Upgrading Jupyter Notebooks with Python 3.7 with GPU

A project administrator can upgrade the Jupyter Notebooks with Python 3.7 with GPU service on IBM® Cloud Pak for Data.

Before you begin

Required role: To complete this task, you must be an administrator of the project (namespace) where Jupyter Notebooks with Python 3.7 with GPU is installed.

Before you upgrade Jupyter Notebooks with Python 3.7 with GPU, ensure that:

The Cloud Pak for Data control plane is already upgraded on your Red Hat® OpenShift® cluster. For details, see Upgrading IBM Cloud Pak for Data.
Jupyter Notebooks with Python 3.7 with GPU is backed up. For details, see Backing up and restoring your project.
The cluster meets the minimum requirements for Jupyter Notebooks with Python 3.7 with GPU. For details, see System requirements for services.
You completed the steps in Preparing to install and upgrade services.

If you are upgrading multiple services on your cluster, you must run the upgrades one at a time and wait until the upgrade completes before upgrading another service. You cannot run the upgrades in parallel.

Tip: For a list of all available options, enter the following command:

./cpd-cli upgrade --help

Important: For the Jupyter Notebooks with Python 3.7 for GPU upgrade, you must have Watson™ Studio already installed.

Procedure

Complete the appropriate steps to upgrade Jupyter Notebooks with Python 3.7 with GPU on your environment:
- Upgrading on clusters connected to the internet
- Upgrading on air-gapped clusters
Verifying that the upgrade completed successfully
Checking for available patches
Complete the tasks listed in What to do next

Upgrading on clusters connected to the internet

From your installation node:

Change to the directory where you placed the Cloud Pak for Data command-line interface and the repo.yaml file.
Log in to your Red Hat OpenShift cluster as a project administrator:
```
oc login OpenShift_URL:port
```

Run the following command to see a preview of what will change when you upgrade the service.

Important: If you are using the internal Red Hat OpenShift registry and you are using the default self-signed certificate, specify the --insecure-skip-tls-verify flag to prevent x509 errors.

./cpd-cli upgrade \
--repo ./repo.yaml \
--assembly  runtime-addon-py37gpu \
--arch Cluster_architecture \  
--namespace Project \
--storageclass Storage_class_name \
--transfer-image-to Registry_location \
--cluster-pull-prefix Registry_from_cluster \
--ask-pull-registry-credentials \
--ask-push-registry-credentials \
--latest-dependency \
--dry-run

Important: By default, this command gets the latest assembly. If you want to upgrade to a specific version of Jupyter Notebooks with Python 3.7 with GPU, add the following line to your command after the --assembly flag:

--version Assembly_version \

The --latest-dependency flag gets the latest version of the dependent assemblies. If you remove the --latest-dependency flag, the installer will either leave the dependent assemblies at the current version or get the minimum version of the dependent assemblies.

If you are upgrading with Portworx storage, add the following line to your upgrade command after the --storageclass flag:

--override-config portworx \

If you are upgrading with OpenShift Container Storage, add the following line to your upgrade command after the --storageclass flag:

--override-config ocs \

Replace the following values:

Variable	Replace with
`Assembly_version`	The version of Jupyter Notebooks with Python 3.7 with GPU that you want to install. The assembly versions are listed in System requirements for services.
`Cluster_architecture`	Specify the architecture of your cluster hardware: For x86-64 hardware, remove this flag or specify `x86_64` For POWER hardware, specify `ppc64le`
`Project`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.
`Storage_class_name`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services. Refresh 2 or later If you are using the 3.5.2 version of the `cpd-cli`, remove the `--storageclass` flag from your command. The `cpd-cli upgrade` command uses the storage class that was specified during installation.
`Registry_location`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.
`Registry_from_cluster`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.

Rerun the previous command without the --dry-run flag to upgrade the service.

Upgrading on air-gapped clusters

From your installation node:

Change to the directory where you placed the Cloud Pak for Data command-line interface.
Log in to your Red Hat OpenShift cluster as a project administrator:
```
oc login OpenShift_URL:port
```

Run the following command to see a preview of what will change when you upgrade the service.

Important: If you are using the internal Red Hat OpenShift registry:

Do not specify the --ask-pull-registry-credentials parameter.
If you are using the default self-signed certificate, specify the --insecure-skip-tls-verify flag to prevent x509 errors.

./cpd-cli upgrade \
--assembly  runtime-addon-py37gpu \
--arch Cluster_architecture \
--namespace Project \
--storageclass Storage_class_name \
--cluster-pull-prefix Registry_from_cluster \
--ask-pull-registry-credentials \
--load-from Image_directory_location \
--latest-dependency \
--dry-run

Note: If the assembly was downloaded using the delta-images command, remove the --latest-dependency flag from the command. If you don't remove the --latest-dependency flag you will get an error indicating that the flag cannot be used.

If you are upgrading with Portworx storage, add the following line to your upgrade command after the --storageclass flag:

--override-config portworx \

If you are upgrading with OpenShift Container Storage, add the following line to your upgrade command after the --storageclass flag:

--override-config ocs \

Replace the following values:

Variable	Replace with
`Cluster_architecture`	Specify the architecture of your cluster hardware: For x86-64 hardware, remove this flag or specify `x86_64` For POWER hardware, specify `ppc64le`
`Project`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.
`Storage_class_name`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services. Refresh 2 or later If you are using the 3.5.2 version of the `cpd-cli`, remove the `--storageclass` flag from your command. The `cpd-cli upgrade` command uses the storage class that was specified during installation.
`Registry_from_cluster`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.
`Image_directory_location`	Use the value provided by your cluster administrator. You should have obtained this information when you completed Preparing to install and upgrade services.

Rerun the previous command without the --dry-run flag to upgrade the service.

Verifying that the upgrade completed successfully

From your installation node:

Run the following command:
```
./cpd-cli status \
--assembly  runtime-addon-py37gpu \
--namespace Project
```
Replace Project with the value you used in the preceding commands.
- If the upgrade completed successfully, the status of the assembly and the modules in the assembly is Ready.
- If the upgrade failed, contact IBM Support for assistance.

Checking for available patches

Determine whether there are any patches available the version of Jupyter Notebooks with Python 3.7 with GPU that you installed:

Clusters connected to the internet

Run the following command to check for patches:

./cpd-cli status \
--repo ./repo.yaml \
--namespace Project \ 
--assembly  runtime-addon-py37gpu \
--patches \
--available-updates

Air-gapped clusters

See the list of Available patches for Jupyter Notebooks with Python 3.7 with GPU.

If you need to apply patches to the service, follow the guidance in Applying patches.

What to do next

The service is ready to use. For details, see GPU node settings.