Configure a Local IDE using an SSH Gateway

The specifics for how to configure a local IDE to work with Cloudera Machine Learning are dependent on the local IDE you want to use.

Cloudera Machine Learning relies on the SSH functionality of the editors to connect to the SSH endpoint on your local machine created with the cdswctl client. Users establish an SSH endpoint on their machine with the cdswctl client. This endpoint acts as the bridge that connects the editor on your machine and the Cloudera Machine Learning deployment.

The following steps are a high-level description of the steps a user must complete:

  1. Establish an SSH endpoint with the CML CLI client. See Initialize an SSH Endpoint.
  2. Configure the local IDE to use Cloudera Machine Learning as the remote interpreter.
  3. Optionally, sync files with tools (like mutagen, SSHFS, or the functionality built into your IDE) from Cloudera Machine Learning to your local machine. Ensure that you adhere to IT policies.
  4. Edit the code in the local IDE and run the code interactively on Cloudera Machine Learning.
  5. Sync the files you edited locally to Cloudera Data Science Workbench.
  6. Use the Cloudera Machine Learning web UI to perform actions such as deploying a model that uses the code you edited.

You can see an end-to-end example for PyCharm configuration in the CML Editors Pycharm.