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Hello, I Would Like To Start A Discussion About

Hello, I would like to start a discussion about clearml-session .

Clearml-session uses ssh tunneling by default. Although ssh tunneling has advantages such as security and convenience in accessing remote servers from local machines, it also has disadvantages such as the need to authenticate with clearml server for each local machine, the requirement to always have ssh tunneling connection, and slow data transfer speeds.

For these reasons, I modified the code for clearml-agent and clearml-session to enable port opening for ssh, vs code, and jupyter, and developed a Kubernetes service to enable connections to each of these tools. With this modification, developers do not need to authenticate on their local machines to use clearml-session or clearml SDK. After cloning a task and modifying the Docker image and selecting queue developers can immediately connect to ssh, vs code, or JupyterLab using the external IP listed in the configuration.

I am interested in hearing your thoughts on this approach.

  
  
Posted 12 months ago
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@<1524922424720625664:profile|TartLeopard58> how is authentication handles in this case?

  
  
Posted 12 months ago

The clearml server I installed is a self-hosted server, and developers log in using a fixed ID and password for authentication. That’s it!

Futhermore, to access ssh/vscode/jupyterlab directly without ssh tunneling, I modified the clearml-session script, and once I upload this script to the DevOps project in draft status, developers clone it to their own project. Then, they enqueue and wait for the command and URL to access ssh/vscode/jupyterlab, which will be displayed.

  
  
Posted 12 months ago