Installing Watson Machine Learning

An instance administrator can install Watson Machine Learning on IBM® Software Hub Version 5.4.

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 5.4.0, you must install Watson Machine Learning at Version 5.4.0.

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:

System requirements

This task assumes that the cluster meets the minimum requirements for Watson Machine Learning.

Where to find more information
If this task is not complete, see System requirements.

In addition, if you plan to use features that require GPU, ensure that you have the appropriate type and number of GPU for Watson Machine Learning.

Where to find more information
If this task is not complete, see GPU requirements.

Workstation

This task assumes that the workstation from which you will run the installation is set up as a client workstation and has the following command-line interfaces:

IBM Software Hub CLI: cpd-cli
OpenShift CLI: oc
Helm CLI: helm

Where to find more information
If this task is not complete, see Setting up a client workstation.

Control plane

This task assumes that the IBM Software Hub control plane is installed.

Where to find more information
If this task is not complete, see Installing an instance of IBM Software Hub.

Private container registry

If your environment uses a private container registry (for example, your cluster is air-gapped), this task assumes that the following tasks are complete:

The Watson Machine Learning software images are mirrored to the private container registry.
Where to find more information

If this task is not complete, see Mirroring images to a private container registry.
The cpd-cli is configured to pull the olm-utils-v4 image from the private container registry.
Where to find more information

If this task is not complete, see Pulling the olm-utils-v4 image from the private container registry.

GPU operators

Where to find more information
If this task is not complete, see Mirroring images to a private container registry.

Where to find more information
If this task is not complete, see Pulling the olm-utils-v4 image from the private container registry.

If you plan to use features that require GPUs, this task assumes that the operators required to use GPUs are installed.

Where to find more information
If this task is not complete, see Installing operators for services that require GPUs.

Cluster-scoped resources

This task assumes that the cluster-scoped resources, such as custom resource definitions, cluster roles, and cluster role bindings, exist.

Where to find more information
If this task is not complete, see Creating cluster-scoped resources for the IBM Software Hub platform and services.

Image pull secrets

This task assumes that the secrets that contain the image pull credentials for the instance exist.

Where to find more information
If this task is not complete, see Creating image pull secrets for an instance of IBM Software Hub.

Prerequisite services

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

Procedure

Complete the following tasks to install Watson Machine Learning:

Installing the service
Validating the installation
What to do next

Installing the service

To install Watson Machine Learning:

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.

Install the operator and custom resource for Watson Machine Learning.

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

Red Hat OpenShift Data Foundation storage

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

IBM Fusion Data Foundation storage

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

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.

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

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.

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

Portworx storage

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--storage_vendor=portworx \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

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.

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

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.

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

AWS with EFS and EBS storage

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

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.

cpd-cli manage install-components \
--license_acceptance=true \
--components=wml \
--release=${VERSION} \
--patch_id=${PATCH_ID} \
--operator_ns=${PROJECT_CPD_INST_OPERATORS} \
--instance_ns=${PROJECT_CPD_INST_OPERANDS} \
--block_storage_class=${STG_CLASS_BLOCK} \
--file_storage_class=${STG_CLASS_FILE} \
--image_pull_prefix=${IMAGE_PULL_PREFIX} \
--image_pull_secret=${IMAGE_PULL_SECRET}

Validating the installation

Watson Machine Learning is installed when the install-components command returns:

[SUCCESS]... The install-components 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

Watson Machine Learning is ready to use but might require additional configuration depending on your use case:

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.
If you want to deploy models that require GPUs and want to configure Multi-Instance GPU (MIG) support, see Configuring NVIDIA Multi-Instance GPU (MIG).