Chapter 3. Working on data science projects

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click Create data science project.

   The Create a data science project dialog opens.

3. Enter a name for your data science project.
4. Optional: Edit the resource name for your data science project. The resource name must consist of lowercase alphanumeric characters, -, and must start and end with an alphanumeric character.
5. Enter a description for your data science project.

6. Click Create.

   A project details page opens. From here, you can create workbenches, add cluster storage, and add data connections to your project.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the action menu (⋮) beside the project whose details you want to update and click Edit project.

   The Edit data science project dialog opens.

3. Optional: Update the name for your data science project.
4. Optional: Update the description for your data science project.
5. Click Update.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the action menu (⋮) beside the project that you want to delete and click Delete project.

   The Delete project dialog opens.

3. Enter the project name in the text field to confirm that you intend to delete it.
4. Click Delete project.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to add the workbench to.

   The Details page for the project opens.

3. Click Create workbench in the Workbenches section.

   The Create workbench page opens.

4. Configure the properties of the workbench you are creating.

   1. Enter a name for your workbench.
   2. Enter a description for your workbench.
   3. Select the notebook image to use for your workbench server.
   4. Select the container size for your server.
   5. Optional: Select and specify values for any new environment variables.

   6. Configure the storage for your OpenShift Data Science cluster.

      1. Select Create new persistent storage to create storage that is retained after you log out of OpenShift Data Science. Fill in the relevant fields to define the storage.
      2. Select Use existing persistent storage to reuse existing storage then select the storage from the Persistent storage list.
5. Click Create workbench.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project whose workbench you want to start.

   The Details page for the project opens.

3. Click the toggle in the Status column for the relevant workbench to start a workbench that is not running.

   The status of the workbench that you started changes from Stopped to Running. After the workbench has started, click Open to open the workbench’s notebook.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project whose workbench you want to update.

   The Details page for the project opens.

3. Click the action menu (⋮) beside the workbench that you want to update in the Workbenches section and click Edit workbench.

   The Edit workbench page opens.

4. Update the workbench’s properties.

   1. Update the name for your workbench, if applicable.
   2. Update description for your workbench, if applicable.
   3. Select a new notebook image to use for your workbench server, if applicable.
   4. Select a new container size for your server, if applicable.
5. Click Update workbench.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to delete the workbench from.

   The Details page for the project opens.

3. Click the action menu (⋮) beside the workbench that you want to delete in the Workbenches section and click Delete workbench.

   The Delete workbench dialog opens.

4. Enter the name of the workbench in the text field to confirm that you intend to delete it.
5. Click Delete workbench.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to add a data connection to.

   A project details page opens.

3. In the Data connections section of the page, click Add data connection.

   The Add data connection dialog opens.

4. Enter a name for the data connection.
5. Enter your access key ID for Amazon Web Services in the AWS_ACCESS_KEY_ID field.
6. Enter your secret access key for the account you specified in the AWS_SECRET_ACCESS_KEY_ID field.
7. Enter the endpoint of your AWS S3 storage in the AWS_S3_ENDPOINT field.
8. Enter the default region of your AWS account in the AWS_DEFAULT_REGION field.
9. Enter the name of the AWS S3 bucket in the AWS_S3_BUCKET field.
10. Click Add data connection.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to delete the data connection from.

   The Details page for the project opens.

3. Click the action menu (⋮) beside the data connection that you want to delete in the Data connections section and click Delete data connection.

   The Delete data connection dialog opens.

4. Enter the name of the data connection in the text field to confirm that you intend to delete it.
5. Click Delete data connection.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project whose data source you want to change.

   A project details page opens.

3. Click the action menu (⋮) beside the data source that you want to change in the Data connections section and click Change connected workbenches.

   The Update connected workbenches dialog opens.

4. Select an existing workbench to connect the data source to from the list.
5. Click Update connected workbenches.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to add the cluster storage to.

   A project details page opens.

3. In the Cluster storage section of the page, click Add cluster storage.

   The Add storage dialog opens.

4. Enter a name for the cluster storage.
5. Enter a description for the cluster storage.
6. Under Persistent storage size, enter a new size in gibibytes. The minimum size is 1 GiB, and the maximum size is 16384 GiB.
7. Optional: Select a workbench from the list to connect the cluster storage to an existing workbench.
8. If you selected a workbench to connect the storage to, enter the storage directory in the Mount folder field.
9. Click Add storage.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project whose storage you want to update.

   The Details page for the project opens.

3. Click the action menu (⋮) beside the storage that you want to update in the Cluster storage section and click Edit storage.

   The Edit storage page opens.

4. Update the storage’s properties.

   1. Update the name for the storage, if applicable.
   2. Update the description for the storage, if applicable.

   3. Increase the Persistent storage size for the storage, if applicable.

      Note that you can only increase the storage size. Updating the storage size restarts the workbench and makes it unavailable for a period of time that is usually proportional to the size change.

   4. Update the workbench that the storage is connected to, if applicable.
   5. If you selected a new workbench to connect the storage to, enter the storage directory in the Mount folder field.
5. Click Update storage.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to delete the storage from.

   A project details page opens.

3. In the Cluster storage section, click the action menu (⋮) beside the storage that you want to delete and then click Delete storage.

   The Delete storage dialog opens.

4. Enter the name of the storage in the text field to confirm that you intend to delete it.
5. Click Delete storage.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to configure a pipeline server for.

   A project details page opens.

3. In the Pipelines section, click Create a pipeline server.

   The Configure pipeline server dialog appears.

4. In the Object storage connection section, to specify the S3-compatible data connection to store your pipeline artifacts, select one of the following sets of actions:

   Note

   After the pipeline server is created, the /metadata and /artifacts folders are automatically created in the default root folder. Therefore, you are not required to specify any storage directories when configuring a data connection for your pipeline server.

   • Select Existing data connection to use a data connection that you previously defined. If you selected this option, from the Name list, select the name of the relevant data connection and skip to step 6.
   • Select Create new data connection to add a new data connection that your pipeline server can access.

5. If you selected Create new data connection, perform the following steps:

   1. In the Name field, enter a name for the data connection.
   2. In the AWS_ACCESS_KEY_ID field, enter your access key ID for Amazon Web Services.
   3. In the AWS_SECRET_ACCESS_KEY_ID field, enter your secret access key for the account you specified.
   4. Optional: In the AWS_S3_ENDPOINT field, enter the endpoint of your AWS S3 storage.
   5. Optional: In the AWS_DEFAULT_REGION field, enter the default region of your AWS account.

   6. In the AWS_S3_BUCKET field, enter the name of the AWS S3 bucket.

      Important

      If you are creating a new data connection, in addition to the other designated mandatory fields, the AWS_S3_BUCKET field is mandatory. If you specify incorrect data connection settings, you cannot update these settings on the same pipeline server. Therefore, you must delete the pipeline server and configure another one.

6. In the Database section, click Show advanced database options to specify the database to store your pipeline data and select one of the following sets of actions:

   • Select Use default database stored on your cluster to deploy a MariaDB database in your project.

   • Select Connect to external MySQL database to add a new connection to an external database that your pipeline server can access.

     1. In the Host field, enter the database’s host name.
     2. In the Port field, enter the database’s port.
     3. In the Username field, enter the default user name that is connected to the database.
     4. In the Password field, enter the password for the default user account.
     5. In the Database field, enter the database name.
7. Click Configure.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Pipelines Pipelines.

   The Pipelines page opens.

2. From the Project list, select the project that you want to import a pipeline to.

3. Click Import pipeline.

   The Import pipeline dialog opens.

4. Enter the details for the pipeline that you are importing.

   1. In the Pipeline name field, enter a name for the pipeline that you are importing.
   2. In the Pipeline description field, enter a description for the pipeline that you are importing.

   3. Click Upload. Alternatively, drag the file from your local machine’s file system and drop it in the designated area in the Import pipeline dialog.

      A file browser opens.

   4. Navigate to the file containing the pipeline code and click Select.
   5. Click Import pipeline.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to configure a model server for.

   A project details page opens.

3. In the Models and model servers section, click Add server.

   The Add model server dialog opens.

4. In the Model server name field, enter a unique name for the model server.
5. From the Serving runtime list, select a model-serving runtime that is installed and enabled in your OpenShift Data Science deployment.
6. In the Number of model replicas to deploy field, specify a value.

7. From the Model server size list, select one of the following server sizes:

   • Small
   • Medium
   • Large
   • Custom

8. Optional: If you selected Custom in the preceding step, configure the following settings in the Model server size section to customize your model server:

   1. In the CPUs requested field, specify a number of CPUs to use with your model server. Use the list beside this field to specify the value in cores or millicores.
   2. In the CPU limit field, specify the maximum number of CPUs to use with your model server. Use the list beside this field to specify the value in cores or millicores.
   3. In the Memory requested field, specify the requested memory for the model server in gibibytes (Gi).
   4. In the Memory limit field, specify the maximum memory limit for the model server in gibibytes (Gi).

9. Optional: In the Model server GPUs field, specify a number of GPUs to use with your model server.

   Important

   OpenShift Data Science includes two versions of the OpenVINO Model Server (OVMS) runtime by default; a version that supports GPUs and one that does not. To use GPUs, from the Serving runtime list, you must select the version whose display name includes Supports GPUs.

   If you are using a custom model-serving runtime with your model server, you must ensure that your custom runtime supports GPUs and is appropriately configured to use them.

10. Optional: In the Model route section, select the Make deployed models available through an external route check box to make your deployed models available to external clients.

11. Optional: In the Token authorization section, select the Require token authentication check box to require token authentication for your model server. To finish configuring token authentication, perform the following actions:

    1. In the Service account name field, enter a service account name for which the token will be generated. The generated token is created and displayed in the Token secret field when the model server is configured.
    2. To add an additional service account, click Add a service account and enter another service account name.
12. Click Add.

Procedure

1. From the OpenShift Data Science dashboard, click Settings > Serving runtimes.

   The Serving runtimes page opens and shows the model-serving runtimes that are already installed and enabled in your OpenShift Data Science deployment. By default, the OpenVINO Model Server runtime is pre-installed and enabled in OpenShift Data Science.

2. To add a new, custom runtime, click Add serving runtime.

   The Add serving runtime page opens.

3. To start adding a new runtime, perform one of the following sets of actions:

   • To upload a YAML file

     1. Click Upload files.

        A file browser opens.

     2. In the file browser, select a YAML file on your computer. This file might be the one of the example runtimes that you downloaded from the kserve/modelmesh-serving repository.

        The embedded YAML editor opens and shows the contents of the file that you uploaded.

   • To enter YAML code directly in the editor

     1. Click Start from scratch.

        The embedded YAML editor opens with no content.

     2. Enter or paste YAML code directly in the embedded editor. The YAML that you paste might be copied from one of the example runtimes in the kserve/modelmesh-serving repository.

4. Optional: If you are adding one of the example runtimes in the kserve/modelmesh-serving repository, perform the following modifications:

   1. In the YAML editor, locate the kind field for your runtime. Update the value of this field to ServingRuntime.

   2. In the YAML editor, locate the containers.image field for your runtime. Based on the runtime that you are adding, update the field to one of the following:

      Nvidia Triton Inference Server

      image: nvcr.io/nvidia/tritonserver:21.06.1-py3

      Seldon Python MLServer

      image: seldonio/mlserver:0.5.2

      TorchServe

      image: pytorch/torchserve:0.6.0-cpu

5. In the metadata.name field, ensure that the value of the runtime you are adding is unique (that is, the value isn’t the same as for a runtime you have already added).

6. Optional: To configure a custom display name for the runtime that you are adding, add a metadata.annotations.openshift.io/display-name field and specify a value, as shown in the following example:

   apiVersion: serving.kserve.io/v1alpha1
   kind: ServingRuntime
   metadata:
     name: mlserver-0.x
     annotations:
       openshift.io/display-name: MLServer

   Note

   If you do not configure a custom display name for your runtime, OpenShift Data Science shows the value of the metadata.name field.

7. Click Add.

   The Serving runtimes page opens and shows the updated list of runtimes that are installed. Observe that the runtime you added is automatically enabled.

8. Optional: To edit your custom runtime, click the action menu (⋮) and select Edit.

   Note

   You cannot directly edit the OpenVINO Model Server runtime that is included in OpenShift Data Science by default. However, you can clone this runtime and edit the cloned version. You can then add the edited clone as a new, custom runtime. To do this, click the action menu beside the OpenVINO Model Server and select Clone.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project whose model server details you want to update.

   A project details page opens.

3. In the Models and model servers section, locate the model server you want to update. Click the action menu (⋮) and select Edit model server.

   The Configure model server dialog opens.

4. Update the model server properties, as follows:

   Note

   You cannot change the Serving runtime selection for a model server that is already configured. This protects against changing to a runtime that does not support already-deployed models.

   1. In the Model server name field, enter a new, unique name for the model server.
   2. In the Number of model replicas to deploy field, specify a value.

   3. From the Model server size list, select one of the following server sizes:

      • Small
      • Medium
      • Large
      • Custom

   4. Optional: If you selected Custom in the preceding step, configure the following settings in the Model server size section to customize your model server:

      1. In the CPUs requested field, specify a number of CPUs to use with your model server. Use the list beside this field to specify the value in cores or millicores.
      2. In the CPU limit field, specify the maximum number of CPUs to use with your model server. Use the list beside this field to specify the value in cores or millicores.
      3. In the Memory requested field, specify the requested memory for the model server in gibibytes (Gi).
      4. In the Memory limit field, specify the maximum memory limit for the model server in gibibytes (Gi).

   5. Optional: In the Model server GPUs field, specify a number of GPUs to use with your model server.

      Important

      OpenShift Data Science includes two versions of the OpenVINO Model Server (OVMS) runtime by default; a version that supports GPUs and one that does not. To use GPUs, from the Serving runtime list, you must select the version whose display name includes Supports GPUs.

      If you are using a custom model-serving runtime with your model server, you must ensure that your custom runtime supports GPUs and is appropriately configured to use them.

   6. Optional: In the Model route section, select the Make deployed models available through an external route check box to make your deployed models available to external clients.

   7. Optional: In the Token authorization section, select the Require token authentication check box to require token authentication for your model server. To finish configuring token authentication, perform the following actions:

      1. In the Service account name field, enter a service account name for which the token will be generated. The generated token is created and displayed in the Token secret field when the model server is configured.
      2. To add an additional service account, click Add a service account and enter another service account name.
5. Click Configure.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project from which you want to delete the model server.

   A project details page opens.

3. Click the action menu (⋮) beside the project whose model server you want to delete in the Models and model servers section and then click Delete model server.

   The Delete model server dialog opens.

4. Enter the name of the model server in the text field to confirm that you intend to delete it.
5. Click Delete model server.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. From the list of data science projects, click the name of the data science project that you want to share access to.

   A project details page opens.

3. Click the Permissions tab.

   The Permissions page for the project opens.

4. Provide one or more users with access to the project.

   1. In the Users section, click Add user.
   2. In the Name field, enter the user name of the user whom you want to provide access to the project.

   3. From the Permissions list, select one of the following access permission levels:

      • Admin: Users with this access level can edit project details and manage access to the project.
      • Edit: Users with this access level can view and edit project components, such as its workbenches, data connections, and storage.
   4. To confirm your entry, click Confirm ( ).
   5. Optional: To add an additional user, click Add user and repeat the process.

5. Provide one or more OpenShift groups with access to the project.

   1. In the Groups section, click Add group.

   2. From the Name list, select a group to provide access to the project.

      Note

      If you do not have cluster-admin or dedicated-admin permissions, the Name list is not visible. Instead, an input field is displayed enabling you to configure group permissions.

   3. From the Permissions list, select one of the following access permission levels:

      • Admin: Groups with this access permission level can edit project details and manage access to the project.
      • Edit: Groups with this access permission level can view and edit project components, such as its workbenches, data connections, and storage.
   4. To confirm your entry, click Confirm ( ).
   5. Optional: To add an additional group, click Add group and repeat the process.

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to change the access permissions of.

   A project details page opens.

3. Click the Permissions tab.

   The Permissions page for the project opens.

4. Update the user access permissions to the project.

   1. In the Name field, update the user name of the user whom you want to provide access to the project.

   2. From the Permissions list, update the user access permissions by selecting one of the following:

      • Admin: Users with this access level can edit project details and manage access to the project.
      • Edit: Users with this access level can view and edit project components, such as its workbenches, data connections, and storage.
   3. To confirm the update to the entry, click Confirm ( ).

5. Update the OpenShift groups access permissions to the project.

   1. From the Name list, update the group that has access to the project by selecting another group from the list.

      Note

      If you do not have cluster-admin or dedicated-admin permissions, the Name list is not visible. Instead, an input field is displayed enabling you to configure group permissions.

   2. From the Permissions list, update the group access permissions by selecting one of the following:

      • Admin: Groups with this access permission level can edit project details and manage access to the project.
      • Edit: Groups with this access permission level can view and edit project components, such as its workbenches, data connections, and storage.
   3. To confirm the update to the entry, click Confirm ( ).

Procedure

1. From the OpenShift Data Science dashboard, click Data Science Projects.

   The Data science projects page opens.

2. Click the name of the project that you want to change the access permissions of.

   A project details page opens.

3. Click the Permissions tab.

   The Permissions page for the project opens.

4. Click the action menu (⋮) beside the user or group whose access permissions you want to revoke and click Delete.