Cloudera Docs

Provisioning an ML Workspace

In Cloudera Machine Learning on Private Cloud, the Machine Learning Workspace provides a space for the data scientists' work. After your Administrator has created or given you access to an environment, you can set up a workspace.

The first user to access the Machine Learning Workspace after it is created must have the EnvironmentAdmin role assigned.
  1. Log in to the Cloudera Data Platform Private Cloud web interface using your corporate credentials or other credentials that you received from your Cloudera Data Platform administrator.
  2. Click Machine Learning Workspaces.
  3. Click Provision Workspace. The Provision Workspace panel displays.
  4. In Provision Workspace, fill out the following fields.
    1. Workspace Name - Give the Cloudera Machine Learning Workspace a name. For example, test-cml. Do not use capital letters in the workspace name.
    2. Select Environment - From the dropdown, select the environment where the Machine Learning Workspace must be provisioned. If you do not have any environments available to you in the dropdown, contact your Cloudera Data Platform administrator to gain access.
    3. Namespace - Enter the namespace to use for the Machine Learning Workspace.
    4. NFS Server - Select Internal to use an NFS server that is integrated into the Kubernetes cluster. This is the recommended selection at this time.
      The path to the internal NFS server is already set in the environment.
  5. In Production Machine Learning enable the following features.
    1. Enable Governance - Enables advanced lineage and governance features.
      Governance Principal Name - If Enable Governance is selected, you can use the default value of mlgov, or enter an alternative name. The alternative name must be present in your environment and be given permissions in Ranger to allow the Machine LearningGovernance service deliver events to Atlas.
    2. Enable Model Metrics - Enables exporting metrics for models to a PostgreSQL database.
  6. In Other Settings enable the following features.
    1. Enable TLS - Select this to enable https access to the workspace.
      To enable TLS, follow the guidelines in Deploying an ML Workspace with support for TLS .
    2. Enable Monitoring - Administrators (users with the EnvironmentAdmin role) can use a Grafana dashboard to monitor resource usage in the provisioned workspace.
    3. Cloudera Machine Learning Static Subdomain - This is a custom name for the workspace endpoint, and it is also used for the URLs of models, applications, and experiments. Only one workspace with the specific subdomain endpoint name can be running at a time. You can create a wildcard certificate for this endpoint in advance. The workspace name has this format: <static subdomain name>.<application domain>
  7. Click Provision Workspace. The new workspace provisioning process takes several minutes.

After the workspace is provisioned, you can log in by clicking the workspace name on the page. The first user to log in must be the administrator.

Test backing up of the Cloudera Machine Learning Workspace. Ensure that the backup completes successfully, and then ensure you have a process to back up the workspace at regular intervals.