Provision an ML Workspace
In CML on Private Cloud, the ML Workspace is where data scientists get their work done. After your Admin has created or given you access to an environment, you can set up a workspace. Only one workspace can be created per environment.
- Log in to the CDP Private Cloud web interface using your corporate credentials or other credentials that you received from your CDP administrator.
- Click ML Workspaces.
- Click Provision Workspace. The Provision Workspace panel displays.
In Provision Workspace, fill out the following
- Workspace Name - Give the ML workspace a name. For example, test-cml.
- Select Environment - From the dropdown, select the environment where the ML workspace must be provisioned. If you do not have any environments available to you in the dropdown, contact your CDP admin to gain access.
- Namespace - Enter the namespace to use for the ML workspace.
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.
In Production Machine Learning, select to enable the
Enable Governance - Enables advanced lineage and
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 MLGovernance service deliver events to Atlas.
- Enable Model Metrics - Enables exporting metrics for models to a PostgreSQL database.
- Enable Governance - Enables advanced lineage and governance features.
In Other Settings, select to enable the following
- Enable TLS - Select this to enable https access to the workspace.
- Enable Monitoring - Administrators (users with the EnvironmentAdmin role) can use a Grafana dashboard to monitor resource usage in the provisioned workspace.
- 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 Machine Learning Workspaces page. The first user to log in must be the administrator.