Upgrading Watson Machine Learning from the Version 3.5 release

Information you need to complete this task

Review the following information before you upgrade Watson Machine Learning:

Environment variables

The commands in this task use environment variables so that you can run the commands exactly as written.

• If you don't have the script that defines the environment variables, see Setting up installation environment variables.
• To use the environment variables from the script, you must source the environment variables before you run the commands in this task, for example:

  source ./cpd_vars.sh

Security context constraint requirements

Watson Machine Learning uses the restricted security context constraint (SCC).

Installation location

Watson Machine Learning is installed in the same project (namespace) as the Cloud Pak for Data control plane. This project is identified by the ${PROJECT_CPD_INSTANCE} environment variable.

Common core services

Watson Machine Learning requires the Cloud Pak for Data common core services.

If the common core services are not at the required version for the release, the common core services will be automatically upgraded when you upgrade Watson Machine Learning. This increases the amount of time the upgrade takes to complete.

Storage requirements

You must tell Watson Machine Learning what storage you use in your existing installation. You cannot change the storage that is associated with Watson Machine Learning during an upgrade. Ensure that the environment variables point to the correct storage classes for your environment.

Before you begin

This task assumes that the following prerequisites are met:

Procedure

Complete the following tasks to upgrade Watson Machine Learning:

1. Logging in to the cluster
2. Installing the operator
3. Upgrading the service
4. Validating the upgrade
5. What to do next

Logging in to the cluster

To run cpd-cli manage commands, you must log in to the cluster.

To log in to the cluster:

1. Run the cpd-cli manage login-to-ocp command to log in to the cluster as a user with sufficient permissions to complete this task. For example:

   cpd-cli manage login-to-ocp \
   --username=${OCP_USERNAME} \
   --password=${OCP_PASSWORD} \
   --server=${OCP_URL}

   Tip: The login-to-ocp command takes the same input as the oc login command. Run oc login --help for details.

Installing the operator

The Watson Machine Learning operator simplifies the process of managing the Watson Machine Learning service on Red Hat® OpenShift Container Platform.

To upgrade Watson Machine Learning, you must install the Watson Machine Learning operator and create the Operator Lifecycle Manager (OLM) objects, such as the catalog source and subscription, for the operator.

Who needs to complete this task?

You must be a cluster administrator (or a user with the appropriate permissions to install operators) to create the OLM objects.

When do you need to complete this task?

Complete this task if the Watson Machine Learning operator and other OLM artifacts have not been created for the current release.

It is not necessary to run this command multiple times for each service that you plan to upgrade. If you complete this task and the OLM artifacts already exist on the cluster, the cpd-cli will recreate the OLM objects for all of the existing components in the ${PROJECT_CPD_OPS} project.

To install the operator:

1. Create the OLM objects for Watson Machine Learning:

   cpd-cli manage apply-olm \
   --release=${VERSION} \
   --cpd_operator_ns=${PROJECT_CPD_OPS} \
   --components=wml

   • If the command succeeds, it returns [SUCCESS]... The apply-olm command ran successfully.
   • If the command fails, it returns [ERROR] and includes information about the cause of the failure.

What to do next: Upgrade the Watson Machine Learning service.

Upgrading the service

After the Watson Machine Learning operator is installed, you can upgrade Watson Machine Learning.

Who needs to complete this task?

You must be an administrator of the project where Watson Machine Learning is installed.

When do you need to complete this task?

Complete this task for each instance of Watson Machine Learning that is associated with an instance of Cloud Pak for Data Version 4.6.

To upgrade the service:

1. Create the custom resource for Watson Machine Learning.

   The command that you run depends on the storage on your cluster:

   ---

   Red Hat OpenShift Data Foundation storage

   Run the following command to create the custom resource.

   cpd-cli manage apply-cr \
   --components=wml \
   --release=${VERSION} \
   --cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
   --block_storage_class=${STG_CLASS_BLOCK} \
   --file_storage_class=${STG_CLASS_FILE} \
   --license_acceptance=true

   ---

   Portworx storage

   cpd-cli manage apply-cr \
   --components=wml \
   --release=${VERSION} \
   --cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
   --storage_vendor=portworx \
   --license_acceptance=true

   ---

   NFS storage

   Run the following command to create the custom resource.

   Remember: When you use NFS storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class.

   cpd-cli manage apply-cr \
   --components=wml \
   --release=${VERSION} \
   --cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
   --block_storage_class=${STG_CLASS_BLOCK} \
   --file_storage_class=${STG_CLASS_FILE} \
   --license_acceptance=true

   ---

Validating the upgrade

Watson Machine Learning is upgraded when the apply-cr command returns [SUCCESS]... The apply-cr command ran successfully.

However, you can optionally run the cpd-cli manage get-cr-status command if you want to confirm that the custom resource status is Completed:

cpd-cli manage get-cr-status \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--components=wml

What to do next