OpenShift AI tutorial - Fraud detection example

Procedure

1. After you log in to the OpenShift console, click the application launcher icon on the header.

   

2. Log in to the OpenShift AI dashboard by using your OpenShift credentials. OpenShift AI uses the same credentials as OpenShift for the dashboard, notebooks, and all other components.

   The OpenShift AI dashboard shows the Home page.

   Note

   You can navigate back to the OpenShift console by clicking the application launcher.

   

   For now, stay in the OpenShift AI dashboard.

3. Make sure that the version of this tutorial matches the OpenShift AI version on your cluster.

   Red Hat OpenShift AI v3.2 is the version described in this tutorial.

   To find the OpenShift AI version for your cluster:

   1. In the top navigation bar of the OpenShift AI dashboard, next to your username, click the help icon ( ) and then select About.

      The About page shows the installed version.

4. Check the Red Hat OpenShift AI Supported Configurations for 3.x page to verify that your installed version is a supported version.

5. If this version of the tutorial does not match the installed version, access the matching version of the tutorial:

   1. Navigate to the Red Hat OpenShift AI documentation page.
   2. Select the matching version from the drop-down list.

Procedure

1. From the left navigation menu, select Projects. This page lists any existing projects that you have access to. You can select an existing project (if any) or create a new one.

2. Click Create project.

   Note

   You can start a Jupyter notebook by clicking the Start basic workbench button. However, in that case, it is a one-off Jupyter notebook run in isolation.

3. In the Create project modal, enter a display name and description.

   
4. Click Create.

Procedure

1. In the OpenShift AI dashboard, on the Fraud Detection page, click the Pipelines tab.

2. Click Configure pipeline server.

   

3. In the Configure pipeline server form, click Autofill from connection and then click Pipeline Artifacts.

   The Configure pipeline server form fills with credentials for the connection.

   
4. In the Advanced Settings section, leave the default values.
5. Click Configure pipeline server.

6. Wait until the loading spinner disappears and Start by importing a pipeline is displayed.

   Important

   You must wait until the pipeline configuration is complete before you continue and create your workbench. If you create your workbench before the pipeline server is ready, your workbench cannot submit pipelines to it.

   1. You can click View progress and event logs.

      If you have waited more than 5 minutes, and the pipeline server configuration does not complete, you can click Cancel pipeline server setup and create it again.

      You can also ask your OpenShift AI administrator to verify that they applied self-signed certificates on your cluster as described in Working with certificates (Self-Managed).

Verification

1. Navigate to the Pipelines tab for the project.

2. Next to Import pipeline, click the action menu (⋮) and then select Manage pipeline server configuration.

   

   An information box opens and displays the object storage connection information for the pipeline server.

Procedure

1. In the OpenShift AI dashboard, click the application launcher icon and then select the OpenShift Console option.

   

2. In the OpenShift console, click + in the top navigation bar, and then click Import YAML.

   

3. Click the down arrow next to the project name, and then select your project from the list of projects. If needed, type the name of your project in the Select project search field.

   

4. Verify that you selected the correct project.

   

5. Copy the following code and paste it into the Import YAML editor.

   ---
   apiVersion: kueue.x-k8s.io/v1beta1
   kind: ResourceFlavor
   metadata:
     name: default-flavor
   ---
   apiVersion: kueue.x-k8s.io/v1beta1
   kind: ClusterQueue
   metadata:
     name: cluster-queue
   spec:
     namespaceSelector: {}
     resourceGroups:
     - coveredResources: ["cpu", "memory"]
       flavors:
       - name: "default-flavor"
         resources:
         - name: "cpu"
           nominalQuota: 4
         - name: "memory"
           nominalQuota: 8Gi
   ---
   apiVersion: kueue.x-k8s.io/v1beta1
   kind: LocalQueue
   metadata:
     name: local-queue
   spec:
     clusterQueue: cluster-queue

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6. Click Create.

Procedure

1. Navigate to the project detail page for the project that you created in Setting up your project.

2. Click the Workbenches tab, and then click the Create workbench button.

   

3. Enter the name and description.

   

   Red Hat provides several supported workbench images. In the Workbench image section, you can select one of the default images or a custom image that an administrator has set up for you. The Tensorflow image has the libraries needed for this tutorial.

4. Select the latest Tensorflow image.

   
5. Under the Deployment size section, for Hardware profile, select default-profile.

6. Keep the default environment variables and storage options.

   
7. For Connections, click Attach existing connection.

8. Select My Storage (the object storage that you configured earlier) and then click Attach.

   
9. Click Create workbench.

Procedure

1. In the Workbenches tab for your project, click the link for your workbench. If prompted, log in and allow JupyterLab to authorize your user.

   

   Your JupyterLab environment window opens.

   This file-browser window shows the files and folders that are saved inside your own personal space in OpenShift AI.

2. Bring the content of this tutorial inside your JupyterLab environment:

   1. On the toolbar, click the Git Clone icon:

      

   2. Enter the following tutorial Git https URL:

      https://github.com/rh-aiservices-bu/fraud-detection.git

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   3. Select the Include submodules option, and then click Clone.

   4. In the file browser, double-click the newly-created fraud-detection folder to expand its contents.

      

   5. In the left navigation bar, click the Git icon, and then click Current Branch to expand the branches and tags selector panel.

      1. On the Branches tab, in the Filter field, enter v3.2.

         

   6. Select origin/v3.2.

      The current branch changes to v3.2.

Verification

1. In the left navigation bar, click the file browser icon to view the notebooks that you cloned from Git.

   
2. Verify that the Git version at the bottom of the JupyterLab window is v3.2.

Procedure

1. In your JupyterLab environment, locate the 0_sandbox.ipynb file and double-click it to launch the notebook. The notebook opens in a new tab in the content section of the environment.

   

2. Experiment by, for example, running the existing cells, adding more cells and creating functions.

   You can do what you want - it is your environment and there is no risk of breaking anything or impacting other users. This environment isolation is also a great advantage brought by OpenShift AI.

3. Optionally, create a new notebook in which the code cells are run by using a Python 3 kernel:

   1. Create a new notebook by either selecting File →New →Notebook or by clicking the Python 3 tile in the Notebook section of the launcher window:

      

      You can use different kernels, with different languages or versions, to run in your notebook.

Procedure

1. In your JupyterLab environment, open the 2_save_model.ipynb file.
2. Follow the instructions in the notebook to make the model accessible in storage.

Procedure

1. In the OpenShift AI dashboard, navigate to the project details page and click the Deployments tab.

2. Click Deploy model.

   The Deploy a model wizard opens.

3. In the Model details section, provide information about the model:

   1. For Model location, select Existing connection and then select My Storage.

   2. Enter the following values from your MinIO storage connection:

      • Access Key
      • Secret Key
      • Endpoint
      • Region
      • Bucket
   3. For Path, enter models/fraud.
   4. For Model type, select Predictive model.
   5. Click Next.

4. In the Model deployment section, configure the deployment:

   1. For Model deployment name, enter fraud.
   2. For Description, enter a description of your deployment.
   3. For the hardware profile, keep the default value.
   4. For Model framework (name - version), select onnx-1.
   5. For the Serving runtime field, accept the auto-selected runtime, OpenVINO Model Server.
   6. Click Next.
5. In the Advanced settings section, accept the defaults by clicking Next.
6. In the Review section, click Deploy model.

Procedure

1. In the OpenShift AI dashboard, navigate to the project details page and click the Deployments tab.

2. Take note of the model’s Inference endpoint URL. You need this information when you test the model API.

   If the Inference endpoint field has an Internal endpoint details link, click the link to open a text box that shows the URL details, and then take note of the restUrl value.

   

   NOTE: When you test the model API from inside a workbench, you must edit the endpoint to specify 8888 for the port. For example:

   http://fraud-predictor.fraud-detection.svc.cluster.local:8888

3. Return to the JupyterLab environment and try out your new endpoint.

   Follow the directions in 3_rest_requests.ipynb to try a REST API call.

Procedure

1. Optionally, view the provided Python code in your JupyterLab environment by navigating to the fraud-detection-notebooks project’s pipeline directory. It contains the following files:

   • 5_get_data_train_upload.py is the main pipeline code.

   • build.sh is a script that builds the pipeline and creates the YAML file.

     For your convenience, the output of the build.sh script is provided in the 5_get_data_train_upload.yaml file. The 5_get_data_train_upload.yaml output file is located in the top-level fraud-detection directory.

2. Right-click the 5_get_data_train_upload.yaml file and then click Download.

3. Upload the 5_get_data_train_upload.yaml file to OpenShift AI.

   1. In the OpenShift AI dashboard, navigate to your project page. Click the Pipelines tab and then click Import pipeline.
   2. Enter values for Pipeline name and Pipeline description.

   3. Click Upload and then select 5_get_data_train_upload.yaml from your local files to upload the pipeline.

      

   4. Click Import pipeline to import and save the pipeline.

      The pipeline shows in graphic view.

      
4. Select Actions → Create run.

5. On the Create run page, provide the following values:

   1. For Experiment, leave the value as Default.
   2. For Name, type any name, for example Run 1.

   3. For Pipeline, select the pipeline that you uploaded.

      You can leave the other fields with their default values.

      
6. Click Create run to create the run.