Third-Party Editors

In addition to the native Cloudera Machine Learning editor, you can configure Cloudera Machine Learning to work with third-party, browser-based IDEs such as Jupyter and also certain local IDEs that run on your machine, such as PyCharm.

When you bring your own editor, you still get many of the benefits Cloudera Machine Learning behind an editor interface you are familiar with:
  • Dependency management that lets you share code with confidence
  • CDH client configurations
  • Automatic Kerberos authentication through Cloudera Data Science Workbench
  • Reuse code in other Cloudera Machine Learning features such as experiments and jobs
  • Collaboration features such as teams
  • Compliance with IT rules for where compute, data, and/or code must reside. For example, compute occurs within the Cloudera Data Science Workbench deployment, not the local machine. Browser IDEs run within a Cloudera Machine Learning session and follow all the same compliance rules. Local IDEs, on the other hand, can bring data or code to a user's machine. Therefore, Site Administrators can opt to disable local IDEs to balance user productivity with compliance concerns.

In the Cloudera Machine Learning documentation, browser-based IDEs like Jupyter will be referred to as "browser IDEs". IDEs such as PyCharm that run on your machine outside of your browser will be referred to as "local IDEs" because they run on your local machine. You can use the browser or local IDE of your choice to edit and run code interactively.

Note that you can only edit and run code interactively with the IDEs. Tasks such as creating a project or deploying a model require the Cloudera Machine Learning web UI and cannot be completed through an editor.