1. In the OpenShift AI dashboard, navigate to the page for your data science project.

2. Click the Data connections tab, and then click Add data connection.

   

3. Fill out the Add data connection form and name your connection My Storage. This connection is for saving your personal work, including data and models.

   
4. Click Add data connection.

1. Click Add data connection.

2. Fill out the form and name your connection Pipeline Artifacts.

   
3. Click Add data connection.

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.

   

3. Select your project from the list of projects.

   

4. Verify that you selected the correct project.

   

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

   Note: This code gets and applies the setup-s3-no-sa.yaml file.

   ---
   apiVersion: v1
   kind: ServiceAccount
   metadata:
     name: demo-setup
   ---
   apiVersion: rbac.authorization.k8s.io/v1
   kind: RoleBinding
   metadata:
     name: demo-setup-edit
   roleRef:
     apiGroup: rbac.authorization.k8s.io
     kind: ClusterRole
     name: edit
   subjects:
     - kind: ServiceAccount
       name: demo-setup
   ---
   apiVersion: batch/v1
   kind: Job
   metadata:
     name: create-s3-storage
   spec:
     selector: {}
     template:
       spec:
         containers:
           - args:
               - -ec
               - |-
                 echo -n 'Setting up Minio instance and data connections'
                 oc apply -f https://github.com/rh-aiservices-bu/fraud-detection/raw/main/setup/setup-s3-no-sa.yaml
             command:
               - /bin/bash
             image: image-registry.openshift-image-registry.svc:5000/openshift/tools:latest
             imagePullPolicy: IfNotPresent
             name: create-s3-storage
         restartPolicy: Never
         serviceAccount: demo-setup
         serviceAccountName: demo-setup

6. Click Create.

Procedure

1. In your Jupyter 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’s 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:

      

Procedure

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

   
2. In the Single-model serving platform tile, click Deploy model.

3. In the form, provide the following values:

   1. For Model Name, type fraud.
   2. For Serving runtime, select OpenVINO Model Server.
   3. For Model framework, select onnx-1.
   4. For Existing data connection, select My Storage.
   5. Type the path that leads to the version folder that contains your model file: models/fraud

   6. Leave the other fields with the default settings.

      
4. Click Deploy.

Procedure

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

   
2. In the Multi-model serving platform tile, click Add model server.

3. In the form, provide the following values:

   1. For Model server name, type a name, for example Model Server.
   2. For Serving runtime, select OpenVINO Model Server.

   3. Leave the other fields with the default settings.

      
4. Click Add.

5. In the Models and model servers list, next to the new model server, click Deploy model.

   

6. In the form, provide the following values:

   1. For Model Name, type fraud.
   2. For Model framework, select onnx-1.
   3. For Existing data connection, select My Storage.
   4. Type the path that leads to the version folder that contains your model file: models/fraud

   5. Leave the other fields with the default settings.

      
7. Click Deploy.

1. Open your workbench’s JupyterLab environment. If the launcher is not visible, click + to open it.

   

2. Click Pipeline Editor.

   

   You’ve created a blank pipeline!

3. Set the default runtime image for when you run your notebook or Python code.

   1. In the pipeline editor, click Open Panel.

      

   2. Select the Pipeline Properties tab.

      

   3. In the Pipeline Properties panel, scroll down to Generic Node Defaults and Runtime Image. Set the value to Tensorflow with Cuda and Python 3.9 (UBI 9).

      
4. Save the pipeline.

1. From the file-browser panel, drag the 1_experiment_train.ipynb and 2_save_model.ipynb notebooks onto the pipeline canvas.

   

2. Click the output port of 1_experiment_train.ipynb and drag a connecting line to the input port of 2_save_model.ipynb.

   
3. Save the pipeline.

1. Click the 1_experiment_train.ipynb node.

   
2. In the Properties panel, click the Node Properties tab.

3. Scroll down to the File Dependencies section and then click Add.

   
4. Set the value to data/card_transdata.csv which contains the data to train your model.

5. Select the Include Subdirectories option and then click Add.

   
6. Save the pipeline.

1. Select node 1 and then select the Node Properties tab.
2. Scroll down to the Output Files section, and then click Add.

3. Set the value to models/fraud/1/model.onnx and then click Add.

   
4. Repeat steps 1-3 for node 2.
5. Save the pipeline.

Procedure

1. Remove any pre-filled environment variables.

   1. Select node 2, and then select the Node Properties tab.

      Under Additional Properties, note that some environment variables have been pre-filled. The pipeline editor inferred that you need them from the notebook code.

      Since you don’t want to save the value in your pipelines, remove all of these environment variables.

   2. Click Remove for each of the pre-filled environment variables.

      

2. Add the S3 bucket and keys by using the Kubernetes secret.

   1. Under Kubernetes Secrets, click Add.

      

   2. Enter the following values and then click Add.

      • Environment Variable: AWS_ACCESS_KEY_ID
      • Secret Name: aws-connection-my-storage

      • Secret Key: AWS_ACCESS_KEY_ID

        

   3. Repeat Steps 2a and 2b for each set of these Kubernetes secrets:

      • Environment Variable: AWS_SECRET_ACCESS_KEY

        • Secret Name: aws-connection-my-storage
        • Secret Key: AWS_SECRET_ACCESS_KEY

      • Environment Variable: AWS_S3_ENDPOINT

        • Secret Name: aws-connection-my-storage
        • Secret Key: AWS_S3_ENDPOINT

      • Environment Variable: AWS_DEFAULT_REGION

        • Secret Name: aws-connection-my-storage
        • Secret Key: AWS_DEFAULT_REGION

      • Environment Variable: AWS_S3_BUCKET

        • Secret Name: aws-connection-my-storage
        • Secret Key: AWS_S3_BUCKET
3. Save and Rename the .pipeline file.

Procedure

1. Click the play button in the toolbar of the pipeline editor.

   
2. Enter a name for your pipeline.
3. Verify the Runtime Configuration: is set to Data Science Pipeline.

4. Click OK.

   Note

   If Data Science Pipeline is not available as a runtime configuration, you may have created your notebook before the pipeline server was available. You can restart your notebook after the pipeline server has been created in your data science project.

5. Return to your data science project and expand the newly created pipeline.

   

6. Click View runs and then view the pipeline run in progress.