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Get Started

This self-paced guide helps you explore the platform’s capabilities to setup, configure and use Jupyter Notebooks for end user self service.

These environments are optimized to support a range of data science and machine learning workloads with preconfigured libraries and dependencies. Users can select from multiple environment types based on their workload needs, including lightweight setups, data science workbenches, and GPU-accelerated machine learning environments.

Configure & Setup

Prerequisites

Before proceeding, ensure the following:

  • Host Cluster: Ensure that a Kubernetes host cluster is available and ready for Jupyter Notebook deployment.
  • Agent Configuration: Configure agents through Global Settings or during cluster provisioning

Load Template

  • As an Org Admin, go to Environment -> Environment Templates
  • Select system-pod-as-a-service and click the edit icon for a specific version to make the required changes, or click on New Version
  • Provide the following details:
    • A unique name for the template.
    • A version name (e.g., 1.0).
  • Go to Agents and configure the required Agent to drive the workflow. Select the Agent from the dropdown list. If no Agents are shown, the Agent may need to be set up (refer to the prerequisites).

Create Project

Configuration

  • Customize and templatize all Jupyter Notebook configurations using input variables, including:
    • Cluster and Access settings: Host server, kubeconfig, client certificate and key data, certificate authority data
    • Notebook parameters: Notebook name, notebook profile, notebook profiles list
    • Resource settings: CPU request, CPU limit, memory request, memory limit, GPU limit, PVC storage
    • Networking settings: Namespace, ingress domain, ingress IP, ingress namespace, subdomain, custom domain, custom secret
    • Cluster management: Host cluster name, project, cluster name
  • Restrict user edits for specific variables by:
    • Setting overrides to Not Allowed for selected variables
    • Defining default values that cannot be edited at the time of launch

Create Project

  • Save the template as a Draft to allow ongoing edits until the configuration is finalized. Once all changes are complete, set it as an Active Version to freeze the version. Learn more about version management.

Save Version

Customize Jupyter Notebook SKU

As a Org Admin or PaaS Admin, use the PaaS Studio to configure a custom Service Profile to make the template available for self-service deployment by end users. In this step, you will customize and personalize the options that you wish to present to the end user.

  • Navigate to PaaS Studio > Service Profiles
  • Select the project where the template was created and click New Service Profile
  • Enter a Name for the profile and select the previously created Template and Version
  • For Service Type, select Notebooks
  • For Will compute be auto-created, select Yes
  • Click Save & Continue

Save Version

  • On the next screen, go to Input Settings, enable or disable overrides for the required parameters, and click Save Changes

Save Version

  • Navigate to Output Settings and click Add Output.
  • Enter the Name and, optionally, the Label and Resource.
  • If required, add more outputs

Save Version