Upgrading Watson Machine Learning Accelerator from Version 4.5 to Version 4.6

A project administrator can upgrade Watson Machine Learning Accelerator from Cloud Pak for Data Version 4.5 to Version 4.6.

What permissions do you need to complete this task?

The permissions that you need depend on which tasks you must complete:

To update the Watson Machine Learning Accelerator operators, you must have the appropriate permissions to create operators and you must be an administrator of the project where the Cloud Pak for Data operators are installed. This project is identified by the ${PROJECT_CPD_OPS} environment variable.
To upgrade Watson Machine Learning Accelerator, you must be an administrator of:
- The project where Watson Machine Learning Accelerator is installed. This project is identified by the ${PROJECT_CPD_INSTANCE} environment variable.
- If the Watson Machine Learning Accelerator service instance is in a tethered project, you must be an administrator of the tethered project. The tethered project is identified by the ${PROJECT_TETHERED} environment variable.

When do you need to complete this task?: If you didn't upgrade Watson Machine Learning Accelerator when you upgraded the platform to Version 4.6, you can complete this task to upgrade your existing Watson Machine Learning Accelerator installation.
If you want to upgrade all of the Cloud Pak for Data components at the same time, follow the process in Upgrading the platform and services instead.

Important: All of the Cloud Pak for Data components in a deployment must be installed at the same release.

Information you need to complete this task

Review the following information before you upgrade Watson Machine Learning Accelerator:

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
```

Installation location

Watson Machine Learning Accelerator 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.

The Watson Machine Learning Accelerator service instance might be installed in either:

The same project as the control plane.
A project that is tethered to the control plane. This project is identified by the ${PROJECT_TETHERED} environment variable.

Storage requirements: You must tell Watson Machine Learning Accelerator what storage you use in your existing installation. You cannot change the storage that is associated with Watson Machine Learning Accelerator 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:

Prerequisite	Where to find more information
The cluster meets the minimum requirements for Watson Machine Learning Accelerator.	If this task is not complete, see System requirements.
The workstation from which you will run the upgrade is set up as a client workstation and the `cpd-cli` has the latest version of the `olm-utils-play` image.	If this task is not complete, see Setting up a client workstation.
The Cloud Pak for Data control plane is upgraded.	If this task is not complete, see Upgrading the platform and services.
For environments that use a private container registry, such as air-gapped environments, the Watson Machine Learning Accelerator software images are mirrored to the private container registry.	If this task is not complete, see Mirroring images to a private container registry.

Prerequisite services

Before you upgrade Watson Machine Learning Accelerator, ensure that the following services are upgraded and running:

Upgrade the scheduling service. See Upgrading the scheduling service.

Procedure

Complete the following tasks to upgrade Watson Machine Learning Accelerator:

Logging in to the cluster
Updating the operator
Upgrading the service
Validating the upgrade
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:

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.

Updating the operator

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

To upgrade Watson Machine Learning Accelerator, ensure that all of the Operator Lifecycle Manager (OLM) objects in the ${PROJECT_CPD_OPS} project, such as the catalog sources and subscriptions, are upgraded to the appropriate release. All of the OLM objects must be at the same release.

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 only if the OLM artifacts have not been updated for the current release using the cpd-cli manage apply-olm command with the --upgrade=true option.
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 update the operator:

Update the OLM objects:
```
cpd-cli manage apply-olm \
--release=${VERSION} \
--cpd_operator_ns=${PROJECT_CPD_OPS} \
--upgrade=true
```
- 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 Accelerator service.

Upgrading the service

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

Who needs to complete this task?: You must be an administrator of the project where Watson Machine Learning Accelerator is installed.

When do you need to complete this task?: Complete this task for each instance of Watson Machine Learning Accelerator that is associated with an instance of Cloud Pak for Data Version 4.6.

To upgrade the service:

If installed in a tethered project, set up the tethered namespace.

cpd-cli manage setup-tethered-ns --cpd_instance_ns= ${PROJECT_CPD_INSTANCE} --tethered_instance_ns= ${PROJECT_TETHERED}

Update the custom resource for Watson Machine Learning Accelerator.

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

Red Hat OpenShift Data Foundation storage

Run the following command to update the custom resource.

If the service and the service instance are in the same project as the control plane, run:

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

If the service instance is in a tethered project, run:

cpd-cli manage apply-cr \
--components=wml_accelerator,wml_accelerator_instance \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--extra-vars='wmla_instance_ns=${PROJECT_TETHERED}' \
--tethered_instance_ns=${PROJECT_TETHERED} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true \
--upgrade=true

IBM Storage Scale Container Native storage

Run the following command to update the custom resource.

Remember: When you use IBM Storage Scale Container Native storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class.

If the service and the service instance are in the same project as the control plane, run:

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

If the service instance is in a tethered project, run:

cpd-cli manage apply-cr \
--components=wml_accelerator,wml_accelerator_instance \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--extra-vars='wmla_instance_ns=${PROJECT_TETHERED}' \
--tethered_instance_ns=${PROJECT_TETHERED} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true \
--upgrade=true

Portworx storage

Run the following command to update the custom resource.

If the service and the service instance are in the same project as the control plane, run:

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

If the service instance is in a tethered project, run:

cpd-cli manage apply-cr \
--components=wml_accelerator,wml_accelerator_instance \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--extra-vars='wmla_instance_ns=${PROJECT_TETHERED}' \
--tethered_instance_ns=${PROJECT_TETHERED} \
--storage_vendor=portworx \
--license_acceptance=true \
--upgrade=true

NFS storage

Run the following command to update the custom resource.

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

If the service and the service instance are in the same project as the control plane, run:

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

If the service instance is in a tethered project, run:

cpd-cli manage apply-cr \
--components=wml_accelerator,wml_accelerator_instance \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--extra-vars='wmla_instance_ns=${PROJECT_TETHERED}' \
--tethered_instance_ns=${PROJECT_TETHERED} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true \
--upgrade=true

Validating the upgrade

Watson Machine Learning Accelerator 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:

What to do next

To connect your Watson™ Machine Learning Accelerator service to the Watson Machine Learning service, see Connecting Watson Machine Learning Accelerator to Watson Machine Learning.
Add users to the Watson Machine Learning Accelerator instance and provide access to the service console, see Add users to the Watson Machine Learning Accelerator instance.
Change your default administrative passwords for monitoring resources, see Updating passwords.