* Make sure you are in the right project (your username) and then press **Create PersistentVolumeClaim**. +* Make sure you are in the right project (your username) and then press **Create PersistentVolumeClaim** + [.bordershadow] image::05/05-create-pvc-button.jpg[Create PVC] @@ -90,8 +90,8 @@ image::05/05-PVC-settings.png[PVC settings] == Run the pipeline -* Start by downloading the `process_claims.yaml` file locally to your laptop. -** In your VSCode Workbench, right click on the file, and select **Download**. +* Start by downloading the `process_claims.yaml` file locally to your laptop +** In your VSCode Workbench, right click on the file, and select **Download** ** Save the file somewhere on your laptop * Then go to the Red Hat OpenShift Data Science dashboard * Select your Data Science project @@ -101,17 +101,17 @@ image::05/05-PVC-settings.png[PVC settings] [.bordershadow] image::05/05-import-pipeline.jpg[import pipeline] -* Now upload the `process_claims.yaml` file, either by drag-and-dropping or using the Upload button. -* Then make sure to give your pipeline a good name like `Process Claims Pipeline`. +* Now upload the `process_claims.yaml` file, either by drag-and-dropping or using the Upload button +* Then make sure to give your pipeline a good name like `Process Claims Pipeline` * It should look something like this afterwards: + [.bordershadow] image::05/05-import-pipeline-highlighted.jpg[imported pipeline] -* Click **Import Pipeline** and you should see it pop up under your pipelines. +* Click **Import Pipeline** and you should see it pop up under your pipelines * Now go into the settings at the right side -* Click **Create Run** to create a new run of the pipeline you just added. +* Click **Create Run** to create a new run of the pipeline you just added + [.bordershadow] image::05/05-create-run.jpg[create run] @@ -149,24 +149,24 @@ This is the same route to the object detection endpoint that was used earlier in [.bordershadow] image::05/05-run-settings-create-pipeline.png[run settings] -* Note that by changing claim_id you can change which claim to process. If you set it to 0 it will process all unprocessed claims. +* Note that by changing claim_id you can change which claim to process. If you set it to 0 it will process all unprocessed claims -* Click **Create** and watch it run. +* Click **Create** and watch it run + [.bordershadow] image::05/05-process-claims.jpg[process] == Check the results -* After the pipeline has finished running, you can go to the app and take a look at the claims. -* You will see that claim 3 is now processed. +* After the pipeline has finished running, you can go to the app and take a look at the claims +* You will see that claim 3 is now processed + [.bordershadow] image::05/05-process-claim3-app.jpg[claim3processed] -* Click on claim3. -* Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field. -* You can also see that we have new image(s) which have bounding boxes where the damage is. +* Click on claim3 +* Instead of just a long body, you will now see a summary, a location field, an accident time field, and a sentiment field +* You can also see that we have new image(s) which have bounding boxes where the damage is + [.bordershadow] image::05/05-processed-claim.jpg[claim3processe] \ No newline at end of file From a428e8ab0429ad9d11a9c6e6031adc16a2be804c Mon Sep 17 00:00:00 2001 From: Erwan Granger <32554862+erwangranger@users.noreply.github.com> Date: Sun, 18 Feb 2024 13:11:24 -0300 Subject: [PATCH 06/58] spelling --- .github/.wordlist.txt | 1 + content/modules/ROOT/pages/05-05-process-claims.adoc | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/.wordlist.txt b/.github/.wordlist.txt index 6ef3197c..c6f3d3bf 100644 --- a/.github/.wordlist.txt +++ b/.github/.wordlist.txt @@ -107,6 +107,7 @@ Inlining insecureEdgeTerminationPolicy io ipynb +jpg js json jsonpath diff --git a/content/modules/ROOT/pages/05-05-process-claims.adoc b/content/modules/ROOT/pages/05-05-process-claims.adoc index 387159bd..519a6404 100644 --- a/content/modules/ROOT/pages/05-05-process-claims.adoc +++ b/content/modules/ROOT/pages/05-05-process-claims.adoc @@ -169,4 +169,4 @@ image::05/05-process-claim3-app.jpg[claim3processed] * You can also see that we have new image(s) which have bounding boxes where the damage is + [.bordershadow] -image::05/05-processed-claim.jpg[claim3processe] \ No newline at end of file +image::05/05-processed-claim.jpg[claim3processed] \ No newline at end of file From 2cabb7f97acc55664de4c93499bc939f8251e12d Mon Sep 17 00:00:00 2001 From: Guillaume Moutier Date: Wed, 21 Feb 2024 15:35:23 -0500 Subject: [PATCH 07/58] link to contributors --- content/modules/ROOT/pages/07-01-end-of-lab.adoc | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/content/modules/ROOT/pages/07-01-end-of-lab.adoc b/content/modules/ROOT/pages/07-01-end-of-lab.adoc index e12c0fe7..b4e5ca08 100644 --- a/content/modules/ROOT/pages/07-01-end-of-lab.adoc +++ b/content/modules/ROOT/pages/07-01-end-of-lab.adoc @@ -9,11 +9,4 @@ The instructions of this lab are always available at https://rh-aiservices-bu.gi == Acknowledgements -Here is a list of people who have participated, directly or indirectly, to the creation and delivery of this lab. - -* Guillaume Moutier -* Robert Lundberg -* Audrey Guidera -* Chris Chase -* Prasanth Anbalagan -* Erwan Granger +Thanks to everyone who helped create this lab, https://github.com/rh-aiservices-bu/insurance-claim-processing/graphs/contributors[contributors,window=_blank] and reviewers! From 1d71fe8aa9b2b6aa24e913486c5a39bac334361c Mon Sep 17 00:00:00 2001 From: RHRolun Date: Mon, 26 Feb 2024 15:37:05 +0100 Subject: [PATCH 08/58] Lowered the requirement for acceptable response similarity --- lab-materials/03/06/test_response_quality.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lab-materials/03/06/test_response_quality.py b/lab-materials/03/06/test_response_quality.py index 89a74097..cb4dc5f4 100644 --- a/lab-materials/03/06/test_response_quality.py +++ b/lab-materials/03/06/test_response_quality.py @@ -17,7 +17,7 @@ def test_response_quality(): similarity = similarity_metric(response, expected_response) print(similarity) - if similarity <= 0.9: + if similarity <= 0.8: raise Exception("Output is not similar enough to expected output") print("Response Quality OK") From dfb8d80f4c8aa93cf9016cb2c26e836f809e1acc Mon Sep 17 00:00:00 2001 From: RHRolun Date: Wed, 6 Mar 2024 09:10:49 +0100 Subject: [PATCH 09/58] Removing model_sha as it seems volatile --- lab-materials/03/06/test_security.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/lab-materials/03/06/test_security.py b/lab-materials/03/06/test_security.py index c79b82ea..06deb501 100644 --- a/lab-materials/03/06/test_security.py +++ b/lab-materials/03/06/test_security.py @@ -2,7 +2,7 @@ import requests import json -def test_security(endpoint, expected_model_sha, expected_serving_sha): +def test_security(endpoint, expected_serving_sha): response = requests.get(endpoint) if response.status_code==200: @@ -10,8 +10,6 @@ def test_security(endpoint, expected_model_sha, expected_serving_sha): else: raise Exception(f"Response status code is {response.status_code}") - if response_json["model_sha"] != expected_model_sha: - raise Exception(f"Model SHA has changed, model may have been tampered with") if response_json["sha"] != expected_serving_sha: raise Exception(f"Serving SHA has changed, endpoint may have been tampered with") @@ -19,12 +17,10 @@ def test_security(endpoint, expected_model_sha, expected_serving_sha): with open("security_result.json", "w") as f: json.dump({ - "model_sha_match": response_json["model_sha"] == expected_model_sha, "serving_sha_match": response_json["sha"] == expected_serving_sha }, f) if __name__ == '__main__': info_endpoint = "http://llm.ic-shared-llm.svc.cluster.local:3000" + "/info" - expected_model_sha = "b70aa86578567ba3301b21c8a27bea4e8f6d6d61" expected_serving_sha = "630800eed37b15c4b0c9eb8e6ab47212026720f7" - test_security(info_endpoint, expected_model_sha, expected_serving_sha) \ No newline at end of file + test_security(info_endpoint, expected_serving_sha) \ No newline at end of file From 7d9c2382dcad4a3423ea087dc7fcacab9ac2de74 Mon Sep 17 00:00:00 2001 From: RHRolun Date: Wed, 20 Mar 2024 11:13:05 +0100 Subject: [PATCH 10/58] Updated 03/06 to use VLLM and improved response time Removed streaming and fixed the response quality Updated requirements for VLLM Updated responsetime to hit the text generation endpoint --- lab-materials/03/06/llm_usage.py | 22 ++++++++--------- lab-materials/03/06/requirements.txt | 3 ++- lab-materials/03/06/test_response_quality.py | 2 +- lab-materials/03/06/test_responsetime.py | 25 +++++++++++--------- 4 files changed, 27 insertions(+), 25 deletions(-) diff --git a/lab-materials/03/06/llm_usage.py b/lab-materials/03/06/llm_usage.py index a4eccac5..63b7a155 100644 --- a/lab-materials/03/06/llm_usage.py +++ b/lab-materials/03/06/llm_usage.py @@ -1,30 +1,28 @@ import os -from langchain.llms import HuggingFaceTextGenInference +from langchain.llms import VLLMOpenAI from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain.evaluation import load_evaluator from langchain.embeddings import HuggingFaceEmbeddings -INFERENCE_SERVER_URL = "http://llm.ic-shared-llm.svc.cluster.local:3000" +INFERENCE_SERVER_URL = "http://vllm.llm-hosting.svc.cluster.local:8000" MAX_NEW_TOKENS = 512 -TOP_K = 10 TOP_P = 0.95 -TYPICAL_P = 0.95 TEMPERATURE = 0.01 -REPETITION_PENALTY = 1.03 +PRESENCE_PENALTY = 1.03 def infer_with_template(input_text, template): - llm = HuggingFaceTextGenInference( - inference_server_url=INFERENCE_SERVER_URL, - max_new_tokens=MAX_NEW_TOKENS, - top_k=TOP_K, + llm = VLLMOpenAI( + openai_api_key="EMPTY", + openai_api_base= f"{INFERENCE_SERVER_URL}/v1", + model_name="mistralai/Mistral-7B-Instruct-v0.2", + max_tokens=MAX_NEW_TOKENS, top_p=TOP_P, - typical_p=TYPICAL_P, temperature=TEMPERATURE, - repetition_penalty=REPETITION_PENALTY, - streaming=True, + presence_penalty=PRESENCE_PENALTY, + streaming=False, verbose=False, ) diff --git a/lab-materials/03/06/requirements.txt b/lab-materials/03/06/requirements.txt index 4fd64e8c..1fa8000c 100644 --- a/lab-materials/03/06/requirements.txt +++ b/lab-materials/03/06/requirements.txt @@ -1,3 +1,4 @@ langchain==0.0.340 text_generation==0.6.1 -sentence_transformers==2.2.2 \ No newline at end of file +sentence_transformers==2.2.2 +openai==1.13.3 \ No newline at end of file diff --git a/lab-materials/03/06/test_response_quality.py b/lab-materials/03/06/test_response_quality.py index cb4dc5f4..28a115b9 100644 --- a/lab-materials/03/06/test_response_quality.py +++ b/lab-materials/03/06/test_response_quality.py @@ -9,7 +9,7 @@ def test_response_quality(): with open('summary_template.txt') as f: template = f.read() - expected_response = """A car insurance claim has been initiated by John Smith for a recent accident involving his Honda Accord and a Ford Escape. The accident occurred on October 15, 2023, at approximately 2:30 PM, at the intersection of Elm Street and Maple Avenue, near Smith Park, in Springfield, Illinois. The other party ran a red light and collided with the front passenger side of John's vehicle, causing significant damage to both vehicles. John sustained no serious injuries, but there were witnesses to the accident, and he has photos of the scene and the other party's insurance information. He is requesting that the insurance company initiate a claim under his policy for the damages to his vehicle and has provided the necessary documentation and information.""" + expected_response = """On October 15, 2023, at around 2:30 PM, John Smith was involved in a car accident at the intersection of Elm Street and Maple Avenue in Springfield, Illinois (coordinates: 39.7476° N, 89.6960° W). He was driving his Honda Accord with a green light when a Ford Escape, which ran a red light, collided with the front passenger side of his vehicle. The accident occurred in overcast weather with light rain, and the road was wet. No serious injuries were reported, but both vehicles sustained significant damage. A police report was filed, and the officer's badge number is 12345. Witnesses to the accident include Sarah Johnson, Mark Williams, and Lisa Anderson, and their contact information has been provided. Photos of the accident scene, including the damage to both vehicles, traffic signals, and road conditions, have also been taken. John is requesting that a claim be initiated under his policy (ABC12345) for the damages to his vehicle and is asking for guidance on the claim process and required documentation.""" response = infer_with_template(input_text, template) print(f"Response: {response}") diff --git a/lab-materials/03/06/test_responsetime.py b/lab-materials/03/06/test_responsetime.py index aeb3c1e4..4e01c69d 100644 --- a/lab-materials/03/06/test_responsetime.py +++ b/lab-materials/03/06/test_responsetime.py @@ -1,20 +1,24 @@ -import os +from llm_usage import infer_with_template import requests import json +import time -max_response_time = 0.5 +max_response_time = 3 def send_request(endpoint): response = requests.get(endpoint) return response -def test_responsetime(endpoint): - response = send_request(endpoint) - - if response.status_code==200: - response_time = response.elapsed.total_seconds() - else: - raise Exception(f"Response status code is {response.status_code}") +def test_responsetime(): + TEMPLATE = """[INST] <> +Answer below truthfully and in less than 10 words: +<> +{silly_question} +[/INST]""" + + start = time.perf_counter() + response = infer_with_template("Who saw a saw saw a salsa?", TEMPLATE) + response_time = time.perf_counter() - start if response_time>max_response_time: raise Exception(f"Response took {response_time} which is greater than {max_response_time}") @@ -27,5 +31,4 @@ def test_responsetime(endpoint): }, f) if __name__ == '__main__': - health_endpoint = "http://llm.ic-shared-llm.svc.cluster.local:3000" + "/health" - test_responsetime(health_endpoint) \ No newline at end of file + test_responsetime() \ No newline at end of file From fdf6e3ee13e02062acd44cff7ceb4f1778e73040 Mon Sep 17 00:00:00 2001 From: rcarrata Date: Tue, 26 Mar 2024 23:36:28 +0100 Subject: [PATCH 11/58] minor fixes in lab1 --- .../modules/ROOT/pages/01-01-setting-stage.adoc | 8 ++++---- .../ROOT/pages/01-03-proposed-improvements.adoc | 16 ++++++++-------- .../pages/01-04-examples-from-prototype.adoc | 12 ++++++------ 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/content/modules/ROOT/pages/01-01-setting-stage.adoc b/content/modules/ROOT/pages/01-01-setting-stage.adoc index b39d2b92..7b15499a 100644 --- a/content/modules/ROOT/pages/01-01-setting-stage.adoc +++ b/content/modules/ROOT/pages/01-01-setting-stage.adoc @@ -6,13 +6,13 @@ In order to make this lab seem more realistic, we will be describing this imagin == Situation :slide: -* We all work for a large multi-national insurance company +* We all work for a large multi-national insurance company: ** Our company is in the midst of digital transformation ** It is looking at modernizing practices and leveraging new technologies * A small team was asked to: -** review the way insurance claims are currently being processed -** provide advice on potential improvements -* That team is currently very small (about 4 people) +** Review the way insurance claims are currently being processed +** Provide advice on potential improvements +* That team is currently very small (about 4 people): ** Findings will be presented to the board ** If they are convincing, the team will be granted the resources to implement the recommendations * The next sections in this chapter are the materials that were presented to the board diff --git a/content/modules/ROOT/pages/01-03-proposed-improvements.adoc b/content/modules/ROOT/pages/01-03-proposed-improvements.adoc index 32f4cc33..071c7fc6 100644 --- a/content/modules/ROOT/pages/01-03-proposed-improvements.adoc +++ b/content/modules/ROOT/pages/01-03-proposed-improvements.adoc @@ -6,15 +6,15 @@ include::_attributes.adoc[] * Recommendations: ** Progressive, step-wise, implementation ** Use various AI/ML tools and techniques to **assist** the adjusters -** (Completely replacing humans is extremely hard. Helping them is much easier) +*** (Completely replacing humans is extremely hard. Helping them is much easier) ** Provide support for low-level, repetitive tasks *** Point out areas in need of review *** Help with parsing and data extraction * Goals: -** bring average processing time from 7h/claim down to 2h/claim -** reduce human error by 80% -** improve fraud detection by 25% -* Requirements -** More precise measurements of performance -**** at baseline -**** after every change/improvement +** Bring average processing time from 7h/claim down to 2h/claim +** Reduce human error by 80% +** Improve fraud detection by 25% +* Requirements: +** More precise measurements of performance: +**** At baseline +**** After every change/improvement diff --git a/content/modules/ROOT/pages/01-04-examples-from-prototype.adoc b/content/modules/ROOT/pages/01-04-examples-from-prototype.adoc index 2a2f9d61..7b5172b5 100644 --- a/content/modules/ROOT/pages/01-04-examples-from-prototype.adoc +++ b/content/modules/ROOT/pages/01-04-examples-from-prototype.adoc @@ -5,42 +5,42 @@ The examples below are what we hope to achieve through our prototype version of == Using an LLM for text summarization -* Allows for faster reading by the claims adjuster +* Allows for faster reading by the claims adjuster: + [.bordershadow] image::01/proto-summary.png[test image] == Using an LLM for information extraction -* Extract key pieces of information for better population of database +* Extract key pieces of information for better population of database: + [.bordershadow] image::01/proto-info-extract.png[ info extraction] == Using an LLM for sentiment analysis -* Detect tone of text, and potentially act on it. +* Detect tone of text, and potentially act on it: + [.bordershadow] image::01/proto-sentiment-analysis.png[] == Using image recognition to frame vehicle(s) in pictures -* Analyse images provided by customer +* Analyse images provided by customer: + [.bordershadow] image::01/proto-car-recog.png[] == Using image recognition to detect damage -* Assessment of damage based on picture +* Assessment of damage based on picture: + [.bordershadow] image::01/proto-accident-grading.png[] == Web Application to review/process claims -* Have an application that ties in these tools together and enables users to process the incoming claims more efficiently +* Have an application that ties in these tools together and enables users to process the incoming claims more efficiently: + [.bordershadow] image::01/proto-claims-processing-app.png[] From d88817ecaec3e449eeee44d70956d1188ee6728a Mon Sep 17 00:00:00 2001 From: rcarrata Date: Tue, 26 Mar 2024 23:38:50 +0100 Subject: [PATCH 12/58] minor fixes in lab2 and update images from 2.8 --- .../images/02/02-02-pipelineserver01.png | Bin 101269 -> 64326 bytes .../images/02/02-02-pipelineserver02.png | Bin 113072 -> 85662 bytes .../images/02/02-02-pipelineserver03.png | Bin 24680 -> 13365 bytes .../ROOT/pages/02-01-getting-connected.adoc | 4 ++-- .../ROOT/pages/02-02-creating-project.adoc | 17 ++++++++--------- 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z4^|p8?K9H;Ff2Jh>9BJ{sv`uIO&;gvX+PM;^<|;w5j<>vl{v~52E&pDfzusE(Mm#> zc+Zb%Q#>&eq@N#65f=b+?x{!F(pI%*=Rrf^F^_`1V)0yxFhEw1HOt!4YiUzqY!Hmf z9mV!%|`x1(T8J!d_#iAdM%?s+Nu9>rEE||PubRl%URzx=*{mc2LQn; zRjBR)^Xz4|*!tHG^U_~@HpkgHFU$3qD%$1%uhKfu6N3Iz5&`dJq9kcSk=B>cXS^pK!+b1}uht57(3_AlIE8bCFBnLo4g19ANIBjHdrl-jFvS*Mq&eInzS_ zd5PYupC<>{@A*9HEdF8h3cunw|_duROc_m^k?D9Irq7 z9D$ZiB_`hs?*wjIL=upWqk zUsKlmZAky=RW;HdJF9*dYiLBPUN7~9QMi9gDjZfSRfy1|XdZ6KvAM~Aj!3w`?7SM| zM9ko|&vv$4U!(gNI3vw;|iT>xSV~-8tV+Z% G@c#godR49f diff --git a/content/modules/ROOT/pages/04-01-over-approach.adoc b/content/modules/ROOT/pages/04-01-over-approach.adoc index 0acb9043..839441fe 100644 --- a/content/modules/ROOT/pages/04-01-over-approach.adoc +++ b/content/modules/ROOT/pages/04-01-over-approach.adoc @@ -4,7 +4,14 @@ include::_attributes.adoc[] As part of this prototype, we investigated the use of the YOLOv8 model. This model can be found online at https://www.yolov8.com[yolov8,window=_blank] and downloaded. -We will first review its out-of-the-box capabilities. We will then fine-tune it to allow it to do more specialized work for us. Once we have a new, customized version of the model, we will deploy it in OpenShift AI Model Serving. Once that is done, we will send queries to it. +== Image Processing Sections + +. We will first review its out-of-the-box capabilities. +. We will then fine-tune it to allow it to do more specialized work for us. +. Once we have a new, customized version of the model, we will deploy it in OpenShift AI Model Serving. +. Once that is done, we will send queries to it. + +== Image Processing Out-of-the-box capabilities Let's start by looking at a YOLOv8 model and explore how it works on static car images. @@ -12,8 +19,5 @@ Let's start by looking at a YOLOv8 model and explore how it works on static car image::04/sample-car-image.png[car image] - In your running workbench, navigate to the folder `insurance-claim-processing/lab-materials/04`. -- Look for (and open) the notebook called `04-01-over-approach.ipynb` -- Execute the cells of the notebook, and ensure you understand what is happening - - - +- Look for (and open) the notebook called`04-01-over-approach.ipynb`. +- Execute the cells of the notebook, and ensure you understand what is happening. \ No newline at end of file diff --git a/content/modules/ROOT/pages/04-02-car-recog.adoc b/content/modules/ROOT/pages/04-02-car-recog.adoc index 6454cce9..697b6148 100644 --- a/content/modules/ROOT/pages/04-02-car-recog.adoc +++ b/content/modules/ROOT/pages/04-02-car-recog.adoc @@ -23,6 +23,4 @@ image::04/box-identified-cars.png[identify cars] - In your running workbench, navigate to the folder `insurance-claim-processing/lab-materials/04`. - Look for (and open) the notebook called `04-02-car-recognition.ipynb` -- Execute the cells of the notebook, and ensure you understand what is happening - - +- Execute the cells of the notebook, and ensure you understand what is happening \ No newline at end of file diff --git a/content/modules/ROOT/pages/04-04-accident-recog.adoc b/content/modules/ROOT/pages/04-04-accident-recog.adoc index df2e0816..9f4b3e43 100644 --- a/content/modules/ROOT/pages/04-04-accident-recog.adoc +++ b/content/modules/ROOT/pages/04-04-accident-recog.adoc @@ -3,9 +3,9 @@ include::_attributes.adoc[] Now that we have retrained our model we can test it against some sample images. -We have converted our model to onnx format and placed a copy within an S3 bucket. We will test this version against some sample test images. +We have converted our model to onnx format and placed a copy within an S3 bucket. We will test this version against some sample test images. -Using the re-trained model, we will see that we are able to identify a severe car crash with 88% certainty, like in the below picture. +Using the re-trained model, we will see that we are able to identify a severe car crash with 88% certainty, like in the below picture: [.bordershadow] image::04/retrained-model-results.png[retrained modelresults] diff --git a/content/modules/ROOT/pages/04-05-model-serving.adoc b/content/modules/ROOT/pages/04-05-model-serving.adoc index e7075386..d0b49101 100644 --- a/content/modules/ROOT/pages/04-05-model-serving.adoc +++ b/content/modules/ROOT/pages/04-05-model-serving.adoc @@ -2,13 +2,13 @@ include::_attributes.adoc[] . At this point, we need to deploy the model into RHOAI model serving. -. We will create another data connection... -.. with almost identical information -.. but we will change the bucket name from `userX` to `models` +. We will create another Data Connection... +.. With almost identical information +.. But we will change the bucket name from `userX` to `models` == Create a Data Connection -* In your Data Science project, create a data connection that refers to the shared minio. +* In your Data Science project, create a Data Connection that refers to the shared MinIO. * Here is the info you need to enter: ** Name: [.lines_space] @@ -54,9 +54,9 @@ image::04/model-data-connection.png[model connection] == Create a Model Server -In your project create a model server. +In your project create a model server: -* Click **Add model server** +* In the "Multi-model serving platform" type of model, click **Add model server**: + [.bordershadow] image::04/add-model-server.png[] @@ -118,7 +118,7 @@ image::04/add-model-server-config.png[] In your project, under **Models and model servers** select **Deploy model**. -* Click **Deploy model** +* Click **Deploy model**: + [.bordershadow] image::04/select-deploy-model.png[] From 8cf9176771a656c03a718ba23048ab631ee47c38 Mon Sep 17 00:00:00 2001 From: rcarrata Date: Tue, 26 Mar 2024 23:44:45 +0100 Subject: [PATCH 14/58] minor adjustments in lab6 --- .../modules/ROOT/pages/06-01-potential-imp-ref.adoc | 12 ++++++------ .../ROOT/pages/06-02-applicability-other.adoc | 8 ++++---- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc b/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc index 5303e421..47352e80 100644 --- a/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc +++ b/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc @@ -11,8 +11,8 @@ But this section of the lab is not meant for Improvements and Refinements **to t What we have shown in this lab is a very rough prototype, put together very quickly, in order to demonstrate: -* which improvements could be done -* how long it would take to do them +* Which improvements could be done +* How long it would take to do them In such a situation, it is common to go fast and make short-term decisions since there is no guarantee that this will become a real project. @@ -35,16 +35,16 @@ If you want to read what **we** thought could be improved, read below! (response ==== * We could have something that analyzes the images and checks for discrepancies with the customer data, such as: -** not the same make or color car as what is on file -** mismatch in license plate, if visible in the picture +** Not the same make or color car as what is on file. +** Mismatch in license plate, if visible in the picture. * We've only scratched the surface with gitops and pipelines here ** There was no performance testing done. If too many users connect at the same time, it might overwhelm either the app, the database, the LLM, etc... * Currently, most simple changes would probably end up breaking the application. And the person who, for example decides to change Mistral7B for Flan-T5-Small would not necessarily realize that. -** It would be critical to have multiple instances (Dev/Test/UAT/Prod) of the application +** It would be critical to have multiple instances (Dev/Test/UAT/Prod) of the application. ** It would also be required to have integration pipelines run in these environments to confirm that changes made do not break the overall application. * We could ask the LLM to start writing a response to the customer. ** It could be just to ask for missing details. -** or it could be to let them know whether the claim is accepted or denied +** or it could be to let them know whether the claim is accepted or denied. * However, to do this, the LLM would have to be aware of the policies that the insurance company uses to make those determinations. ** This could be an interesting use-case for the https://research.ibm.com/blog/retrieval-augmented-generation-RAG[RAG,window=_blank] approach. diff --git a/content/modules/ROOT/pages/06-02-applicability-other.adoc b/content/modules/ROOT/pages/06-02-applicability-other.adoc index 2d129a08..eff49bbf 100644 --- a/content/modules/ROOT/pages/06-02-applicability-other.adoc +++ b/content/modules/ROOT/pages/06-02-applicability-other.adoc @@ -20,17 +20,17 @@ So let's take some examples, and discuss what we **could** do using the same tec == Use cases 1. Healthcare - - medical imaging analysis - - patient record summarization + - Medical imaging analysis + - Patient record summarization 2. Automotive - Object recognition for autonomous vehicles 3. Agriculture - - crop disease analysis + - Crop disease analysis 4. Finance - - loan application paperwork analysis + - Loan application paperwork analysis 5. Manufacturing - Quality control through visual inspection of products From 4e6b9c0df3be6a3cea53e2fb45d923fad7f3b72c Mon Sep 17 00:00:00 2001 From: Guillaume Moutier Date: Tue, 2 Apr 2024 15:16:51 -0400 Subject: [PATCH 15/58] Update to vLLM and Ollama for model serving --- .../ic-rhoai-configuration/images-puller.yaml | 2 +- .../odhdashboardconfig.yaml | 1 - .../workbench-imagestream.yaml | 6 +- .../ic-shared-llm/deployment-flant5.yaml | 111 - .../ic-shared-llm/deployment-ollama.yaml | 81 + bootstrap/ic-shared-llm/deployment.yaml | 25 +- bootstrap/ic-shared-llm/kustomization.yaml | 5 +- bootstrap/ic-shared-llm/pvc-ollama.yaml | 18 + ...ervice-flant5.yaml => service-ollama.yaml} | 8 +- bootstrap/ic-shared-llm/service.yaml | 2 +- bootstrap/workbench-image/Pipfile | 47 +- bootstrap/workbench-image/Pipfile.lock | 2788 ++++++++--------- bootstrap/workbench-image/README.md | 6 +- bootstrap/workbench-image/requirements.txt | 251 +- content/modules/ROOT/nav.adoc | 2 +- .../pages/03-04-comparing-model-servers.adoc | 10 + .../ROOT/pages/03-04-comparing-models.adoc | 11 - lab-materials/02/02-04-validating.ipynb | 3 +- lab-materials/03/03-01-nb-llm-example.ipynb | 20 +- lab-materials/03/03-02-summarization.ipynb | 20 +- .../03/03-03-information-extraction.ipynb | 20 +- ...nb => 03-04-comparing-model-servers.ipynb} | 58 +- lab-materials/03/requirements.txt | 261 +- 23 files changed, 1801 insertions(+), 1955 deletions(-) delete mode 100644 bootstrap/ic-shared-llm/deployment-flant5.yaml create mode 100644 bootstrap/ic-shared-llm/deployment-ollama.yaml create mode 100644 bootstrap/ic-shared-llm/pvc-ollama.yaml rename bootstrap/ic-shared-llm/{service-flant5.yaml => service-ollama.yaml} (78%) create mode 100644 content/modules/ROOT/pages/03-04-comparing-model-servers.adoc delete mode 100644 content/modules/ROOT/pages/03-04-comparing-models.adoc rename lab-materials/03/{03-04-comparing-models.ipynb => 03-04-comparing-model-servers.ipynb} (83%) diff --git a/bootstrap/ic-rhoai-configuration/images-puller.yaml b/bootstrap/ic-rhoai-configuration/images-puller.yaml index b5e56abc..b6e30ee1 100644 --- a/bootstrap/ic-rhoai-configuration/images-puller.yaml +++ b/bootstrap/ic-rhoai-configuration/images-puller.yaml @@ -19,7 +19,7 @@ spec: spec: containers: - name: ic-workbench - image: image-registry.openshift-image-registry.svc:5000/redhat-ods-applications/ic-workbench:1.2 + image: image-registry.openshift-image-registry.svc:5000/redhat-ods-applications/ic-workbench:2.0 command: ["tail"] args: ["-f", "/dev/null"] resources: diff --git a/bootstrap/ic-rhoai-configuration/odhdashboardconfig.yaml b/bootstrap/ic-rhoai-configuration/odhdashboardconfig.yaml index 7c6d8449..c5786482 100644 --- a/bootstrap/ic-rhoai-configuration/odhdashboardconfig.yaml +++ b/bootstrap/ic-rhoai-configuration/odhdashboardconfig.yaml @@ -19,7 +19,6 @@ spec: enabled: true notebookNamespace: rhods-notebooks pvcSize: 5Gi - gpuSetting: '4' notebookSizes: - name: Standard resources: diff --git a/bootstrap/ic-rhoai-configuration/workbench-imagestream.yaml b/bootstrap/ic-rhoai-configuration/workbench-imagestream.yaml index 56153a03..4881da6f 100644 --- a/bootstrap/ic-rhoai-configuration/workbench-imagestream.yaml +++ b/bootstrap/ic-rhoai-configuration/workbench-imagestream.yaml @@ -25,17 +25,17 @@ spec: lookupPolicy: local: true tags: - - name: '1.2' + - name: '2.0' annotations: opendatahub.io/notebook-python-dependencies: >- - [{"name":"PyTorch","version":"2.1.2"},{"name":"Langchain","version":"0.0.353"},{"name":"Ultralytics","version":"8.0.232"},] + [{"name":"PyTorch","version":"2.1.2"},{"name":"Langchain","version":"0.1.12"},{"name":"Ultralytics","version":"8.1.29"},] opendatahub.io/notebook-software: >- [{"name":"CUDA","version":"12.1"},{"name":"Python","version":"v3.11"}] openshift.io/imported-from: quay.io/rh-aiservices-bu/rhoai-lab-insurance-claim-workbench from: kind: DockerImage name: >- - quay.io/rh-aiservices-bu/rhoai-lab-insurance-claim-workbench:1.2 + quay.io/rh-aiservices-bu/rhoai-lab-insurance-claim-workbench:2.0 importPolicy: importMode: Legacy referencePolicy: diff --git a/bootstrap/ic-shared-llm/deployment-flant5.yaml b/bootstrap/ic-shared-llm/deployment-flant5.yaml deleted file mode 100644 index c8757b59..00000000 --- a/bootstrap/ic-shared-llm/deployment-flant5.yaml +++ /dev/null @@ -1,111 +0,0 @@ -kind: Deployment -apiVersion: apps/v1 -metadata: - name: llm-flant5 - namespace: ic-shared-llm - labels: - app: llm-flant5 -spec: - replicas: 1 - selector: - matchLabels: - app: llm-flant5 - template: - metadata: - creationTimestamp: null - labels: - app: llm-flant5 - spec: - restartPolicy: Always - schedulerName: default-scheduler - affinity: {} - terminationGracePeriodSeconds: 120 - securityContext: {} - containers: - - resources: - limits: - cpu: '2' - memory: 2Gi - requests: - cpu: '1' - memory: 1Gi - readinessProbe: - httpGet: - path: /health - port: http - scheme: HTTP - timeoutSeconds: 5 - periodSeconds: 30 - successThreshold: 1 - failureThreshold: 3 - terminationMessagePath: /dev/termination-log - name: server - livenessProbe: - httpGet: - path: /health - port: http - scheme: HTTP - timeoutSeconds: 8 - periodSeconds: 100 - successThreshold: 1 - failureThreshold: 3 - env: - - name: MODEL_ID - value: google/flan-t5-small - - name: MAX_INPUT_LENGTH - value: '416' - - name: MAX_TOTAL_TOKENS - value: '512' - - name: HUGGINGFACE_HUB_CACHE - value: /models-cache - - name: PORT - value: '3000' - - name: HOSTNAME - value: '0.0.0.0' - securityContext: - capabilities: - drop: - - ALL - runAsNonRoot: true - allowPrivilegeEscalation: false - seccompProfile: - type: RuntimeDefault - ports: - - name: http - containerPort: 3000 - protocol: TCP - imagePullPolicy: IfNotPresent - startupProbe: - httpGet: - path: /health - port: http - scheme: HTTP - timeoutSeconds: 1 - periodSeconds: 30 - successThreshold: 1 - failureThreshold: 24 - initialDelaySeconds: 60 - volumeMounts: - - name: models-cache - mountPath: /models-cache - - name: shm - mountPath: /dev/shm - terminationMessagePolicy: File - image: 'ghcr.io/huggingface/text-generation-inference:1.3' - volumes: - - name: models-cache - persistentVolumeClaim: - claimName: models-cache - - name: shm - emptyDir: - medium: Memory - sizeLimit: 1Gi - dnsPolicy: ClusterFirst - tolerations: - - key: nvidia.com/gpu - operator: Exists - effect: NoSchedule - strategy: - type: Recreate - revisionHistoryLimit: 10 - progressDeadlineSeconds: 600 \ No newline at end of file diff --git a/bootstrap/ic-shared-llm/deployment-ollama.yaml b/bootstrap/ic-shared-llm/deployment-ollama.yaml new file mode 100644 index 00000000..d781f4b2 --- /dev/null +++ b/bootstrap/ic-shared-llm/deployment-ollama.yaml @@ -0,0 +1,81 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: ollama + namespace: ic-shared-llm +spec: + replicas: 1 + selector: + matchLabels: + app: ollama + strategy: + type: Recreate + template: + metadata: + labels: + app: ollama + spec: + containers: + - name: ollama + image: quay.io/rh-aiservices-bu/ollama-ubi9:0.1.30 + imagePullPolicy: Always + env: + - name: MODEL_ID + value: mistral:latest + - name: OLLAMA_MODELS + value: /.ollama/models + - name: OLLAMA_HOST + value: 0.0.0.0 + - name: OLLAMA_KEEP_ALIVE + value: '-1m' + securityContext: + capabilities: + drop: + - ALL + runAsNonRoot: true + allowPrivilegeEscalation: false + seccompProfile: + type: RuntimeDefault + ports: + - name: http + containerPort: 11434 + protocol: TCP + resources: + limits: + cpu: '4' + memory: 8Gi + requests: + cpu: '4' + memory: 8Gi + readinessProbe: + httpGet: + path: / + port: http + scheme: HTTP + timeoutSeconds: 5 + periodSeconds: 30 + successThreshold: 1 + failureThreshold: 3 + livenessProbe: + httpGet: + path: / + port: http + scheme: HTTP + timeoutSeconds: 8 + periodSeconds: 100 + successThreshold: 1 + failureThreshold: 3 + lifecycle: + postStart: + exec: + command: [ "/bin/sh", "-c", "/bin/ollama pull $MODEL_ID" ] + terminationMessagePath: /dev/termination-log + terminationMessagePolicy: File + volumeMounts: + - mountPath: /.ollama + name: ollama-cache + restartPolicy: Always + volumes: + - name: ollama-cache + persistentVolumeClaim: + claimName: ollama-cache \ No newline at end of file diff --git a/bootstrap/ic-shared-llm/deployment.yaml b/bootstrap/ic-shared-llm/deployment.yaml index ad3ae762..2f0c0097 100644 --- a/bootstrap/ic-shared-llm/deployment.yaml +++ b/bootstrap/ic-shared-llm/deployment.yaml @@ -49,19 +49,14 @@ spec: periodSeconds: 100 successThreshold: 1 failureThreshold: 3 - env: - - name: MODEL_ID - value: mistralai/Mistral-7B-Instruct-v0.2 - - name: MAX_INPUT_LENGTH - value: '4096' - - name: MAX_TOTAL_TOKENS - value: '8192' - - name: HUGGINGFACE_HUB_CACHE - value: /models-cache - - name: PORT - value: '3000' - - name: HOSTNAME - value: '0.0.0.0' + env: [] + args: [ + "--model", + "mistralai/Mistral-7B-Instruct-v0.2", + "--download-dir", + "/models-cache", + "--dtype", "float16", + "--max-model-len", "6144" ] securityContext: capabilities: drop: @@ -72,7 +67,7 @@ spec: type: RuntimeDefault ports: - name: http - containerPort: 3000 + containerPort: 8000 protocol: TCP imagePullPolicy: IfNotPresent startupProbe: @@ -91,7 +86,7 @@ spec: - name: shm mountPath: /dev/shm terminationMessagePolicy: File - image: 'ghcr.io/huggingface/text-generation-inference:1.3' + image: 'quay.io/rh-aiservices-bu/vllm-openai-ubi9:0.4.0' volumes: - name: models-cache persistentVolumeClaim: diff --git a/bootstrap/ic-shared-llm/kustomization.yaml b/bootstrap/ic-shared-llm/kustomization.yaml index 227f6a9d..d8cc7f81 100644 --- a/bootstrap/ic-shared-llm/kustomization.yaml +++ b/bootstrap/ic-shared-llm/kustomization.yaml @@ -11,8 +11,9 @@ resources: - fix-odf-config.yaml # wave 1 - pvc.yaml +- pvc-ollama.yaml - deployment.yaml - service.yaml -- deployment-flant5.yaml -- service-flant5.yaml +- deployment-ollama.yaml +- service-ollama.yaml # wave 2 diff --git a/bootstrap/ic-shared-llm/pvc-ollama.yaml b/bootstrap/ic-shared-llm/pvc-ollama.yaml new file mode 100644 index 00000000..aeccade9 --- /dev/null +++ b/bootstrap/ic-shared-llm/pvc-ollama.yaml @@ -0,0 +1,18 @@ +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: ollama-cache + namespace: ic-shared-llm + labels: + app: ollama + annotations: + argocd.argoproj.io/sync-wave: "0" +spec: + accessModes: + - ReadWriteMany + resources: + requests: + storage: 10Gi + storageClassName: ocs-storagecluster-cephfs + volumeMode: Filesystem \ No newline at end of file diff --git a/bootstrap/ic-shared-llm/service-flant5.yaml b/bootstrap/ic-shared-llm/service-ollama.yaml similarity index 78% rename from bootstrap/ic-shared-llm/service-flant5.yaml rename to bootstrap/ic-shared-llm/service-ollama.yaml index 08b3b8ba..9c4034bf 100644 --- a/bootstrap/ic-shared-llm/service-flant5.yaml +++ b/bootstrap/ic-shared-llm/service-ollama.yaml @@ -1,10 +1,10 @@ kind: Service apiVersion: v1 metadata: - name: llm-flant5 + name: ollama namespace: ic-shared-llm labels: - app: llm-flant5 + app: ollama spec: clusterIP: None ipFamilies: @@ -12,10 +12,10 @@ spec: ports: - name: http protocol: TCP - port: 3000 + port: 11434 targetPort: http type: ClusterIP ipFamilyPolicy: SingleStack sessionAffinity: None selector: - app: llm-flant5 \ No newline at end of file + app: ollama \ No newline at end of file diff --git a/bootstrap/ic-shared-llm/service.yaml b/bootstrap/ic-shared-llm/service.yaml index 61bf36c1..10a107a9 100644 --- a/bootstrap/ic-shared-llm/service.yaml +++ b/bootstrap/ic-shared-llm/service.yaml @@ -12,7 +12,7 @@ spec: ports: - name: http protocol: TCP - port: 3000 + port: 8000 targetPort: http type: ClusterIP ipFamilyPolicy: SingleStack diff --git a/bootstrap/workbench-image/Pipfile b/bootstrap/workbench-image/Pipfile index eea2e857..5fd23a0b 100644 --- a/bootstrap/workbench-image/Pipfile +++ b/bootstrap/workbench-image/Pipfile @@ -12,50 +12,49 @@ name = "pytorch" [packages] # Basic datascience packages and useful extensions -boto3 = "~=1.34.11" +boto3 = "~=1.34.65" kubernetes = "~=25.3.0" # Fixed by kfp@1.8.22 needed by elyra@3.15.0 -matplotlib = "~=3.8.2" -numpy = "~=1.26.3" -pandas = "~=2.1.4" -plotly = "~=5.18.0" -scikit-learn = "~=1.3.2" -scipy = "~=1.11.4" +matplotlib = "~=3.8.3" +numpy = "~=1.26.4" +pandas = "~=2.2.1" +plotly = "~=5.20.0" +scikit-learn = "~=1.4.1.post1" +scipy = "~=1.12.0" # PyTorch -torch = {version = "==2.1.2+cu121", index = "pytorch"} +torch = {version = "==2.2.1+cu121", index = "pytorch"} # LLM section - Langchain -langchain = "==0.0.353" -text_generation = "==0.6.1" -sentence_transformers = "==2.2.2" +langchain = "==0.1.12" +openai = "==1.14.1" # Image recognition section - YOLO -Flask = "~=3.0.0" +Flask = "~=3.0.2" gunicorn = "~=21.2.0" onnx = "~=1.15.0" -onnxruntime = "~=1.16.3" +onnxruntime = "~=1.17.1" opencv-python-headless = "~=4.9.0.80" -ultralytics = "~=8.0.232" +ultralytics = "~=8.1.29" # Base packages -pip = "~=23.3.2" -setuptools = "~=69.0.3" -wheel = "~=0.42.0" +pip = "~=24.0" +setuptools = "~=69.2.0" +wheel = "~=0.43.0" # JupyterLab packages elyra-pipeline-editor-extension = "~=3.15.0" elyra-python-editor-extension = "~=3.15.0" elyra-code-snippet-extension = "~=3.15.0" kfp-tekton = "<1.6.0" -jupyterlab = "~=3.6.6" +jupyterlab = "~=3.6.7" jupyter-bokeh = "~=3.0.7" -jupyter-resource-usage = "~=1.0.1" -jupyter-server-proxy = "~=4.0.0" -jupyter-server-terminals = "~=0.4.4" -jupyterlab-git = "~=0.42.0" -jupyterlab-lsp = "~=4.2.0" -jupyterlab-widgets = "~=3.0.8" +jupyter-resource-usage = "~=1.0.2" +jupyter-server-proxy = "~=4.1.2" +jupyter-server-terminals = "~=0.5.3" +jupyterlab-git = "~=0.44.0" +jupyterlab-lsp = "~=4.3.0" +jupyterlab-widgets = "~=3.0.10" nbdime = "~=3.2.1" [requires] diff --git a/bootstrap/workbench-image/Pipfile.lock b/bootstrap/workbench-image/Pipfile.lock index 8c9e0722..24c402bb 100644 --- a/bootstrap/workbench-image/Pipfile.lock +++ b/bootstrap/workbench-image/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "a3d10fa7c853c5e47dc6ff178d4a65094abe7889fdc2d44aaf2f93d12caed2ee" + "sha256": "a9e1e8bda663b18426aa532eadccb9ebfd5ae91c7f9dc4449263aca669cac0c2" }, "pipfile-spec": 6, "requires": { @@ -39,85 +39,85 @@ }, "aiohttp": { "hashes": [ - "sha256:02ab6006ec3c3463b528374c4cdce86434e7b89ad355e7bf29e2f16b46c7dd6f", - "sha256:04fa38875e53eb7e354ece1607b1d2fdee2d175ea4e4d745f6ec9f751fe20c7c", - "sha256:0b0a6a36ed7e164c6df1e18ee47afbd1990ce47cb428739d6c99aaabfaf1b3af", - "sha256:0d406b01a9f5a7e232d1b0d161b40c05275ffbcbd772dc18c1d5a570961a1ca4", - "sha256:0e49b08eafa4f5707ecfb321ab9592717a319e37938e301d462f79b4e860c32a", - "sha256:0e7ba7ff228c0d9a2cd66194e90f2bca6e0abca810b786901a569c0de082f489", - "sha256:11cb254e397a82efb1805d12561e80124928e04e9c4483587ce7390b3866d213", - "sha256:11ff168d752cb41e8492817e10fb4f85828f6a0142b9726a30c27c35a1835f01", - "sha256:176df045597e674fa950bf5ae536be85699e04cea68fa3a616cf75e413737eb5", - 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"sha256:8f3ceecd20d71067c7fd4c9e832d4e22584318983cabc013dbf3f70ea95de346", - "sha256:9fac43a7466eb73e64a9940ac9ed6369baa39b3bf221ae23493a9ec4d0022674", - "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397", - "sha256:adfdeab2da79ea2f76f87eb42a3ab1966a5313e5a69a0213a3cc06ef692b0e96", - "sha256:ba07e92756c97e3aca0912b5cbc4e5ad802f4557212788e72a72a47ff376950d", - "sha256:c07253088265c363d1ddf4b3cdb808d59a0468ecd017770ed716991620b8f77a", - "sha256:c9d8c8ec7efb887333cf71e328e39cffbf771d8f8f95d308ea4125bf5f90ba64", - "sha256:d00e6be486affb5781468457b21a6cbe848c33ef43f9ea4a73b4882e5f188a44", - "sha256:d429c2430c93b7903914e4db9a966c7f2b068dd2ebdd2fa9b9ce094c7d459f33" + "sha256:11e12fafb13372e18ca1bbf12d50f593e7280646687463dd47730fd4f4d5d257", + "sha256:2895bf0518361a9728773083908801a376743bcc37dfa252b801af8fd281b1ca", + "sha256:39cb34b1f1afbf23e9562501673e7146777efe95da24fab5707b88f7fb11649b", + "sha256:45cc09cc4c3b43fb10b59ef4d07318d9a3ecdbff03abd2e36e77b6dd9f9a5c85", + "sha256:4986db5e8880b0e6b7cd52ba36255d4793bf5cdc95bd6264806c233173b1ec0b", + "sha256:5369136a6474678e02426bd984466343924d1df8e2fd94a9b443cb7e3aa20d19", + "sha256:557ba04c816d23ce98a06e70af6abaa0485f6d94994ec78a42b05d1c03dcbd50", + "sha256:6a4db54edea37d1058b08947c789a2354ee02972ed5d1e0dca9b0b820f4c7f92", + "sha256:6a80d5cae8c265842c7419c560b9961561556c4361b297b4c431903f8c33b269", + "sha256:6a9c71a0b02985b4b0b6d14b875a6c86ddea2fdbebd0c9a720a806a8bbffc69f", + "sha256:6c47bdd680009b11c9ac382163e05ca43baf4127954c5f6d0250e7d772d2b80c", + "sha256:6e949a8a94186bced05b6508faa61b7adacc911115664ccb1923b9ad1f1ccf7b", + "sha256:73c7a935e62033bd5e8f0da33a4dcb763da2361921a69a5a95aaf6c93aa03a87", + "sha256:76ad8484379695f3fe46228962017a7e1337e9acadafed67eb20aabb175df98b", + "sha256:8350d4055505412a426b6ad8c521bc7d367d1637a762c70fdd93a3a0d595990b", + "sha256:87e9df830022488e235dd601478c15ad73a0389628588ba0b028cb74eb72fed8", + "sha256:8f9a542c979df62098ae9c58b19e03ad3df1c9d8c6895d96c0d51da17b243b1c", + "sha256:8fec441f5adcf81dd240a5fe78e3d83767999771630b5ddfc5867827a34fa3d3", + "sha256:9a03e16e55465177d416699331b0f3564138f1807ecc5f2de9d55d8f188d08c7", + "sha256:ba30a896166f0fee83183cec913298151b73164160d965af2e93a20bbd2ab605", + "sha256:c17d98799f32e3f55f181f19dd2021d762eb38fdd381b4a748b9f5a36738e935", + "sha256:c522392acc5e962bcac3b22b9592493ffd06d1fc5d755954e6be9f4990de932b", + "sha256:d0f9bd1fd919134d459d8abf954f63886745f4660ef66480b9d753a7c9d40927", + "sha256:d18d7f18a47de6863cd480734613502904611730f8def45fc52a5d97503e5101", + "sha256:d31481ccf4694a8416b681544c23bd271f5a123162ab603c7d7d2dd7dd901a07", + "sha256:e3e7065cbdabe6183ab82199d7a4f6b3ba0a438c5a512a68559846ccb76a78ec", + "sha256:eed82cdf79cd7f0232e2fdc1ad05b06a5e102a43e331f7d041e5f0e0a34a51c4", + "sha256:f970663fa4f7e80401a7b0cbeec00fa801bf0287d93d48368fc3e6fa32716245", + "sha256:f9b2fdca47dc855516b2d66eef3c39f2672cbf7e7a42e7e67ad2cbfcd6ba107d" ], - "markers": "python_version >= '3.7'", - "version": "==3.0.0" + "markers": "python_version >= '3.8'", + "version": "==4.0.0" }, "wcwidth": { "hashes": [ @@ -3913,20 +3753,20 @@ }, "wheel": { "hashes": [ - "sha256:177f9c9b0d45c47873b619f5b650346d632cdc35fb5e4d25058e09c9e581433d", - "sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8" + "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85", + "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81" ], "index": "pypi", - "markers": "python_version >= '3.7'", - "version": "==0.42.0" + "markers": "python_version >= '3.8'", + "version": "==0.43.0" }, "widgetsnbextension": { "hashes": [ - "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385", - "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175" + "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f", + "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc" ], "markers": "python_version >= '3.7'", - "version": "==4.0.9" + "version": "==4.0.10" }, "wrapt": { "hashes": [ @@ -4212,11 +4052,11 @@ }, "zipp": { "hashes": [ - "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31", - "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0" + "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b", + "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715" ], "markers": "python_version >= '3.8'", - "version": "==3.17.0" + "version": "==3.18.1" } }, "develop": {} diff --git a/bootstrap/workbench-image/README.md b/bootstrap/workbench-image/README.md index 740e31ca..907557fe 100644 --- a/bootstrap/workbench-image/README.md +++ b/bootstrap/workbench-image/README.md @@ -8,6 +8,10 @@ The `requirements.txt` file used to build the container image is generated in th - Create a Pipfile with the needed packages - Lock the packages (`pipenv lock`) -- Create the requirement.txt with `pipenv requirements > requirement.txt` +- Create the requirement.txt with `pipenv requirements > requirements.txt` - Edit `requirements.txt` and remove the package `opencv-python` (which has been "injected" as a dependency), it was not in the Pipfile, but keep `opencv-python-headless` - `requirements.txt` content will be installed when the container is built with the option `no-dependencies` to avoid unwanted packages to come back... This option is really needed, otherwise pip will install the standard opencv version again as a dependency... + +## Build the image + +`podman build -t rhoai-lab-insurance-claim-workbench:x.y .` diff --git a/bootstrap/workbench-image/requirements.txt b/bootstrap/workbench-image/requirements.txt index ce4dc405..3f0e8c38 100644 --- a/bootstrap/workbench-image/requirements.txt +++ b/bootstrap/workbench-image/requirements.txt @@ -2,46 +2,47 @@ --extra-index-url https://download.pytorch.org/whl/cu121/ absl-py==1.4.0; python_version >= '3.6' aiofiles==22.1.0; python_version >= '3.7' and python_version < '4.0' -aiohttp==3.9.1; python_version >= '3.8' +aiohttp==3.9.3; python_version >= '3.8' aiosignal==1.3.1; python_version >= '3.7' -aiosqlite==0.19.0; python_version >= '3.7' -anyio==4.2.0; python_version >= '3.8' +aiosqlite==0.20.0; python_version >= '3.8' +anyio==4.3.0; python_version >= '3.8' argon2-cffi==23.1.0; python_version >= '3.7' argon2-cffi-bindings==21.2.0; python_version >= '3.6' arrow==1.3.0; python_version >= '3.8' -astroid==3.0.2; python_full_version >= '3.8.0' +astroid==3.0.3; python_full_version >= '3.8.0' asttokens==2.4.1 attrs==23.2.0; python_version >= '3.7' autopep8==2.0.4; python_version >= '3.6' babel==2.14.0; python_version >= '3.7' -beautifulsoup4==4.12.2; python_full_version >= '3.6.0' -black==23.12.1; python_version >= '3.8' +beautifulsoup4==4.12.3; python_full_version >= '3.6.0' +black==24.3.0; python_version >= '3.8' bleach==6.1.0; python_version >= '3.8' blinker==1.7.0; python_version >= '3.8' -bokeh==3.3.2; python_version >= '3.9' -boto3==1.34.14; python_version >= '3.8' -botocore==1.34.14; python_version >= '3.8' -cachetools==5.3.2; python_version >= '3.7' -certifi==2023.11.17; python_version >= '3.6' +bokeh==3.4.0; python_version >= '3.9' +boto3==1.34.65; python_version >= '3.8' +botocore==1.34.65; python_version >= '3.8' +cachetools==5.3.3; python_version >= '3.7' +certifi==2024.2.2; python_version >= '3.6' cffi==1.16.0; python_version >= '3.8' charset-normalizer==3.3.2; python_full_version >= '3.7.0' click==8.1.7; python_version >= '3.7' cloudpickle==2.2.1; python_version >= '3.6' colorama==0.4.6; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' coloredlogs==15.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -comm==0.2.1; python_version >= '3.8' +comm==0.2.2; python_version >= '3.8' contourpy==1.2.0; python_version >= '3.9' -cryptography==41.0.7 +cryptography==42.0.5 cycler==0.12.1; python_version >= '3.8' -dataclasses-json==0.6.3; python_version >= '3.7' and python_version < '4.0' -debugpy==1.8.0; python_version >= '3.8' +dataclasses-json==0.6.4; python_version >= '3.7' and python_version < '4.0' +debugpy==1.8.1; python_version >= '3.8' decorator==5.1.1; python_version >= '3.5' defusedxml==0.7.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' deprecated==1.2.14; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' deprecation==2.1.0 -dill==0.3.7; python_version >= '3.11' -docstring-parser==0.15; python_version >= '3.6' and python_version < '4.0' -docstring-to-markdown==0.13; python_version >= '3.6' +dill==0.3.8; python_version >= '3.11' +distro==1.9.0; python_version >= '3.6' +docstring-parser==0.16; python_version >= '3.6' and python_version < '4.0' +docstring-to-markdown==0.15; python_version >= '3.6' elyra-code-snippet-extension==3.15.0 elyra-pipeline-editor-extension==3.15.0 elyra-python-editor-extension==3.15.0 @@ -50,120 +51,137 @@ entrypoints==0.4; python_version >= '3.6' executing==2.0.1; python_version >= '3.5' fastjsonschema==2.19.1 filelock==3.13.1; python_version >= '3.8' -fire==0.5.0 -flake8==6.1.0 -flask==3.0.0; python_version >= '3.8' -flatbuffers==23.5.26 -fonttools==4.47.0; python_version >= '3.8' +fire==0.6.0 +flake8==7.0.0 +flask==3.0.2; python_version >= '3.8' +flatbuffers==24.3.7 +fonttools==4.50.0; python_version >= '3.8' fqdn==1.5.1 frozenlist==1.4.1; python_version >= '3.8' -fsspec==2023.12.2; python_version >= '3.8' +fsspec==2024.3.1; python_version >= '3.8' gitdb==4.0.11; python_version >= '3.7' -gitpython==3.1.40; python_version >= '3.7' -google-api-core==2.15.0; python_version >= '3.7' +gitpython==3.1.42; python_version >= '3.7' +google-api-core==2.17.1; python_version >= '3.7' google-api-python-client==1.12.11; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' -google-auth==2.26.1; python_version >= '3.7' +google-auth==2.28.2; python_version >= '3.7' google-auth-httplib2==0.2.0 google-cloud-core==2.4.1; python_version >= '3.7' -google-cloud-storage==2.14.0; python_version >= '3.7' +google-cloud-storage==2.16.0; python_version >= '3.7' google-crc32c==1.5.0; python_version >= '3.7' google-resumable-media==2.7.0; python_version >= '3.7' -googleapis-common-protos==1.62.0; python_version >= '3.7' +googleapis-common-protos==1.63.0; python_version >= '3.7' greenlet==3.0.3; platform_machine == 'aarch64' or (platform_machine == 'ppc64le' or (platform_machine == 'x86_64' or (platform_machine == 'amd64' or (platform_machine == 'AMD64' or (platform_machine == 'win32' or platform_machine == 'WIN32'))))) gunicorn==21.2.0; python_version >= '3.5' +h11==0.14.0; python_version >= '3.7' +httpcore==1.0.4; python_version >= '3.8' httplib2==0.22.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' -huggingface-hub==0.20.2; python_full_version >= '3.8.0' +httpx==0.27.0; python_version >= '3.8' humanfriendly==10.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' idna==3.6; python_version >= '3.5' -importlib-metadata==7.0.1; python_version >= '3.8' -ipykernel==6.28.0; python_version >= '3.8' -ipython==8.20.0; python_version >= '3.10' +importlib-metadata==7.0.2; python_version >= '3.8' +ipykernel==6.29.3; python_version >= '3.8' +ipython==8.22.2; python_version >= '3.10' ipython-genutils==0.2.0 -ipywidgets==8.1.1; python_version >= '3.7' +ipywidgets==8.1.2; python_version >= '3.7' isoduration==20.11.0 isort==5.13.2; python_full_version >= '3.8.0' itsdangerous==2.1.2; python_version >= '3.7' jedi==0.19.1; python_version >= '3.6' -jinja2==3.1.2; python_version >= '3.7' +jinja2==3.1.3; python_version >= '3.7' jmespath==1.0.1; python_version >= '3.7' joblib==1.3.2; python_version >= '3.7' -json5==0.9.14 +json5==0.9.24; python_version >= '3.8' jsonpatch==1.33; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' jsonpointer==2.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' -jsonschema[format-nongpl]==4.20.0; python_version >= '3.8' +jsonschema[format-nongpl]==4.21.1; python_version >= '3.8' jsonschema-specifications==2023.12.1; python_version >= '3.8' jupyter-bokeh==3.0.7; python_version >= '3.8' jupyter-client==7.4.9; python_version >= '3.7' -jupyter-core==5.7.1; python_version >= '3.8' -jupyter-events==0.9.0; python_version >= '3.8' -jupyter-lsp==2.2.1; python_version >= '3.8' +jupyter-core==5.7.2; python_version >= '3.8' +jupyter-events==0.10.0; python_version >= '3.8' +jupyter-lsp==2.2.4; python_version >= '3.8' jupyter-packaging==0.12.3; python_version >= '3.7' -jupyter-resource-usage==1.0.1; python_version >= '3.8' -jupyter-server==2.12.2; python_version >= '3.8' +jupyter-resource-usage==1.0.2; python_version >= '3.8' +jupyter-server==2.13.0; python_version >= '3.8' jupyter-server-fileid==0.9.1; python_version >= '3.7' jupyter-server-mathjax==0.2.6; python_version >= '3.7' -jupyter-server-proxy==4.0.0; python_version >= '3.8' -jupyter-server-terminals==0.4.4; python_version >= '3.8' +jupyter-server-proxy==4.1.2; python_version >= '3.8' +jupyter-server-terminals==0.5.3; python_version >= '3.8' jupyter-server-ydoc==0.8.0; python_version >= '3.7' jupyter-ydoc==0.2.5; python_version >= '3.7' -jupyterlab==3.6.6; python_version >= '3.7' -jupyterlab-git==0.42.0; python_version >= '3.7' -jupyterlab-lsp==4.2.0; python_version >= '3.8' +jupyterlab==3.6.7; python_version >= '3.7' +jupyterlab-git==0.44.0; python_version >= '3.7' +jupyterlab-lsp==4.3.0; python_version >= '3.8' jupyterlab-pygments==0.3.0; python_version >= '3.8' -jupyterlab-server==2.25.2; python_version >= '3.8' -jupyterlab-widgets==3.0.9; python_version >= '3.7' +jupyterlab-server==2.25.4; python_version >= '3.8' +jupyterlab-widgets==3.0.10; python_version >= '3.7' kfp==1.8.22; python_full_version >= '3.6.1' kfp-pipeline-spec==0.1.16; python_full_version >= '3.7.0' kfp-server-api==1.8.5 -kfp-tekton==1.5.9; python_full_version >= '3.6.1' +kfp-tekton==1.5.10; python_full_version >= '3.6.1' kiwisolver==1.4.5; python_version >= '3.7' kubernetes==25.3.0; python_version >= '3.6' -langchain==0.0.353; python_version < '4.0' and python_full_version >= '3.8.1' -langchain-community==0.0.10; python_version < '4.0' and python_full_version >= '3.8.1' -langchain-core==0.1.8; python_version < '4.0' and python_full_version >= '3.8.1' -langsmith==0.0.77; python_version < '4.0' and python_full_version >= '3.8.1' -markupsafe==2.1.3; python_version >= '3.7' -marshmallow==3.20.1; python_version >= '3.8' -matplotlib==3.8.2; python_version >= '3.9' +langchain==0.1.12; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-community==0.0.28; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-core==0.1.32; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-text-splitters==0.0.1; python_version < '4.0' and python_full_version >= '3.8.1' +langsmith==0.1.29; python_version < '4.0' and python_full_version >= '3.8.1' +markupsafe==2.1.5; python_version >= '3.7' +marshmallow==3.21.1; python_version >= '3.8' +matplotlib==3.8.3; python_version >= '3.9' matplotlib-inline==0.1.6; python_version >= '3.5' mccabe==0.7.0 -minio==7.2.3 +minio==7.2.5 mistune==3.0.2; python_version >= '3.7' mpmath==1.3.0 -multidict==6.0.4; python_version >= '3.7' +multidict==6.0.5; python_version >= '3.7' mypy-extensions==1.0.0; python_version >= '3.5' nbclassic==1.0.0; python_version >= '3.7' -nbclient==0.9.0; python_full_version >= '3.8.0' -nbconvert==7.14.0; python_version >= '3.8' +nbclient==0.10.0; python_full_version >= '3.8.0' +nbconvert==7.16.2; python_version >= '3.8' nbdime==3.2.1; python_version >= '3.6' -nbformat==5.9.2; python_version >= '3.8' -nest-asyncio==1.5.8; python_version >= '3.5' +nbformat==5.10.3; python_version >= '3.8' +nest-asyncio==1.6.0; python_version >= '3.5' networkx==3.2.1; python_version >= '3.9' -nltk==3.8.1; python_version >= '3.7' notebook==6.5.6; python_version >= '3.7' -notebook-shim==0.2.3; python_version >= '3.7' -numpy==1.26.3; python_version >= '3.9' +notebook-shim==0.2.4; python_version >= '3.7' +numpy==1.26.4; python_version >= '3.9' +nvidia-cublas-cu12==12.1.3.1; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-cupti-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-runtime-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cudnn-cu12==8.9.2.26; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cufft-cu12==11.0.2.54; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-curand-cu12==10.3.2.106; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cusolver-cu12==11.4.5.107; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cusparse-cu12==12.1.0.106; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-nccl-cu12==2.19.3; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-nvjitlink-cu12==12.4.99; python_version >= '3' +nvidia-nvtx-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' oauthlib==3.2.2; python_version >= '3.6' onnx==1.15.0; python_version >= '3.8' -onnxruntime==1.16.3 +onnxruntime==1.17.1 +openai==1.14.1; python_full_version >= '3.7.1' +opencv-python==4.9.0.80; python_version >= '3.6' opencv-python-headless==4.9.0.80; python_version >= '3.6' -overrides==7.4.0; python_version >= '3.6' +orjson==3.9.15; python_version >= '3.8' +overrides==7.7.0; python_version >= '3.6' packaging==23.2; python_version >= '3.7' -pandas==2.1.4; python_version >= '3.9' -pandocfilters==1.5.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +pandas==2.2.1; python_version >= '3.9' +pandocfilters==1.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' papermill==2.5.0; python_version >= '3.8' parso==0.8.3; python_version >= '3.6' pathspec==0.12.1; python_version >= '3.8' pexpect==4.9.0 pillow==10.2.0; python_version >= '3.8' -pip==23.3.2; python_version >= '3.7' -platformdirs==4.1.0; python_version >= '3.8' -plotly==5.18.0; python_version >= '3.6' -pluggy==1.3.0; python_version >= '3.8' -prometheus-client==0.19.0; python_version >= '3.8' +pip==24.0; python_version >= '3.7' +platformdirs==4.2.0; python_version >= '3.8' +plotly==5.20.0; python_version >= '3.8' +pluggy==1.4.0; python_version >= '3.8' +prometheus-client==0.20.0; python_version >= '3.8' prompt-toolkit==3.0.43; python_full_version >= '3.7.0' protobuf==3.20.3; python_version >= '3.7' -psutil==5.9.7; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' +psutil==5.9.8; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' ptyprocess==0.7.0 pure-eval==0.2.2 py-cpuinfo==9.0.0 @@ -171,90 +189,83 @@ pyasn1==0.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, pyasn1-modules==0.3.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' pycodestyle==2.11.1; python_version >= '3.8' pycparser==2.21 -pycryptodome==3.19.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -pydantic==1.10.13; python_version >= '3.7' +pycryptodome==3.20.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' +pydantic==1.10.14; python_version >= '3.7' pydocstyle==6.3.0 -pyflakes==3.1.0 -pygithub==2.1.1; python_version >= '3.7' +pyflakes==3.2.0 +pygithub==2.2.0; python_version >= '3.7' pygments==2.17.2; python_version >= '3.7' pyjwt[crypto]==2.8.0; python_version >= '3.7' -pylint==3.0.3 +pylint==3.0.4 pynacl==1.5.0; python_version >= '3.6' -pyparsing==3.1.1; python_full_version >= '3.6.8' -python-dateutil==2.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +pyparsing==3.1.2; python_full_version >= '3.6.8' +python-dateutil==2.9.0.post0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' python-json-logger==2.0.7; python_version >= '3.6' python-lsp-jsonrpc==1.1.2; python_version >= '3.8' -python-lsp-server[all]==1.9.0; python_version >= '3.8' -pytoolconfig[global]==1.2.6; python_version >= '3.7' -pytz==2023.3.post1 +python-lsp-server[all]==1.10.1; python_version >= '3.8' +pytoolconfig[global]==1.3.1; python_version >= '3.8' +pytz==2024.1 pyyaml==6.0.1; python_version >= '3.6' pyzmq==24.0.1; python_version >= '3.6' -referencing==0.32.1; python_version >= '3.8' -regex==2023.12.25; python_version >= '3.7' +referencing==0.34.0; python_version >= '3.8' requests==2.31.0; python_version >= '3.7' -requests-oauthlib==1.3.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +requests-oauthlib==1.4.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' requests-toolbelt==0.10.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' rfc3339-validator==0.1.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' rfc3986-validator==0.1.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -rope==1.11.0 -rpds-py==0.16.2; python_version >= '3.8' +rope==1.12.0 +rpds-py==0.18.0; python_version >= '3.8' rsa==4.9; python_version >= '3.6' and python_version < '4' -s3transfer==0.10.0; python_version >= '3.8' -safetensors==0.4.1; python_version >= '3.7' -scikit-learn==1.3.2; python_version >= '3.8' -scipy==1.11.4; python_version >= '3.9' -seaborn==0.13.1; python_version >= '3.8' +s3transfer==0.10.1; python_version >= '3.8' +scikit-learn==1.4.1.post1; python_version >= '3.9' +scipy==1.12.0; python_version >= '3.9' +seaborn==0.13.2; python_version >= '3.8' send2trash==1.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -sentence-transformers==2.2.2; python_full_version >= '3.6.0' -sentencepiece==0.1.99 -setuptools==69.0.3; python_version >= '3.8' +setuptools==69.2.0; python_version >= '3.8' simpervisor==1.0.0; python_version >= '3.8' six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' smmap==5.0.1; python_version >= '3.7' -sniffio==1.3.0; python_version >= '3.7' +sniffio==1.3.1; python_version >= '3.7' snowballstemmer==2.2.0 soupsieve==2.5; python_version >= '3.8' -sqlalchemy==2.0.25; python_version >= '3.7' +sqlalchemy==2.0.28; python_version >= '3.7' stack-data==0.6.3 strip-hints==0.1.10 sympy==1.12; python_version >= '3.8' tabulate==0.9.0; python_version >= '3.7' tenacity==8.2.3; python_version >= '3.7' termcolor==2.4.0; python_version >= '3.8' -terminado==0.18.0; python_version >= '3.8' -text-generation==0.6.1; python_version >= '3.7' and python_version < '4.0' +terminado==0.18.1; python_version >= '3.8' thop==0.1.1.post2209072238 -threadpoolctl==3.2.0; python_version >= '3.8' +threadpoolctl==3.3.0; python_version >= '3.8' tinycss2==1.2.1; python_version >= '3.7' -tokenizers==0.15.0; python_version >= '3.7' tomli==2.0.1; python_version >= '3.7' -tomlkit==0.12.3; python_version >= '3.7' -torch==2.1.2+cu121 -torchvision==0.16.2; python_version >= '3.8' +tomlkit==0.12.4; python_version >= '3.7' +torch==2.2.1+cu121 +torchvision==0.17.1; python_version >= '3.8' tornado==6.4; python_version >= '3.8' -tqdm==4.66.1; python_version >= '3.7' -traitlets==5.14.1; python_version >= '3.8' -transformers==4.36.2; python_full_version >= '3.8.0' -triton==2.1.0; platform_system == 'Linux' and platform_machine == 'x86_64' +tqdm==4.66.2; python_version >= '3.7' +traitlets==5.14.2; python_version >= '3.8' +triton==2.2.0; python_version < '3.12' and platform_system == 'Linux' and platform_machine == 'x86_64' typer==0.9.0; python_version >= '3.6' -types-python-dateutil==2.8.19.20240106; python_version >= '3.8' -typing-extensions==4.9.0; python_version >= '3.8' +types-python-dateutil==2.9.0.20240316; python_version >= '3.8' +typing-extensions==4.10.0; python_version >= '3.8' typing-inspect==0.9.0 -tzdata==2023.4; python_version >= '2' +tzdata==2024.1; python_version >= '2' ujson==5.9.0; python_version >= '3.8' -ultralytics==8.0.237; python_version >= '3.8' +ultralytics==8.1.29; python_version >= '3.8' uri-template==1.3.0 uritemplate==3.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' urllib3==1.26.18; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' -watchdog==3.0.0; python_version >= '3.7' +watchdog==4.0.0; python_version >= '3.8' wcwidth==0.2.13 webcolors==1.13 webencodings==0.5.1 websocket-client==1.7.0; python_version >= '3.8' werkzeug==3.0.1; python_version >= '3.8' whatthepatch==1.0.5 -wheel==0.42.0; python_version >= '3.7' -widgetsnbextension==4.0.9; python_version >= '3.7' +wheel==0.43.0; python_version >= '3.8' +widgetsnbextension==4.0.10; python_version >= '3.7' wrapt==1.16.0; python_version >= '3.6' xyzservices==2023.10.1; python_version >= '3.8' y-py==0.6.2 @@ -262,4 +273,4 @@ yapf==0.40.2 yarl==1.9.4; python_version >= '3.7' yaspin==3.0.1; python_version >= '3.9' and python_version < '4.0' ypy-websocket==0.8.4; python_version >= '3.7' -zipp==3.17.0; python_version >= '3.8' +zipp==3.18.1; python_version >= '3.8' diff --git a/content/modules/ROOT/nav.adoc b/content/modules/ROOT/nav.adoc index 3108a968..5c75ca22 100644 --- a/content/modules/ROOT/nav.adoc +++ b/content/modules/ROOT/nav.adoc @@ -15,7 +15,7 @@ ** xref:03-01-notebook-based-llm.adoc[3.1 Notebook-Based LLM Example] ** xref:03-02-summarization.adoc[3.2 Text Summarization] ** xref:03-03-information-extractions.adoc[3.3 Information Extraction] -** xref:03-04-comparing-models.adoc[3.4 Comparing Models Accuracy] +** xref:03-04-comparing-model-servers.adoc[3.4 Comparing Model Servers] ** xref:03-05-prompt-engineering.adoc[3.5 Prompt Engineering Exercise (Optional)] ** xref:03-06-sanity-check.adoc[3.6 Sanity-check pipeline] diff --git a/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc b/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc new file mode 100644 index 00000000..d5518561 --- /dev/null +++ b/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc @@ -0,0 +1,10 @@ += Comparing Model Servers +include::_attributes.adoc[] + +So far, for this lab, we have used the model https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2[Mistral-7B Instruct v2,window=_blank]. Although lighter than other models, it is still quite heavy and we need a large GPU to run it. Would we get as good results with a quantized model (reduced in size) to run on CPU only? Let's try! + +In this exercise, we'll pitch our previous model against the itself but in a quantized version. The values of the 7 Billion parameters used in the model have been "rounded", therefore loosing precision, but saving space. As it consumes now less memory, we are able to load it directly in a container and run it on a CPU. We'll compare the results and see if the quantized model is good enough for our use case. + +From the `insurance-claim-processing/lab-materials/03` folder, please open the notebook called `03-04-comparing-model-servers.ipynb` and follow the instructions. + +When done, you can close the notebook and head to the next page. diff --git a/content/modules/ROOT/pages/03-04-comparing-models.adoc b/content/modules/ROOT/pages/03-04-comparing-models.adoc deleted file mode 100644 index 24469054..00000000 --- a/content/modules/ROOT/pages/03-04-comparing-models.adoc +++ /dev/null @@ -1,11 +0,0 @@ -= Comparing Models -include::_attributes.adoc[] - -So far, for this lab, we have used the model https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2[Mistral-7B Instruct v2,window=_blank]. Although lighter than other models, it is still quite heavy and we need a large GPU to run it. Would we get as good results with a smaller model? Let's try! - -In this exercise, we'll pitch it against a much smaller LLM called https://huggingface.co/google/flan-t5-small[flan-t5-small,window=_blank]. - - -From the `insurance-claim-processing/lab-materials/03` folder, please open the notebook called `03-04-comparing-models.ipynb` and follow the instructions. - -When done, you can close the notebook and head to the next page. diff --git a/lab-materials/02/02-04-validating.ipynb b/lab-materials/02/02-04-validating.ipynb index 56f39820..73a3c605 100644 --- a/lab-materials/02/02-04-validating.ipynb +++ b/lab-materials/02/02-04-validating.ipynb @@ -68,7 +68,8 @@ " (\"minio.ic-shared-minio.svc.cluster.local\", 9000, \"Minio\"),\n", " (\"gitea.gitea.svc.cluster.local\", 3000, \"Gitea\"),\n", " (\"claimdb.ic-shared-db.svc.cluster.local\", 5432, \"Postgres Database\"),\n", - " (\"llm.ic-shared-llm.svc.cluster.local\", 3000, \"LLM Service\"),\n", + " (\"llm.ic-shared-llm.svc.cluster.local\", 8000, \"LLM Service\"),\n", + " (\"ollama.ic-shared-llm.svc.cluster.local\", 11434, \"LLM Service-Ollama\"),\n", " (\"modelmesh-serving.ic-shared-img-det.svc.cluster.local\", 8033, \"ModelMesh\"),\n", " # Add more services as needed\n", " ]\n", diff --git a/lab-materials/03/03-01-nb-llm-example.ipynb b/lab-materials/03/03-01-nb-llm-example.ipynb index 23b4302f..1304d3e6 100644 --- a/lab-materials/03/03-01-nb-llm-example.ipynb +++ b/lab-materials/03/03-01-nb-llm-example.ipynb @@ -40,7 +40,7 @@ "from os import listdir\n", "from os.path import isfile, join\n", "from langchain.chains import LLMChain\n", - "from langchain.llms import HuggingFaceTextGenInference\n", + "from langchain_community.llms import VLLMOpenAI\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "from langchain.prompts import PromptTemplate" ] @@ -67,17 +67,17 @@ "outputs": [], "source": [ "# LLM Inference Server URL\n", - "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:3000/\"\n", + "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:8000\"\n", "\n", "# LLM definition\n", - "llm = HuggingFaceTextGenInference(\n", - " inference_server_url=inference_server_url,\n", - " max_new_tokens=512,\n", - " top_k=10,\n", - " top_p=0.95,\n", - " typical_p=0.95,\n", + "llm = VLLMOpenAI(\n", + " openai_api_key=\"EMPTY\",\n", + " openai_api_base= f\"{inference_server_url}/v1\",\n", + " model_name=\"mistralai/Mistral-7B-Instruct-v0.2\",\n", + " top_p=0.92,\n", " temperature=0.01,\n", - " repetition_penalty=1.03,\n", + " max_tokens=512,\n", + " presence_penalty=1.03,\n", " streaming=True,\n", " callbacks=[StreamingStdOutCallbackHandler()]\n", ")" @@ -190,7 +190,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/lab-materials/03/03-02-summarization.ipynb b/lab-materials/03/03-02-summarization.ipynb index ecb6cf24..8f56b7cf 100644 --- a/lab-materials/03/03-02-summarization.ipynb +++ b/lab-materials/03/03-02-summarization.ipynb @@ -38,7 +38,7 @@ "from os import listdir\n", "from os.path import isfile, join\n", "from langchain.chains import LLMChain\n", - "from langchain.llms import HuggingFaceTextGenInference\n", + "from langchain_community.llms import VLLMOpenAI\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "from langchain.prompts import PromptTemplate" ] @@ -63,17 +63,17 @@ "outputs": [], "source": [ "# LLM Inference Server URL\n", - "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:3000/\"\n", + "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:8000\"\n", "\n", "# LLM definition\n", - "llm = HuggingFaceTextGenInference(\n", - " inference_server_url=inference_server_url,\n", - " max_new_tokens=512,\n", - " top_k=10,\n", - " top_p=0.95,\n", - " typical_p=0.95,\n", + "llm = VLLMOpenAI(\n", + " openai_api_key=\"EMPTY\",\n", + " openai_api_base= f\"{inference_server_url}/v1\",\n", + " model_name=\"mistralai/Mistral-7B-Instruct-v0.2\",\n", + " top_p=0.92,\n", " temperature=0.01,\n", - " repetition_penalty=1.03,\n", + " max_tokens=512,\n", + " presence_penalty=1.03,\n", " streaming=True,\n", " callbacks=[StreamingStdOutCallbackHandler()]\n", ")" @@ -214,7 +214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/lab-materials/03/03-03-information-extraction.ipynb b/lab-materials/03/03-03-information-extraction.ipynb index ccc4c166..70a81abb 100644 --- a/lab-materials/03/03-03-information-extraction.ipynb +++ b/lab-materials/03/03-03-information-extraction.ipynb @@ -38,7 +38,7 @@ "from os import listdir\n", "from os.path import isfile, join\n", "from langchain.chains import LLMChain\n", - "from langchain.llms import HuggingFaceTextGenInference\n", + "from langchain_community.llms import VLLMOpenAI\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "from langchain.prompts import PromptTemplate" ] @@ -63,17 +63,17 @@ "outputs": [], "source": [ "# LLM Inference Server URL\n", - "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:3000/\"\n", + "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:8000\"\n", "\n", "# LLM definition\n", - "llm = HuggingFaceTextGenInference(\n", - " inference_server_url=inference_server_url,\n", - " max_new_tokens=512,\n", - " top_k=10,\n", - " top_p=0.95,\n", - " typical_p=0.95,\n", + "llm = VLLMOpenAI(\n", + " openai_api_key=\"EMPTY\",\n", + " openai_api_base= f\"{inference_server_url}/v1\",\n", + " model_name=\"mistralai/Mistral-7B-Instruct-v0.2\",\n", + " top_p=0.92,\n", " temperature=0.01,\n", - " repetition_penalty=1.03,\n", + " max_tokens=512,\n", + " presence_penalty=1.03,\n", " streaming=True,\n", " callbacks=[StreamingStdOutCallbackHandler()]\n", ")" @@ -228,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/lab-materials/03/03-04-comparing-models.ipynb b/lab-materials/03/03-04-comparing-model-servers.ipynb similarity index 83% rename from lab-materials/03/03-04-comparing-models.ipynb rename to lab-materials/03/03-04-comparing-model-servers.ipynb index f440f215..270a9fb4 100644 --- a/lab-materials/03/03-04-comparing-models.ipynb +++ b/lab-materials/03/03-04-comparing-model-servers.ipynb @@ -5,11 +5,11 @@ "id": "4ad2cc4e-31ec-4648-b0fe-6632f2bdbc36", "metadata": {}, "source": [ - "## Comparing models for our different tasks\n", + "## Comparing model servers for our different tasks\n", "\n", - "In this Notebook, we are going to use another model, Flan-T5-Small in parallel to Mistral-7B and see how it behaves.\n", + "In this Notebook, we are going to use the same model, Mistral-7B, but served with the Ollama runtime, only using a CPU.\n", "\n", - "Flan-T5-Small is indeed much smaller, runs without GPU and on only 1GB of RAM, but is it up to the task?" + "In order to run on CPU the model is quantized, so reduced in size, but is it up to the task?" ] }, { @@ -38,7 +38,7 @@ "from os import listdir\n", "from os.path import isfile, join\n", "from langchain.chains import LLMChain\n", - "from langchain.llms import HuggingFaceTextGenInference\n", + "from langchain_community.llms import VLLMOpenAI, Ollama\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "from langchain.prompts import PromptTemplate" ] @@ -62,18 +62,18 @@ }, "outputs": [], "source": [ - "# Main LLM Inference Server URL\n", - "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:3000/\"\n", + "# LLM Inference Server URL\n", + "inference_server_url = \"http://llm.ic-shared-llm.svc.cluster.local:8000\"\n", "\n", "# LLM definition\n", - "llm = HuggingFaceTextGenInference(\n", - " inference_server_url=inference_server_url,\n", - " max_new_tokens=512,\n", - " top_k=10,\n", - " top_p=0.95,\n", - " typical_p=0.95,\n", + "llm = VLLMOpenAI(\n", + " openai_api_key=\"EMPTY\",\n", + " openai_api_base= f\"{inference_server_url}/v1\",\n", + " model_name=\"mistralai/Mistral-7B-Instruct-v0.2\",\n", + " top_p=0.92,\n", " temperature=0.01,\n", - " repetition_penalty=1.03,\n", + " max_tokens=512,\n", + " presence_penalty=1.03,\n", " streaming=True,\n", " callbacks=[StreamingStdOutCallbackHandler()]\n", ")" @@ -88,19 +88,17 @@ }, "outputs": [], "source": [ - "# Flan-T5-Small LLM Inference Server URL\n", - "inference_server_url_flan_t5 = \"http://llm-flant5.ic-shared-llm.svc.cluster.local:3000/\"\n", + "# Ollama Inference Server URL\n", + "inference_server_url = \"http://ollama.ic-shared-llm.svc.cluster.local:11434\"\n", "\n", "# LLM definition\n", - "llm_flant5 = HuggingFaceTextGenInference(\n", - " inference_server_url=inference_server_url_flan_t5,\n", - " max_new_tokens=96,\n", - " top_k=10,\n", - " top_p=0.95,\n", - " typical_p=0.95,\n", + "llm_ollama = Ollama(\n", + " base_url=inference_server_url,\n", + " model=\"mistral\",\n", + " top_p=0.92,\n", " temperature=0.01,\n", - " repetition_penalty=1.03,\n", - " streaming=True,\n", + " num_predict=512,\n", + " repeat_penalty=1.03,\n", " callbacks=[StreamingStdOutCallbackHandler()]\n", ")" ] @@ -161,7 +159,7 @@ " prompt=PROMPT,\n", " verbose=False\n", " )\n", - "conversation_flant5 = LLMChain(llm=llm_flant5,\n", + "conversation_ollama = LLMChain(llm=llm_ollama,\n", " prompt=PROMPT,\n", " verbose=False\n", " )" @@ -201,7 +199,7 @@ "print(\"Original content:\")\n", "print(\"-----------------\")\n", "print(f\"Subject: {claims[filename]['subject']}\\nContent:\\n{claims[filename]['content']}\\n\\n\")\n", - "print('Analysis with Mistral-7B:')\n", + "print('Analysis with Mistral-7B served with vLLM using a GPU:')\n", "print(\"--------\")\n", "text_input = f\"Subject: {claims[filename]['subject']}\\nContent:\\n{claims[filename]['content']}\"\n", "sentiment_query = \"What is the sentiment of the person sending this claim?\"\n", @@ -214,18 +212,18 @@ "print(\"\\n- Time: \")\n", "conversation.predict(text=text_input, query=time_query);\n", "print(\"\\n\\n ----====----\\n\")\n", - "print('Analysis with Flan-T5-Small:')\n", + "print('Analysis with Mistral-7B served with Ollama using a CPU:')\n", "print(\"--------\")\n", "text_input = f\"Subject: {claims[filename]['subject']}\\nContent:\\n{claims[filename]['content']}\"\n", "sentiment_query = \"What is the sentiment of the person sending this claim?\"\n", "location_query = \"Where does the event the claim is related to happen?\"\n", "time_query = \"When does the event the claim is related to happen? If possible, specify the date and the time.\"\n", "print(f\"- Sentiment: \")\n", - "conversation_flant5.predict(text=text_input, query=sentiment_query);\n", + "conversation_ollama.predict(text=text_input, query=sentiment_query);\n", "print(\"\\n- Location: \")\n", - "conversation_flant5.predict(text=text_input, query=location_query);\n", + "conversation_ollama.predict(text=text_input, query=location_query);\n", "print(\"\\n- Time: \")\n", - "conversation_flant5.predict(text=text_input, query=time_query);\n", + "conversation_ollama.predict(text=text_input, query=time_query);\n", "print(\"\\n\\n ----====----\\n\")" ] }, @@ -258,7 +256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/lab-materials/03/requirements.txt b/lab-materials/03/requirements.txt index b75afe6c..3f0e8c38 100644 --- a/lab-materials/03/requirements.txt +++ b/lab-materials/03/requirements.txt @@ -2,168 +2,186 @@ --extra-index-url https://download.pytorch.org/whl/cu121/ absl-py==1.4.0; python_version >= '3.6' aiofiles==22.1.0; python_version >= '3.7' and python_version < '4.0' -aiohttp==3.9.1; python_version >= '3.8' +aiohttp==3.9.3; python_version >= '3.8' aiosignal==1.3.1; python_version >= '3.7' -aiosqlite==0.19.0; python_version >= '3.7' -anyio==4.2.0; python_version >= '3.8' +aiosqlite==0.20.0; python_version >= '3.8' +anyio==4.3.0; python_version >= '3.8' argon2-cffi==23.1.0; python_version >= '3.7' argon2-cffi-bindings==21.2.0; python_version >= '3.6' arrow==1.3.0; python_version >= '3.8' -astroid==3.0.2; python_full_version >= '3.8.0' +astroid==3.0.3; python_full_version >= '3.8.0' asttokens==2.4.1 attrs==23.2.0; python_version >= '3.7' autopep8==2.0.4; python_version >= '3.6' babel==2.14.0; python_version >= '3.7' -beautifulsoup4==4.12.2; python_full_version >= '3.6.0' -black==23.12.1; python_version >= '3.8' +beautifulsoup4==4.12.3; python_full_version >= '3.6.0' +black==24.3.0; python_version >= '3.8' bleach==6.1.0; python_version >= '3.8' blinker==1.7.0; python_version >= '3.8' -bokeh==3.3.2; python_version >= '3.9' -boto3==1.34.13; python_version >= '3.8' -botocore==1.34.13; python_version >= '3.8' -cachetools==5.3.2; python_version >= '3.7' -certifi==2023.11.17; python_version >= '3.6' +bokeh==3.4.0; python_version >= '3.9' +boto3==1.34.65; python_version >= '3.8' +botocore==1.34.65; python_version >= '3.8' +cachetools==5.3.3; python_version >= '3.7' +certifi==2024.2.2; python_version >= '3.6' cffi==1.16.0; python_version >= '3.8' charset-normalizer==3.3.2; python_full_version >= '3.7.0' click==8.1.7; python_version >= '3.7' cloudpickle==2.2.1; python_version >= '3.6' colorama==0.4.6; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' coloredlogs==15.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -comm==0.2.1; python_version >= '3.8' +comm==0.2.2; python_version >= '3.8' contourpy==1.2.0; python_version >= '3.9' -cryptography==41.0.7 +cryptography==42.0.5 cycler==0.12.1; python_version >= '3.8' -dataclasses-json==0.6.3; python_version >= '3.7' and python_version < '4.0' -debugpy==1.8.0; python_version >= '3.8' +dataclasses-json==0.6.4; python_version >= '3.7' and python_version < '4.0' +debugpy==1.8.1; python_version >= '3.8' decorator==5.1.1; python_version >= '3.5' defusedxml==0.7.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' deprecated==1.2.14; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' deprecation==2.1.0 -dill==0.3.7; python_version >= '3.11' -docstring-parser==0.15; python_version >= '3.6' and python_version < '4.0' -docstring-to-markdown==0.13; python_version >= '3.6' -elyra[kfp-tekton]==3.15.0; python_version >= '3.8' +dill==0.3.8; python_version >= '3.11' +distro==1.9.0; python_version >= '3.6' +docstring-parser==0.16; python_version >= '3.6' and python_version < '4.0' +docstring-to-markdown==0.15; python_version >= '3.6' +elyra-code-snippet-extension==3.15.0 +elyra-pipeline-editor-extension==3.15.0 +elyra-python-editor-extension==3.15.0 +elyra-server==3.15.0; python_version >= '3.8' entrypoints==0.4; python_version >= '3.6' executing==2.0.1; python_version >= '3.5' fastjsonschema==2.19.1 filelock==3.13.1; python_version >= '3.8' -fire==0.5.0 -flake8==6.1.0 -flask==3.0.0; python_version >= '3.8' -flatbuffers==23.5.26 -fonttools==4.47.0; python_version >= '3.8' +fire==0.6.0 +flake8==7.0.0 +flask==3.0.2; python_version >= '3.8' +flatbuffers==24.3.7 +fonttools==4.50.0; python_version >= '3.8' fqdn==1.5.1 frozenlist==1.4.1; python_version >= '3.8' -fsspec==2023.12.2; python_version >= '3.8' +fsspec==2024.3.1; python_version >= '3.8' gitdb==4.0.11; python_version >= '3.7' -gitpython==3.1.40; python_version >= '3.7' -google-api-core==2.15.0; python_version >= '3.7' +gitpython==3.1.42; python_version >= '3.7' +google-api-core==2.17.1; python_version >= '3.7' google-api-python-client==1.12.11; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' -google-auth==2.26.1; python_version >= '3.7' +google-auth==2.28.2; python_version >= '3.7' google-auth-httplib2==0.2.0 google-cloud-core==2.4.1; python_version >= '3.7' -google-cloud-storage==2.14.0; python_version >= '3.7' +google-cloud-storage==2.16.0; python_version >= '3.7' google-crc32c==1.5.0; python_version >= '3.7' google-resumable-media==2.7.0; python_version >= '3.7' -googleapis-common-protos==1.62.0; python_version >= '3.7' +googleapis-common-protos==1.63.0; python_version >= '3.7' greenlet==3.0.3; platform_machine == 'aarch64' or (platform_machine == 'ppc64le' or (platform_machine == 'x86_64' or (platform_machine == 'amd64' or (platform_machine == 'AMD64' or (platform_machine == 'win32' or platform_machine == 'WIN32'))))) gunicorn==21.2.0; python_version >= '3.5' +h11==0.14.0; python_version >= '3.7' +httpcore==1.0.4; python_version >= '3.8' httplib2==0.22.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' -huggingface-hub==0.20.2; python_full_version >= '3.8.0' +httpx==0.27.0; python_version >= '3.8' humanfriendly==10.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' idna==3.6; python_version >= '3.5' -importlib-metadata==7.0.1; python_version >= '3.8' -ipykernel==6.28.0; python_version >= '3.8' -ipython==8.19.0; python_version >= '3.10' +importlib-metadata==7.0.2; python_version >= '3.8' +ipykernel==6.29.3; python_version >= '3.8' +ipython==8.22.2; python_version >= '3.10' ipython-genutils==0.2.0 -ipywidgets==8.1.1; python_version >= '3.7' +ipywidgets==8.1.2; python_version >= '3.7' isoduration==20.11.0 isort==5.13.2; python_full_version >= '3.8.0' itsdangerous==2.1.2; python_version >= '3.7' jedi==0.19.1; python_version >= '3.6' -jinja2==3.1.2; python_version >= '3.7' +jinja2==3.1.3; python_version >= '3.7' jmespath==1.0.1; python_version >= '3.7' joblib==1.3.2; python_version >= '3.7' -json5==0.9.14 +json5==0.9.24; python_version >= '3.8' jsonpatch==1.33; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' jsonpointer==2.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6' -jsonschema[format-nongpl]==4.20.0; python_version >= '3.8' +jsonschema[format-nongpl]==4.21.1; python_version >= '3.8' jsonschema-specifications==2023.12.1; python_version >= '3.8' jupyter-bokeh==3.0.7; python_version >= '3.8' jupyter-client==7.4.9; python_version >= '3.7' -jupyter-core==5.7.0; python_version >= '3.8' -jupyter-events==0.9.0; python_version >= '3.8' -jupyter-lsp==2.2.1; python_version >= '3.8' +jupyter-core==5.7.2; python_version >= '3.8' +jupyter-events==0.10.0; python_version >= '3.8' +jupyter-lsp==2.2.4; python_version >= '3.8' jupyter-packaging==0.12.3; python_version >= '3.7' -jupyter-resource-usage==1.0.1; python_version >= '3.8' -jupyter-server==2.12.2; python_version >= '3.8' +jupyter-resource-usage==1.0.2; python_version >= '3.8' +jupyter-server==2.13.0; python_version >= '3.8' jupyter-server-fileid==0.9.1; python_version >= '3.7' jupyter-server-mathjax==0.2.6; python_version >= '3.7' -jupyter-server-proxy==4.0.0; python_version >= '3.8' -jupyter-server-terminals==0.5.1; python_version >= '3.8' +jupyter-server-proxy==4.1.2; python_version >= '3.8' +jupyter-server-terminals==0.5.3; python_version >= '3.8' jupyter-server-ydoc==0.8.0; python_version >= '3.7' jupyter-ydoc==0.2.5; python_version >= '3.7' -jupyterlab==3.6.6; python_version >= '3.7' -jupyterlab-git==0.42.0; python_version >= '3.7' -jupyterlab-lsp==4.2.0; python_version >= '3.8' +jupyterlab==3.6.7; python_version >= '3.7' +jupyterlab-git==0.44.0; python_version >= '3.7' +jupyterlab-lsp==4.3.0; python_version >= '3.8' jupyterlab-pygments==0.3.0; python_version >= '3.8' -jupyterlab-server==2.25.2; python_version >= '3.8' -jupyterlab-tabnine==0.0.24; python_version >= '3.6' -jupyterlab-widgets==3.0.9; python_version >= '3.7' +jupyterlab-server==2.25.4; python_version >= '3.8' +jupyterlab-widgets==3.0.10; python_version >= '3.7' kfp==1.8.22; python_full_version >= '3.6.1' kfp-pipeline-spec==0.1.16; python_full_version >= '3.7.0' kfp-server-api==1.8.5 -kfp-tekton==1.9.2; python_full_version >= '3.8.0' -kfp-tekton-server-api==1.8.0rc8 +kfp-tekton==1.5.10; python_full_version >= '3.6.1' kiwisolver==1.4.5; python_version >= '3.7' kubernetes==25.3.0; python_version >= '3.6' -langchain==0.0.353; python_version < '4.0' and python_full_version >= '3.8.1' -langchain-community==0.0.8; python_version < '4.0' and python_full_version >= '3.8.1' -langchain-core==0.1.6; python_version < '4.0' and python_full_version >= '3.8.1' -langsmith==0.0.77; python_version < '4.0' and python_full_version >= '3.8.1' -markupsafe==2.1.3; python_version >= '3.7' -marshmallow==3.20.1; python_version >= '3.8' -matplotlib==3.8.2; python_version >= '3.9' +langchain==0.1.12; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-community==0.0.28; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-core==0.1.32; python_version < '4.0' and python_full_version >= '3.8.1' +langchain-text-splitters==0.0.1; python_version < '4.0' and python_full_version >= '3.8.1' +langsmith==0.1.29; python_version < '4.0' and python_full_version >= '3.8.1' +markupsafe==2.1.5; python_version >= '3.7' +marshmallow==3.21.1; python_version >= '3.8' +matplotlib==3.8.3; python_version >= '3.9' matplotlib-inline==0.1.6; python_version >= '3.5' mccabe==0.7.0 -minio==7.2.3 +minio==7.2.5 mistune==3.0.2; python_version >= '3.7' mpmath==1.3.0 -multidict==6.0.4; python_version >= '3.7' +multidict==6.0.5; python_version >= '3.7' mypy-extensions==1.0.0; python_version >= '3.5' nbclassic==1.0.0; python_version >= '3.7' -nbclient==0.9.0; python_full_version >= '3.8.0' -nbconvert==7.14.0; python_version >= '3.8' +nbclient==0.10.0; python_full_version >= '3.8.0' +nbconvert==7.16.2; python_version >= '3.8' nbdime==3.2.1; python_version >= '3.6' -nbformat==5.9.2; python_version >= '3.8' -nbgitpuller==1.2.0 -nest-asyncio==1.5.8; python_version >= '3.5' +nbformat==5.10.3; python_version >= '3.8' +nest-asyncio==1.6.0; python_version >= '3.5' networkx==3.2.1; python_version >= '3.9' -nltk==3.8.1; python_version >= '3.7' notebook==6.5.6; python_version >= '3.7' -notebook-shim==0.2.3; python_version >= '3.7' -numpy==1.26.3; python_version >= '3.9' +notebook-shim==0.2.4; python_version >= '3.7' +numpy==1.26.4; python_version >= '3.9' +nvidia-cublas-cu12==12.1.3.1; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-cupti-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cuda-runtime-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cudnn-cu12==8.9.2.26; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cufft-cu12==11.0.2.54; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-curand-cu12==10.3.2.106; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cusolver-cu12==11.4.5.107; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-cusparse-cu12==12.1.0.106; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-nccl-cu12==2.19.3; platform_system == 'Linux' and platform_machine == 'x86_64' +nvidia-nvjitlink-cu12==12.4.99; python_version >= '3' +nvidia-nvtx-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' oauthlib==3.2.2; python_version >= '3.6' onnx==1.15.0; python_version >= '3.8' -onnxruntime==1.16.3 +onnxruntime==1.17.1 +openai==1.14.1; python_full_version >= '3.7.1' +opencv-python==4.9.0.80; python_version >= '3.6' opencv-python-headless==4.9.0.80; python_version >= '3.6' -overrides==7.4.0; python_version >= '3.6' +orjson==3.9.15; python_version >= '3.8' +overrides==7.7.0; python_version >= '3.6' packaging==23.2; python_version >= '3.7' -pandas==2.1.4; python_version >= '3.9' -pandocfilters==1.5.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +pandas==2.2.1; python_version >= '3.9' +pandocfilters==1.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' papermill==2.5.0; python_version >= '3.8' parso==0.8.3; python_version >= '3.6' pathspec==0.12.1; python_version >= '3.8' pexpect==4.9.0 pillow==10.2.0; python_version >= '3.8' -pip==23.3.2; python_version >= '3.7' -platformdirs==4.1.0; python_version >= '3.8' -plotly==5.18.0; python_version >= '3.6' -pluggy==1.3.0; python_version >= '3.8' -prometheus-client==0.19.0; python_version >= '3.8' +pip==24.0; python_version >= '3.7' +platformdirs==4.2.0; python_version >= '3.8' +plotly==5.20.0; python_version >= '3.8' +pluggy==1.4.0; python_version >= '3.8' +prometheus-client==0.20.0; python_version >= '3.8' prompt-toolkit==3.0.43; python_full_version >= '3.7.0' protobuf==3.20.3; python_version >= '3.7' -psutil==5.9.7; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' +psutil==5.9.8; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' ptyprocess==0.7.0 pure-eval==0.2.2 py-cpuinfo==9.0.0 @@ -171,90 +189,83 @@ pyasn1==0.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, pyasn1-modules==0.3.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' pycodestyle==2.11.1; python_version >= '3.8' pycparser==2.21 -pycryptodome==3.19.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -pydantic==1.10.13; python_version >= '3.7' +pycryptodome==3.20.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' +pydantic==1.10.14; python_version >= '3.7' pydocstyle==6.3.0 -pyflakes==3.1.0 -pygithub==2.1.1; python_version >= '3.7' +pyflakes==3.2.0 +pygithub==2.2.0; python_version >= '3.7' pygments==2.17.2; python_version >= '3.7' pyjwt[crypto]==2.8.0; python_version >= '3.7' -pylint==3.0.3 +pylint==3.0.4 pynacl==1.5.0; python_version >= '3.6' -pyparsing==3.1.1; python_full_version >= '3.6.8' -python-dateutil==2.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +pyparsing==3.1.2; python_full_version >= '3.6.8' +python-dateutil==2.9.0.post0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' python-json-logger==2.0.7; python_version >= '3.6' python-lsp-jsonrpc==1.1.2; python_version >= '3.8' -python-lsp-server[all]==1.9.0; python_version >= '3.8' -pytoolconfig[global]==1.2.6; python_version >= '3.7' -pytz==2023.3.post1 +python-lsp-server[all]==1.10.1; python_version >= '3.8' +pytoolconfig[global]==1.3.1; python_version >= '3.8' +pytz==2024.1 pyyaml==6.0.1; python_version >= '3.6' pyzmq==24.0.1; python_version >= '3.6' -referencing==0.32.1; python_version >= '3.8' -regex==2023.12.25; python_version >= '3.7' +referencing==0.34.0; python_version >= '3.8' requests==2.31.0; python_version >= '3.7' -requests-oauthlib==1.3.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' +requests-oauthlib==1.4.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' requests-toolbelt==0.10.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' rfc3339-validator==0.1.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' rfc3986-validator==0.1.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -rope==1.11.0 -rpds-py==0.16.2; python_version >= '3.8' +rope==1.12.0 +rpds-py==0.18.0; python_version >= '3.8' rsa==4.9; python_version >= '3.6' and python_version < '4' -s3transfer==0.10.0; python_version >= '3.8' -safetensors==0.4.1; python_version >= '3.7' -scikit-learn==1.3.2; python_version >= '3.8' -scipy==1.11.4; python_version >= '3.9' -seaborn==0.13.1; python_version >= '3.8' +s3transfer==0.10.1; python_version >= '3.8' +scikit-learn==1.4.1.post1; python_version >= '3.9' +scipy==1.12.0; python_version >= '3.9' +seaborn==0.13.2; python_version >= '3.8' send2trash==1.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' -sentence-transformers==2.2.2; python_full_version >= '3.6.0' -sentencepiece==0.1.99 -setuptools==69.0.3; python_version >= '3.8' +setuptools==69.2.0; python_version >= '3.8' simpervisor==1.0.0; python_version >= '3.8' six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' smmap==5.0.1; python_version >= '3.7' -sniffio==1.3.0; python_version >= '3.7' +sniffio==1.3.1; python_version >= '3.7' snowballstemmer==2.2.0 soupsieve==2.5; python_version >= '3.8' -sqlalchemy==2.0.25; python_version >= '3.7' +sqlalchemy==2.0.28; python_version >= '3.7' stack-data==0.6.3 strip-hints==0.1.10 sympy==1.12; python_version >= '3.8' tabulate==0.9.0; python_version >= '3.7' tenacity==8.2.3; python_version >= '3.7' termcolor==2.4.0; python_version >= '3.8' -terminado==0.18.0; python_version >= '3.8' -text-generation==0.6.1; python_version >= '3.7' and python_version < '4.0' +terminado==0.18.1; python_version >= '3.8' thop==0.1.1.post2209072238 -threadpoolctl==3.2.0; python_version >= '3.8' +threadpoolctl==3.3.0; python_version >= '3.8' tinycss2==1.2.1; python_version >= '3.7' -tokenizers==0.15.0; python_version >= '3.7' tomli==2.0.1; python_version >= '3.7' -tomlkit==0.12.3; python_version >= '3.7' -torch==2.1.2+cu121 -torchvision==0.16.2; python_version >= '3.8' +tomlkit==0.12.4; python_version >= '3.7' +torch==2.2.1+cu121 +torchvision==0.17.1; python_version >= '3.8' tornado==6.4; python_version >= '3.8' -tqdm==4.66.1; python_version >= '3.7' -traitlets==5.14.1; python_version >= '3.8' -transformers==4.36.2; python_full_version >= '3.8.0' -triton==2.1.0; platform_system == 'Linux' and platform_machine == 'x86_64' +tqdm==4.66.2; python_version >= '3.7' +traitlets==5.14.2; python_version >= '3.8' +triton==2.2.0; python_version < '3.12' and platform_system == 'Linux' and platform_machine == 'x86_64' typer==0.9.0; python_version >= '3.6' -types-python-dateutil==2.8.19.14 -typing-extensions==4.9.0; python_version >= '3.8' +types-python-dateutil==2.9.0.20240316; python_version >= '3.8' +typing-extensions==4.10.0; python_version >= '3.8' typing-inspect==0.9.0 -tzdata==2023.4; python_version >= '2' +tzdata==2024.1; python_version >= '2' ujson==5.9.0; python_version >= '3.8' -ultralytics==8.0.235; python_version >= '3.8' +ultralytics==8.1.29; python_version >= '3.8' uri-template==1.3.0 uritemplate==3.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' urllib3==1.26.18; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5' -watchdog==3.0.0; python_version >= '3.7' -wcwidth==0.2.12 +watchdog==4.0.0; python_version >= '3.8' +wcwidth==0.2.13 webcolors==1.13 webencodings==0.5.1 websocket-client==1.7.0; python_version >= '3.8' werkzeug==3.0.1; python_version >= '3.8' whatthepatch==1.0.5 -wheel==0.42.0; python_version >= '3.7' -widgetsnbextension==4.0.9; python_version >= '3.7' +wheel==0.43.0; python_version >= '3.8' +widgetsnbextension==4.0.10; python_version >= '3.7' wrapt==1.16.0; python_version >= '3.6' xyzservices==2023.10.1; python_version >= '3.8' y-py==0.6.2 @@ -262,4 +273,4 @@ yapf==0.40.2 yarl==1.9.4; python_version >= '3.7' yaspin==3.0.1; python_version >= '3.9' and python_version < '4.0' ypy-websocket==0.8.4; python_version >= '3.7' -zipp==3.17.0; python_version >= '3.8' +zipp==3.18.1; python_version >= '3.8' From 22ad6e69bb90447dd00e10e0460f92baf0980fff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Roberto=20Carratal=C3=A1?= Date: Fri, 5 Apr 2024 15:24:40 +0200 Subject: [PATCH 16/58] Better leverage antora variables (#170) * add antora rhoai, argocd and ocp vars * added lab vars * minor typo fix --- content/antora.yml | 9 ++++++++- .../modules/ROOT/pages/01-01-setting-stage.adoc | 2 +- .../ROOT/pages/02-01-getting-connected.adoc | 8 ++++---- .../ROOT/pages/02-02-creating-project.adoc | 2 +- .../ROOT/pages/02-03-creating-workbench.adoc | 2 +- .../modules/ROOT/pages/02-04-validating-env.adoc | 2 +- .../pages/03-04-comparing-model-servers.adoc | 2 +- .../ROOT/pages/03-05-prompt-engineering.adoc | 4 ++-- .../modules/ROOT/pages/03-06-sanity-check.adoc | 2 +- .../modules/ROOT/pages/04-01-over-approach.adoc | 2 +- .../ROOT/pages/04-03-model-retraining.adoc | 2 +- .../modules/ROOT/pages/04-05-model-serving.adoc | 2 +- .../modules/ROOT/pages/05-01-application.adoc | 12 ++++++------ .../ROOT/pages/05-02-openshift-terminal.adoc | 12 ++++++------ .../pages/05-03-web-app-deploy-application.adoc | 16 ++++++++-------- .../modules/ROOT/pages/05-05-process-claims.adoc | 4 ++-- .../ROOT/pages/06-01-potential-imp-ref.adoc | 8 ++++---- .../ROOT/pages/06-02-applicability-other.adoc | 2 +- content/modules/ROOT/pages/07-01-end-of-lab.adoc | 4 ++-- content/modules/ROOT/pages/index.adoc | 10 +++++----- 20 files changed, 57 insertions(+), 50 deletions(-) diff --git a/content/antora.yml b/content/antora.yml index 13ae18c7..6cc8a5dd 100644 --- a/content/antora.yml +++ b/content/antora.yml @@ -4,7 +4,6 @@ version: ~ nav: - modules/ROOT/nav.adoc - asciidoc: attributes: release-version: dev @@ -27,6 +26,14 @@ asciidoc: openshift_cluster_ingress_domain: PLACEHOLDER-URL.com rhoai_dashboard_url: https://rhods-dashboard-redhat-ods-applications.{openshift_cluster_ingress_domain}/ login_command: oc login --insecure-skip-tls-verify=false -u userX -p openshift https://api.MYCLUSTER.com:6443" + rhoai: "Red Hat OpenShift AI" + rhoai-short: "RHOAI" + ocp: "OpenShift Container Platform" + ocp-short: "OpenShift" + argocd: "ArgoCD" + ocp-gitops: "OpenShift GitOps" + ic-lab: "Lab" + ic: "Insurance Claim Processing Lab" extensions: - ./content/lib/tab-block.js diff --git a/content/modules/ROOT/pages/01-01-setting-stage.adoc b/content/modules/ROOT/pages/01-01-setting-stage.adoc index 7b15499a..69d7f296 100644 --- a/content/modules/ROOT/pages/01-01-setting-stage.adoc +++ b/content/modules/ROOT/pages/01-01-setting-stage.adoc @@ -1,7 +1,7 @@ = Setting the stage include::_attributes.adoc[] -In order to make this lab seem more realistic, we will be describing this imaginary scenario. +In order to make this {ic} seem more realistic, we will be describing this imaginary scenario. == Situation :slide: diff --git a/content/modules/ROOT/pages/02-01-getting-connected.adoc b/content/modules/ROOT/pages/02-01-getting-connected.adoc index ffa51ee7..0fa5567b 100644 --- a/content/modules/ROOT/pages/02-01-getting-connected.adoc +++ b/content/modules/ROOT/pages/02-01-getting-connected.adoc @@ -1,7 +1,7 @@ = Getting connected include::_attributes.adoc[] -For the purposes of this training session, we have provisioned a single OpenShift cluster, with OpenShift AI deployed on it. +For the purposes of this training session, we have provisioned a single {ocp} cluster, with {rhoai} deployed on it. Each person attending this lab will have a unique user account in which to do their work. @@ -14,7 +14,7 @@ If you are using the customized version of the instructions, the information bel In a new window or tab, open the following URL and log in: -* The Red Hat OpenShift AI Dashboard URL for our shared environment: +* The {rhoai} Dashboard URL for our shared environment: ** https://rhods-dashboard-redhat-ods-applications.{openshift_cluster_ingress_domain}/[https://rhods-dashboard-redhat-ods-applications.{openshift_cluster_ingress_domain}/,window=_blank] * Enter your credentials (as detailed above) * The result should look like: @@ -33,9 +33,9 @@ image::02/02-01-login-scary.png[] [.bordershadow] image::02/02-01-rhoai-front-page.png[] -If you got this far and saw all that, congratulations, you properly connected to the OpenShift AI Dashboard Application! +If you got this far and saw all that, congratulations, you properly connected to the {rhoai} Dashboard Application! -We are now ready to start the lab. +We are now ready to start the {ic}. == Getting Support during RH1 In-Person Labs diff --git a/content/modules/ROOT/pages/02-02-creating-project.adoc b/content/modules/ROOT/pages/02-02-creating-project.adoc index d56e764e..b91a729c 100644 --- a/content/modules/ROOT/pages/02-02-creating-project.adoc +++ b/content/modules/ROOT/pages/02-02-creating-project.adoc @@ -22,7 +22,7 @@ The instructions below will guide you through these steps. Follow them carefully == Create a project -* First, in the OpenShift AI Dashboard application, navigate to the Data Science Projects menu on the left: +* First, in the {rhoai} Dashboard application, navigate to the Data Science Projects menu on the left: + [.bordershadow] image::02/02-02-ds-proj-nav.png[] diff --git a/content/modules/ROOT/pages/02-03-creating-workbench.adoc b/content/modules/ROOT/pages/02-03-creating-workbench.adoc index d933c544..c2f2a175 100644 --- a/content/modules/ROOT/pages/02-03-creating-workbench.adoc +++ b/content/modules/ROOT/pages/02-03-creating-workbench.adoc @@ -9,7 +9,7 @@ include::_attributes.adoc[] image::02/02-03-create-wb.png[] * Make sure it has the following characteristics: ** Choose a name for it, like: `My Workbench` -** Image Selection: `CUSTOM - Insurance Claim Processing Lab Workbench` +** Image Selection: `CUSTOM - {ic} Workbench` ** Container Size: `Standard` ** Accelerator: `None` * That should look like: diff --git a/content/modules/ROOT/pages/02-04-validating-env.adoc b/content/modules/ROOT/pages/02-04-validating-env.adoc index 8f8b5762..0b8c9bc3 100644 --- a/content/modules/ROOT/pages/02-04-validating-env.adoc +++ b/content/modules/ROOT/pages/02-04-validating-env.adoc @@ -33,7 +33,7 @@ Success: LLM Service is reachable on llm.ic-shared-llm.svc.cluster.local:3000 Success: ModelMesh is reachable on modelmesh-serving.ic-shared-img-det.svc.cluster.local:8033 ---- -If the output of this notebook looks suspicious, please inform the people leading the lab. +If the output of this notebook looks suspicious, please inform the people leading the {ic}. == Overall view diff --git a/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc b/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc index d5518561..5effef0c 100644 --- a/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc +++ b/content/modules/ROOT/pages/03-04-comparing-model-servers.adoc @@ -1,7 +1,7 @@ = Comparing Model Servers include::_attributes.adoc[] -So far, for this lab, we have used the model https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2[Mistral-7B Instruct v2,window=_blank]. Although lighter than other models, it is still quite heavy and we need a large GPU to run it. Would we get as good results with a quantized model (reduced in size) to run on CPU only? Let's try! +So far, for this {ic-lab}, we have used the model https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2[Mistral-7B Instruct v2,window=_blank]. Although lighter than other models, it is still quite heavy and we need a large GPU to run it. Would we get as good results with a quantized model (reduced in size) to run on CPU only? Let's try! In this exercise, we'll pitch our previous model against the itself but in a quantized version. The values of the 7 Billion parameters used in the model have been "rounded", therefore loosing precision, but saving space. As it consumes now less memory, we are able to load it directly in a container and run it on a CPU. We'll compare the results and see if the quantized model is good enough for our use case. diff --git a/content/modules/ROOT/pages/03-05-prompt-engineering.adoc b/content/modules/ROOT/pages/03-05-prompt-engineering.adoc index e9a02141..3d971a37 100644 --- a/content/modules/ROOT/pages/03-05-prompt-engineering.adoc +++ b/content/modules/ROOT/pages/03-05-prompt-engineering.adoc @@ -1,9 +1,9 @@ = Prompt Engineering exercise (optional) include::_attributes.adoc[] -NOTE: This part of the lab is marked as optional. It can therefore be skipped without effect on the other parts of the lab. You can always come back to it later at the end of the lab if you have time to spare. +NOTE: This part of the {ic} is marked as optional. It can therefore be skipped without effect on the other parts of the lab. You can always come back to it later at the end of the {ic-lab} if you have time to spare. -IMPORTANT: When you interact with ChatGPT or other commercial services, a lot of guardrails are in place to prevent you from getting unwanted or not suitable for work results. In our Lab exercises, we are using a model that is not protected by those guardrails, and we will be modifying its settings. Therefore, it is your responsibility to do it in a safe way, and to make sure that the results you get are suitable for you and/or your audience. The authors of this Lab cannot be held responsible for any inappropriate results you may get. +IMPORTANT: When you interact with ChatGPT or other commercial services, a lot of guardrails are in place to prevent you from getting unwanted or not suitable for work results. In our {ic} exercises, we are using a model that is not protected by those guardrails, and we will be modifying its settings. Therefore, it is your responsibility to do it in a safe way, and to make sure that the results you get are suitable for you and/or your audience. The authors of this {ic} cannot be held responsible for any inappropriate results you may get. As you have seen in the previous sections, there are different factors that will influence the results you can get from your model: the model parameters, the prompt, the query, and of course the data itself... diff --git a/content/modules/ROOT/pages/03-06-sanity-check.adoc b/content/modules/ROOT/pages/03-06-sanity-check.adoc index 69a82427..90474b1b 100644 --- a/content/modules/ROOT/pages/03-06-sanity-check.adoc +++ b/content/modules/ROOT/pages/03-06-sanity-check.adoc @@ -42,7 +42,7 @@ image::03/03-07-run-pipeline-ok.png[] We can also **schedule** an execution so that the sanity check is executed at regular intervals. + To do that: -. Go back to the OpenShift AI Data Science Project +. Go back to the {rhoai} Data Science Project . Find the pipeline you just ran . Click the 3 dots at the very end of the line, and click "Create run". + diff --git a/content/modules/ROOT/pages/04-01-over-approach.adoc b/content/modules/ROOT/pages/04-01-over-approach.adoc index 839441fe..d864ff92 100644 --- a/content/modules/ROOT/pages/04-01-over-approach.adoc +++ b/content/modules/ROOT/pages/04-01-over-approach.adoc @@ -8,7 +8,7 @@ This model can be found online at https://www.yolov8.com[yolov8,window=_blank] a . We will first review its out-of-the-box capabilities. . We will then fine-tune it to allow it to do more specialized work for us. -. Once we have a new, customized version of the model, we will deploy it in OpenShift AI Model Serving. +. Once we have a new, customized version of the model, we will deploy it in {rhoai} Model Serving. . Once that is done, we will send queries to it. == Image Processing Out-of-the-box capabilities diff --git a/content/modules/ROOT/pages/04-03-model-retraining.adoc b/content/modules/ROOT/pages/04-03-model-retraining.adoc index afdaf749..15875f1c 100644 --- a/content/modules/ROOT/pages/04-03-model-retraining.adoc +++ b/content/modules/ROOT/pages/04-03-model-retraining.adoc @@ -15,7 +15,7 @@ image::04/roboflow-test-images.png[roboflow images] 3. Each image has an annotation text file in the 'labels' subfolder. The annotation text files have the same names as the image files. ==== -We have provided the following training data set, available as a zip file, and located in an S3 bucket: `accident-sample.zip` (as we don,t have time in this Lab to fully retrain the model, we will use a sample of the training data set). +We have provided the following training data set, available as a zip file, and located in an S3 bucket: `accident-sample.zip` (as we don,t have time in this {ic-lab} to fully retrain the model, we will use a sample of the training data set). - In your running workbench, navigate to the folder `insurance-claim-processing/lab-materials/04`. - Look for (and open) the notebook called `04-03-model-retraining.ipynb` diff --git a/content/modules/ROOT/pages/04-05-model-serving.adoc b/content/modules/ROOT/pages/04-05-model-serving.adoc index d0b49101..3dc4a8e1 100644 --- a/content/modules/ROOT/pages/04-05-model-serving.adoc +++ b/content/modules/ROOT/pages/04-05-model-serving.adoc @@ -1,7 +1,7 @@ = Model Serving include::_attributes.adoc[] -. At this point, we need to deploy the model into RHOAI model serving. +. At this point, we need to deploy the model into {rhoai-short} model serving. . We will create another Data Connection... .. With almost identical information .. But we will change the bucket name from `userX` to `models` diff --git a/content/modules/ROOT/pages/05-01-application.adoc b/content/modules/ROOT/pages/05-01-application.adoc index 774329ce..3f2bc179 100644 --- a/content/modules/ROOT/pages/05-01-application.adoc +++ b/content/modules/ROOT/pages/05-01-application.adoc @@ -12,17 +12,17 @@ image::05/application-architecture.drawio.svg[] The different components are: - The **frontend**: the application itself, developed using the Patternfly framework (React, Typescript), running in the browser of the user. -- The **backend**: a Python FastAPI application, running in a container on OpenShift. It handles the communication with the database, the LLM, and the Object storage. It handles the communication with the frontend by exposing a REST API. -- The **database**: a PostgreSQL database, running in a container on OpenShift. It stores the claims. +- The **backend**: a Python FastAPI application, running in a container on {ocp}. It handles the communication with the database, the LLM, and the Object storage. It handles the communication with the frontend by exposing a REST API. +- The **database**: a PostgreSQL database, running in a container on {ocp}. It stores the claims. - The **LLM**: the language model used to summarize and extract information from the claims. It is consumed by the backend through its API. - The **Object storage**: an S3-compatible object storage. It stores the claim images. == Application Code -If you want to have a look at the code of the application, you can do it directly from OpenShift AI! +If you want to have a look at the code of the application, you can do it directly from {rhoai}! - Close the different Jupyter tabs that may still be opened in your browser. -- Go back to the OpenShift AI dashboard. +- Go back to the {rhoai} Dashboard. - Stop your Workbench by using the toggle. + @@ -37,11 +37,11 @@ image::05/05-toggle-off-workbench.jpg[] [.bordershadow] image::05/stop-workbench.png[] -- In the image section, change the image to `CUSTOM - VSCode for Insurance Claim Processsing Lab`: +- In the image section, change the image to `CUSTOM - VSCode for {ic}`: + [.bordershadow] image::05/05-change-to-vscode.jpg[] -- Note that this is a Custom-built image added in this particular lab to illustrate the flexibility of the OpenShift AI platform. Not everyone wants to use Jupyter Notebooks for everything. +- Note that this is a Custom-built image added in this particular {ic-lab} to illustrate the flexibility of the {rhoai} platform. Not everyone wants to use Jupyter Notebooks for everything. - Then click on `Update workbench` at the bottom of the page: + diff --git a/content/modules/ROOT/pages/05-02-openshift-terminal.adoc b/content/modules/ROOT/pages/05-02-openshift-terminal.adoc index 72547e04..16600ca2 100644 --- a/content/modules/ROOT/pages/05-02-openshift-terminal.adoc +++ b/content/modules/ROOT/pages/05-02-openshift-terminal.adoc @@ -1,12 +1,12 @@ -= Working with the OpenShift Terminal += Working with the {ocp} Terminal include::_attributes.adoc[] -To deploy the application, you will use the OpenShift Web Terminal. + -This is a web-based terminal that allows you to execute commands on the OpenShift cluster from the OpenShift Console. +To deploy the application, you will use the {ocp} Web Terminal. + +This is a web-based terminal that allows you to execute commands on the {ocp-short} cluster from the {ocp-short} Console. -== Opening the OpenShift Console +== Opening the {ocp} Console -- Access the OpenShift Console via this link: +- Access the {ocp-short} Console via this link: + [.bordershadow] image::05/01-openshift-console.png[] @@ -15,7 +15,7 @@ image::05/01-openshift-console.png[] == Open a Web Terminal -- On the OpenShift Console, click on the Web Terminal icon in the top right corner: +- On the {ocp-short} Console, click on the Web Terminal icon in the top right corner: + [.bordershadow] image::05/web-term-1.png[] diff --git a/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc b/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc index 385f8a82..8e248d86 100644 --- a/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc +++ b/content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc @@ -1,12 +1,12 @@ = Deploying the application via GitOps include::_attributes.adoc[] -== Deploy your instance of ArgoCD +== Deploy your instance of {argocd} We will start by deploying an instance of ArgoCD in your namespace. + This will be used to deploy the application. -- Copy the following text, and paste it in the OpenShift Terminal to deploy ArgoCD. +- Copy the following text, and paste it in the {ocp-short} Terminal to deploy {argocd}. + [.lines_space] [.console-input] @@ -42,7 +42,7 @@ spec: termination: edge EOF -- Still in the Terminal, run the following command to check the status of the ArgoCD deployment. +- Still in the Terminal, run the following command to check the status of the {argocd} deployment. + [.lines_space] [.console-input] @@ -54,17 +54,17 @@ oc rollout status deploy/argocd-server [.bordershadow] image::05/argocd-rollout.png[] -**Optional**: If you know and understand ArgoCD and want to look at its interface, look at the following details. Otherwise, you can skip to the next step. +**Optional**: If you know and understand {argocd} and want to look at its interface, look at the following details. Otherwise, you can skip to the next step. [%collapsible] ==== Now that Argo is deployed, you can connect to its UI through its Route. -- Let's find the Route of the ArgoCD instance by running the following command in the Terminal: +- Let's find the Route of the {argocd} instance by running the following command in the Terminal: + [.lines_space] [.console-input] [source, text] -echo " ArgoCD UI : https://$(oc get route argocd-server -ojsonpath='{.status.ingress[0].host}')/ " +echo " {argocd} UI : https://$(oc get route argocd-server -ojsonpath='{.status.ingress[0].host}')/ " - You should obtain something like this: + @@ -76,9 +76,9 @@ image::05/argocd-route.png[] == Deploy the application(s) via GitOps -Now that ArgoCD is deployed in your namespace, we can use it to deploy the application via GitOps. +Now that {argocd} is deployed in your namespace, we can use it to deploy the application via GitOps. -- Copy the content of the following text, and paste it in the OpenShift Terminal to deploy the application. +- Copy the content of the following text, and paste it in the {ocp-short} Terminal to deploy the application. + [.lines_space] [.console-input] diff --git a/content/modules/ROOT/pages/05-05-process-claims.adoc b/content/modules/ROOT/pages/05-05-process-claims.adoc index 519a6404..18cb9aa4 100644 --- a/content/modules/ROOT/pages/05-05-process-claims.adoc +++ b/content/modules/ROOT/pages/05-05-process-claims.adoc @@ -39,7 +39,7 @@ NOTE: In the folder, we still have for reference an Elyra version of the pipelin Before we can run the pipeline, we need to create a PVC that will be used to store intermediary files and results in. + -* Go to the OpenShift Console +* Go to the {ocp-short} Console * Make sure and change your view from **Developer** to **Administrator** + [.bordershadow] @@ -93,7 +93,7 @@ image::05/05-PVC-settings.png[PVC settings] * Start by downloading the `process_claims.yaml` file locally to your laptop ** In your VSCode Workbench, right click on the file, and select **Download** ** Save the file somewhere on your laptop -* Then go to the Red Hat OpenShift Data Science dashboard +* Then go to the {rhoai} Dashboard * Select your Data Science project * Scroll down until you see the **Pipelines** section * Click **Import Pipeline** diff --git a/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc b/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc index 47352e80..e55b01a1 100644 --- a/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc +++ b/content/modules/ROOT/pages/06-01-potential-imp-ref.adoc @@ -1,15 +1,15 @@ = Potential Improvements and Refinements include::_attributes.adoc[] -== To the lab materials +== To the {ic} materials -If you have any feedback regarding this Lab, please use https://github.com/rh-aiservices-bu/insurance-claim-processing/issues[Issues,window=_blank] and https://github.com/rh-aiservices-bu/insurance-claim-processing/pulls[Pull Requests,window=_blank]. +If you have any feedback regarding this {ic}, please use https://github.com/rh-aiservices-bu/insurance-claim-processing/issues[Issues,window=_blank] and https://github.com/rh-aiservices-bu/insurance-claim-processing/pulls[Pull Requests,window=_blank]. -But this section of the lab is not meant for Improvements and Refinements **to the lab**. Instead it is about... +But this section of the {ic} is not meant for Improvements and Refinements **to the lab**. Instead it is about... == To the presented prototype -What we have shown in this lab is a very rough prototype, put together very quickly, in order to demonstrate: +What we have shown in this {ic} is a very rough prototype, put together very quickly, in order to demonstrate: * Which improvements could be done * How long it would take to do them diff --git a/content/modules/ROOT/pages/06-02-applicability-other.adoc b/content/modules/ROOT/pages/06-02-applicability-other.adoc index eff49bbf..f3b12761 100644 --- a/content/modules/ROOT/pages/06-02-applicability-other.adoc +++ b/content/modules/ROOT/pages/06-02-applicability-other.adoc @@ -1,7 +1,7 @@ = Applicability to other industries include::_attributes.adoc[] -Now, the examples we've shown throughout this lab are specific to the Insurance industry, and within that, claims processing. +Now, the examples we've shown throughout this {ic} are specific to the Insurance industry, and within that, claims processing. However, none of the techniques we used here are inherently related to this industry or this use-case. diff --git a/content/modules/ROOT/pages/07-01-end-of-lab.adoc b/content/modules/ROOT/pages/07-01-end-of-lab.adoc index b4e5ca08..8554c060 100644 --- a/content/modules/ROOT/pages/07-01-end-of-lab.adoc +++ b/content/modules/ROOT/pages/07-01-end-of-lab.adoc @@ -5,8 +5,8 @@ We hope that the materials we used during this time together were useful and gav If you notice https://github.com/rh-aiservices-bu/insurance-claim-processing/issues[issues,window=_blank] with the content and/or want to send us a https://github.com/rh-aiservices-bu/insurance-claim-processing/pulls[pull request,window=_blank], we'll appreciate it very much. -The instructions of this lab are always available at https://rh-aiservices-bu.github.io/insurance-claim-processing/[https://rh-aiservices-bu.github.io/insurance-claim-processing/,window=_blank]. Make note of this URL as it will be updated with new content as we continue to improve the lab. +The instructions of this {ic} are always available at https://rh-aiservices-bu.github.io/insurance-claim-processing/[https://rh-aiservices-bu.github.io/insurance-claim-processing/,window=_blank]. Make note of this URL as it will be updated with new content as we continue to improve the {ic-lab}. == Acknowledgements -Thanks to everyone who helped create this lab, https://github.com/rh-aiservices-bu/insurance-claim-processing/graphs/contributors[contributors,window=_blank] and reviewers! +Thanks to everyone who helped create this {ic}, https://github.com/rh-aiservices-bu/insurance-claim-processing/graphs/contributors[contributors,window=_blank] and reviewers! diff --git a/content/modules/ROOT/pages/index.adoc b/content/modules/ROOT/pages/index.adoc index 07b80891..363339d2 100644 --- a/content/modules/ROOT/pages/index.adoc +++ b/content/modules/ROOT/pages/index.adoc @@ -1,19 +1,19 @@ = {lab_name} include::_attributes.adoc[] -== Lab Overview +== {ic} Overview -This lab will illustrate how the use of various AI/ML technologies can be combined to produce a valuable solution to a business problem. +This {ic} will illustrate how the use of various AI/ML technologies can be combined to produce a valuable solution to a business problem. The information, code, models and techniques it contains are illustrations of what a first prototype could look like. It is not the definitive way of addressing the stated requirements. == Disclaimer -This lab is an example of what a customer could build using OpenShift AI. OpenShift AI has no specific feature related to Insurance Claim Processing. +This {ic} is an example of what a customer could build using {rhoai}. {rhoai} has no specific feature related to Insurance Claim Processing. -This lab makes use of large language models (LLM) and image processing models. These models are not included in the OpenShift AI product. They are provided as a convenience for this lab. +This {ic-lab} makes use of large language models (LLM) and image processing models. These models are not included in the {rhoai} product. They are provided as a convenience for this {ic-lab}. -The quality of these models is enough for a prototype. Choosing the right model to use in a production environment is a complex task that requires a lot of experimentation and tuning. This lab does not cover this aspect. +The quality of these models is enough for a prototype. Choosing the right model to use in a production environment is a complex task that requires a lot of experimentation and tuning. 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-31,4 +31,5 @@ video { .navbar-item-right-content { width: 100%; + background-color: #131313; } \ No newline at end of file diff --git a/content/supplemental-ui/partials/header-content.hbs b/content/supplemental-ui/partials/header-content.hbs index f4da0b0d..decf93f5 100644 --- a/content/supplemental-ui/partials/header-content.hbs +++ b/content/supplemental-ui/partials/header-content.hbs @@ -1,32 +1,11 @@

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