Cloudera Docs

Using PBJ Workbench

PBJ Workbench offers a classic workbench user interface powered by the open-source Jupyter protocol prepackaged within a runtime image. Users can select this runtime image when launching a session. The open-source Jupyter infrastructure eliminates the dependency on proprietary Cloudera AI code for building a Docker images, enabling faster creation of runtime images. PBJ Workbench enables you to build runtime images using Ubuntu base images, including non-Cloudera base images, and integrate them with the Cloudera AI Workbench.

ML Runtimes have been open-sourced and are available in the cloudera/ml-runtimes GitHub repository. To gain a deeper understanding of the Runtime environment or to build a new Runtime from scratch, you can access the Dockerfiles used to build the ML Runtime container images in this repository.

The PBJ Workbench is available by default, but you have to select it when you launch a session.

  1. In the Cloudera console, click the Cloudera AI tile.

    The Cloudera AI Workbenches page displays.

  2. Click on the name of the workbench.
    The workbench Home page displays.
  3. Select the required workbench.
  4. Select the required project or create a new project.
  5. Click the New Session button.
  6. In Runtime > Editor, select PBJ Workbench
  7. Click Start Session.

Now you can use the PBJ Workbench as you would the normal workbench.