Installing Watson Machine Learning

An instance administrator can install Watson Machine Learning on IBM® Software Hub Version 5.1.

Who needs to complete this task?

Instance administrator To install Watson Machine Learning, you must be an instance administrator. An instance administrator has permission to install software in the following projects:

The operators project for the instance

The operators for this instance of Watson Machine Learning are installed in the operators project.

In the installation commands, the ${PROJECT_CPD_INST_OPERATORS} environment variable refers to the operators project.

The operands project for the instance

The custom resources for the control plane and Watson Machine Learning are installed in the operands project.

In the installation commands, the ${PROJECT_CPD_INST_OPERANDS} environment variable refers to the operands project.

When do you need to complete this task?

Review the following options to determine whether you need to complete this task:

  • If you want to install multiple services at the same time, follow the process in Running a batch installation of solutions and services instead.
  • If you didn't install Watson Machine Learning as part of a batch installation, complete this task to add Watson Machine Learning to your environment.

    Repeat as needed If you are responsible for multiple instances of IBM Software Hub, you can repeat this task to install more instances of Watson Machine Learning on the cluster.

Information you need to complete this task

Review the following information before you install Watson Machine Learning:

Version requirements

All of the components that are associated with an instance of IBM Software Hub must be installed at the same release. For example, if the IBM Software Hub control plane is installed at Version 5.1.3, you must install Watson Machine Learning at Version 5.1.3.

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, run:
    source ./cpd_vars.sh
Security context constraint

Watson Machine Learning works with the default Red Hat® OpenShift® Container Platform security context constraint, restricted-v2.

Common core services

Watson Machine Learning requires the IBM Software Hub common core services.

If the common core services are not installed in the operands project for the instance, the common core services are automatically installed when you install Watson Machine Learning. The common core services installation increases the amount of time the installation takes to complete.

Storage requirements
You must specify storage classes when you install Watson Machine Learning. 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 Notes Storage classes
OpenShift Data Foundation When you install the service, specify file storage and block storage.
  • File storage: ocs-storagecluster-cephfs
  • Block storage: ocs-storagecluster-ceph-rbd
IBM Fusion Data Foundation When you install the service, specify file storage and block storage.
  • File storage: ocs-storagecluster-cephfs
  • Block storage: ocs-storagecluster-ceph-rbd
IBM Fusion Global Data Platform When you install the service, specify the same storage class for both file storage and block storage.
  • File storage:

    Either of the following storage classes:

    • ibm-spectrum-scale-sc
    • ibm-storage-fusion-cp-sc
  • Block storage:

    Either of the following storage classes:

    • ibm-spectrum-scale-sc
    • ibm-storage-fusion-cp-sc
IBM Storage Scale Container Native When you install the service, specify the same storage class for both file storage and block storage.
  • File storage: ibm-spectrum-scale-sc
  • Block storage: ibm-spectrum-scale-sc
Portworx When you install the service, the --storage_vendor=portworx option ensures that the service uses the correct storage classes.
  • File storage: portworx-rwx-gp3-sc

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

  • Block storage:
    • portworx-gp3-sc
    • portworx-couchdb-sc *
    • portworx-elastic-sc *
NFS When you install the service, specify the same storage class for both file storage and block storage.
  • File storage: managed-nfs-storage
  • Block storage: managed-nfs-storage
Amazon Elastic storage
When you install the service, you can specify:
  • File storage only
  • File storage and block storage (recommended)

File storage is provided by Amazon Elastic File System. Block storage is provided by Amazon Elastic Block Store.
  • File storage: efs-nfs-client
  • Block storage:

    Either of the following storage classes:

    • gp2-csi
    • gp3-csi
NetApp Trident When you install the service, specify the same storage class for both file storage and block storage.
  • File storage: ontap-nas
  • Block storage: ontap-nas
Nutanix When you install the service, specify file storage and block storage.
  • File storage: nutanix-file
  • Block storage: nutanix-volume

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:
  • IBM Software Hub CLI: cpd-cli
  • OpenShift CLI: oc
 If this task is not complete, see Setting up a client workstation.
The IBM Software Hub control plane is installed. If this task is not complete, see Installing an instance of IBM Software Hub.
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.
For environments that use a private container registry, such as air-gapped environments, the cpd-cli is configured to pull the olm-utils-v3 image from the private container registry. If this task is not complete, see Pulling the olm-utils-v3 image from the private container registry.
If you plan to use features that require GPUs, the operators that are required to use GPUs are installed. If this task is not complete, see Installing operators for services that require GPUs.

Procedure

Complete the following tasks to install Watson Machine Learning:

  1. Installing the service
  2. Validating the installation
  3. What to do next

Installing the service

To install Watson Machine Learning:

  1. Log the cpd-cli in to the Red Hat OpenShift Container Platform cluster:
    ${CPDM_OC_LOGIN}
    Remember: CPDM_OC_LOGIN is an alias for the cpd-cli manage login-to-ocp command.
  2. Run the following command to create the required OLM objects for Watson Machine Learning in the operators project for the instance:
    cpd-cli manage apply-olm \
--release=${VERSION} \
--cpd_operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--components=wml
    Wait for the cpd-cli to return the following message before you proceed to the next step:
    [SUCCESS]... The apply-olm command ran successfully

    If the apply-olm fails, see Troubleshooting the apply-olm command during installation or upgrade.

  3. 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_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    IBM Fusion 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_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    IBM Fusion Global Data Platform storage
    Remember: When you use IBM Fusion Global Data Platform storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically ibm-spectrum-scale-sc or ibm-storage-fusion-cp-sc.

    Run the following command to create the custom resource.

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

    Run the following command to create the custom resource.

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

    Run the following command to create the custom resource.

    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--storage_vendor=portworx \
--license_acceptance=true
    NFS storage
    Remember: When you use NFS storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically managed-nfs-storage.

    Run the following command to create the custom resource.

    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    AWS with EFS storage only
    Remember: When you use EFS storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically efs-nfs-client.

    Run the following command to create the custom resource.

    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--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_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--license_acceptance=true
    NetApp Trident
    Remember: When you use NetApp Trident storage, both ${STG_CLASS_BLOCK} and ${STG_CLASS_FILE} point to the same storage class, typically ontap-nas.

    Run the following command to create the custom resource.

    cpd-cli manage apply-cr \
--components=wml \
--release=${VERSION} \
--cpd_instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--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

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

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

What to do next

No post-upgrade steps. The service is ready to use.

However, if you plan to use Deep Learning, you must configure the IBM Software Hub scheduling service. For details, see Installing shared cluster components for IBM Software Hub.

If you plan to use trained NLP models in Watson Machine Learning deployments, you must follow these steps in Installing pre-trained NLP models for Python-based notebook runtimes.

To configure MIG support, see MIG Support in OpenShift Container Platform.