Installing Watson Machine Learning

A project administrator can install Watson Machine Learning on IBM® Cloud Pak for Data.

What permissions do you need to complete this task?
The permissions that you need depend on which tasks you must complete:
  • To install the Watson Machine Learning 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 install Watson Machine Learning, you must be an administrator of the project where you will install Watson Machine Learning. This project is identified by the ${PROJECT_CPD_INSTANCE} environment variable.
When do you need to complete this task?
If you didn't install Watson Machine Learning when you installed the platform, you can complete this task to add Watson Machine Learning to your environment.

If you want to install all of the Cloud Pak for Data components at the same time, follow the process in Installing 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 install 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 must be 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 installed in the project where you plan to install Watson Machine Learning, the common core services are automatically installed when you install Watson Machine Learning. This increases the amount of time the installation takes to complete.

Storage requirements
You must tell Watson Machine Learning what storage to use. The following storage classes are recommended. However, if you don't use these storage classes on your cluster, ensure that you specify a storage class with an equivalent definition.

* indicates that the storage class is used only if common core services needs to be installed.

Storage Storage classes
OpenShift® Data Foundation
  • ocs-storagecluster-cephfs
  • ocs-storagecluster-ceph-rbd
IBM Spectrum® Fusion ibm-spectrum-scale-sc
IBM Spectrum Scale Container Native ibm-spectrum-scale-sc
Portworx
  • portworx-rwx-gp3-sc

    (Equivalent to portworx-shared-gp3 in older installations)

  • portworx-gp3-sc
  • portworx-couchdb-sc *
  • portworx-elastic-sc *
NFS managed-nfs-storage
Amazon Elastic Block Store gp2-csi * or gp3-csi *

Block storage is supported but not required. If you specify block storage, you must also specify file storage is also required.
Amazon Elastic File System efs-nfs-client
IBM Cloud Block Storage ibmc-block-gold *

Block storage is supported but not required. If you specify block storage, you must also specify file storage is also required.
IBM Cloud File Storage ibmc-file-gold-gid or ibm-file-custom-gold-gid

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 installing Watson Machine Learning. If this task is not complete, see System requirements.
The workstation from which you will run the installation is set up as a client workstation and includes the following command-line interfaces:
  • Cloud Pak for Data CLI: cpd-cli
  • OpenShift CLI: oc
 If this task is not complete, see Setting up a client workstation.
The Cloud Pak for Data control plane is installed. If this task is not complete, see Installing the platform and services.
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. If this task is not complete, see Mirroring images to a private container registry.

Procedure

Complete the following tasks to install Watson Machine Learning:

  1. Logging in to the cluster
  2. Installing the operator
  3. Installing the service
  4. Validating the installation
  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 install 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.

If you complete this task and the OLM artifacts already exist on the cluster, the cpd-cli detects that you already have the OLM objects for the components at the specified release, the cpd-cli does not attempt to create the OLM objects again.

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: Install the Watson Machine Learning service.

Installing the service

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

Who needs to complete this task?
You must be an administrator of the project where you will install Watson Machine Learning.
When do you need to complete this task?
Complete this task if you want to add Watson Machine Learning to your environment.

To install 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
    IBM Spectrum Fusion storage

    Run the following command to create the custom resource.

    Remember: When you use IBM Spectrum Fusion storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically ibm-spectrum-scale-sc.
    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    IBM Spectrum Scale Container Native storage

    Run the following command to create the custom resource.

    Remember: When you use IBM Spectrum Scale Container Native storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically ibm-spectrum-scale-sc.
    cpd-cli manage apply-cr \
--components=wml \
--release=${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 ${STG_CLASS_FILE} point to the same storage class, typically managed-nfs-storage.
    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    AWS with EFS storage only

    Run the following command to create the custom resource.

    Remember: When you use EFS storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same RWX storage class.
    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    AWS with EFS and EBS 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
    IBM Cloud with IBM Cloud File Storage only

    Run the following command to create the custom resource.

    Remember: When you use IBM Cloud File Storage storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically ibmc-file-gold-gid or ibm-file-custom-gold-gid.
    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INSTANCE} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    IBM Cloud with IBM Cloud File Storage and IBM Cloud Block 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

Validating the installation

Watson Machine Learning is installed 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

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

However, if you plan to use Deep Learning, you must configure Watson Machine Learning Accelerator. For details, see Administering Watson Machine Learning.