Installing Watson Machine Learning

Before you begin

Ensure that the cluster meets the minimum requirements for installing Watson Machine Learning. For details, see System requirements.

Additionally, ensure that a cluster administrator completed the required Pre-installation tasks for your environment. Specifically, verify that a cluster administrator completed the following tasks:

1. Cloud Pak for Data is installed. For details, see Installing Cloud Pak for Data.
2. For environments that use a private container registry, such as air-gapped environments, the Watson Machine Learning software images are mirrored to the private container registry. For details, see Mirroring images to your container registry.
3. The cluster is configured to pull the Watson Machine Learning software images. For details, see Configuring your cluster to pull images.
4. The Watson Machine Learning catalog source exists. For details, see Creating catalog sources.
5. The Watson Machine Learning operator subscription exists. For details, see Creating operator subscriptions.

If these tasks are not complete, the Watson Machine Learning installation will fail.

Procedure

Complete the following tasks to install Watson Machine Learning:

1. Installing the service
2. Verifying the installation
3. Choosing a service upgrade plan
4. What to do next

Installing the service

To install Watson Machine Learning:

1. Log in to Red Hat® OpenShift Container Platform as a user with sufficient permissions to complete the task:

   oc login OpenShift_URL:port

2. Create a WmlBase custom resource to install Watson Machine Learning. Follow the appropriate guidance for your environment.
   Important: By creating a WmlBase custom resource with spec.license.accept: true, you are accepting the license terms for Watson Machine Learning. You can find links to the relevant licenses in IBM Cloud Pak for Data License Information.

   • The cluster uses Red Hat OpenShift Container Storage

     Create a custom resource with the following format.

     cat <<EOF |oc apply -f -
     apiVersion: wml.cpd.ibm.com/v1beta1
     kind: WmlBase
     metadata:
       name: wml-cr     # This is the recommended name, but you can change it
       namespace: project-name     # Replace with the project where you will install Watson Machine Learning
       labels:
         app.kubernetes.io/instance: wml-cr
         app.kubernetes.io/managed-by: ibm-cpd-wml-operator
         app.kubernetes.io/name: ibm-cpd-wml-operator
     spec:
       ignoreForMaintenance: false
       scaleConfig: small    #This value can be set to small or medium
       license:
         accept: true
         license: Enterprise | Standard     # Specify the license you purchased.
       version: 4.0.9
       storageVendor: ocs
       storageClass: ocs-storagecluster-cephfs          #if you use a different storage class, replace it with the appropriate storage class                      
     EOF

   • The cluster uses IBM Spectrum Scale Container Native

     Create a custom resource with the following format.

     cat <<EOF |oc apply -f -
     apiVersion: wml.cpd.ibm.com/v1beta1
     kind: WmlBase
     metadata:
       name: wml-cr     # This is the recommended name, but you can change it
       namespace: project-name     # Replace with the project where you will install Watson Machine Learning
       labels:
         app.kubernetes.io/instance: wml-cr
         app.kubernetes.io/managed-by: ibm-cpd-wml-operator
         app.kubernetes.io/name: ibm-cpd-wml-operator
     spec:
       ignoreForMaintenance: false
       scaleConfig: small    #This value can be set to small or medium
       license:
         accept: true
         license: Enterprise | Standard     # Specify the license you purchased.
       version: 4.0.9
       storageClass: ibm-spectrum-scale-sc          #if you use a different storage class, replace it with the appropriate storage class                      
     EOF

   • The cluster uses Portworx

     Create a custom resource with the following format.

     cat <<EOF |oc apply -f -
     apiVersion: wml.cpd.ibm.com/v1beta1
     kind: WmlBase
     metadata:
       name: wml-cr     # This is the recommended name, but you can change it
       namespace: project-name     # Replace with the project where you will install Watson Machine Learning
       labels:
         app.kubernetes.io/instance: wml-cr
         app.kubernetes.io/managed-by: ibm-cpd-wml-operator
         app.kubernetes.io/name: ibm-cpd-wml-operator
     spec:
       ignoreForMaintenance: false
       scaleConfig: small    #This value can be set to small or medium
       license:
         accept: true
         license: Enterprise | Standard     # Specify the license you purchased
       version: 4.0.9
       storageVendor: portworx
       storageClass: portworx-shared-gp3          #if you use a different storage class, replace it with the appropriate storage class                    
     EOF

   • The cluster uses NFS

     Create a custom resource with the following format.

     cat <<EOF |oc apply -f -
     apiVersion: wml.cpd.ibm.com/v1beta1
     kind: WmlBase
     metadata:
       name: wml-cr     # This is the recommended name, but you can change it
       namespace: project-name     # Replace with the project where you will install Watson Machine Learning
       labels:
         app.kubernetes.io/instance: wml-cr
         app.kubernetes.io/managed-by: ibm-cpd-wml-operator
         app.kubernetes.io/name: ibm-cpd-wml-operator
     spec:
       ignoreForMaintenance: false
       scaleConfig: small    #This value can be set to small or medium
       license:
         accept: true
         license: Enterprise | Standard     # Specify the license you purchased
       version: 4.0.9
       storageVendor: ""
       storageClass: managed-nfs-storage          #if you use a different storage class, replace it with the appropriate storage class                   
     EOF

Verifying the installation

When you create the custom resource, the Watson Machine Learning operator processes the contents of the custom resource and starts up the microservices that comprise Watson Machine Learning, including WmlBase. (The WmlBase microservice is defined by the wml-cr custom resource.) Watson Machine Learning is installed when the WmlBase status is Completed.

To check the status of the installation:

1. Change to the project where you installed Watson Machine Learning:

   oc project project-name

2. Get the status of Watson Machine Learning (wml-cr):

   oc get WmlBase wml-cr -o jsonpath='{.status.wmlStatus} {"\n"}'

   Watson Machine Learning is ready when the command returns Completed

Choosing a service upgrade plan

You can choose how Watson Machine Learning is upgraded when you install a newer version of the Watson Machine Learning operator on the cluster.

Automatic upgrade (recommended)

If you want Watson Machine Learning to be automatically upgraded when you install a newer version of the Watson Machine Learning operator on the cluster, remove the version entry from the WmlBase custom resource.

To remove the version entry, run the following command. You must update the command with the appropriate project name before you run the command.

oc patch WmlBase wml-cr \
--namespace project-name \
--type=json \
--patch '[{ "op": "remove", "path": "/spec/version" }]'

Manual upgrade

If you want to manually upgrade Watson Machine Learning after you install a newer version of the Watson Machine Learning operator, you can pin the installation at a specific version in the WmlBase custom resource.

By default, when you create the WmlBase custom resource, it includes the version entry, so no additional action is required.

If you removed the version entry from the WmlBase custom resource, run the following command to pin the installation at Version 4.0.9. You must update the command with the appropriate project name before you run the command.

oc patch WmlBase wml-cr \
--namespace project-name \
--type=merge \
--patch '{"spec": {"version":"4.0.9"}}'

For a list of operand versions supported by the Watson Machine Learning operator, see Operator and operand versions.

What to do next

The service is ready to use. For details, see Watson Machine Learning overview

The service must be configured before users can access the service. For details, see Administering Watson Machine Learning.