Installing Watson Machine Learning

A project administrator can upgrade the Watson Machine Learning service on IBM® Cloud Pak for Data

Permissions you need for this task
You must be an administrator of the OpenShift® project (Kubernetes namespace) where you will deploy Watson Machine Learning.
Information you need to complete this task
  • Watson Machine Learning needs only the restricted security context constraint (SCC).
  • Watson Machine Learning must be installed in the same project as Cloud Pak for Data.
  • Watson Machine Learning can be installed in either:
    • The project where Cloud Pak for Data is installed
    • A project that is tethered to the project where Cloud Pak for Data is installed
  • Watson Machine Learning requires the Cloud Pak for Data common core services. If the common core services are not installed in the project where you plan to install Watson Machine Learning, the common core services will be automatically installed when you install Watson Machine Learning, which will increase the amount of time the installation takes to complete.
  • Supported storage classes:
    • OpenShift Container Storage: ocs-storagecluster-cephfs
    • NFS: managed-nfs-storage
    • Portworx: portworx-shared-gp3
    • IBM Cloud File Storage: ibmc-file-gold-gid

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 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. 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.
    • 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
        license:
          accept: true
          license: Enterprise|Standard     # Specify the license you purchased
        storageVendor: ocs
        storageClass: ocs-storagecluster-cephfs          #if you use a different storage class, replace it with the appropriate storage class                      
      EOF
    • 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
        license:
          accept: true
          license: Enterprise|Standard     # Specify the license you purchased
        storageVendor: portworx
        storageClass: portworx-shared-gp3          #if you use a different storage class, replace it with the appropriate storage class                    
      EOF
    • 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
        license:
          accept: true
          license: Enterprise|Standard     # Specify the license you purchased
        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

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.