Skip to main content

ClearML Session

Machine Learning and Deep Learning development is sometimes more challenging than traditional software development. If you are working on an average laptop or computer, and you have a sizeable dataset that requires significant computation, your local machine may not be able to provide you with the resources for an effective workflow.

If you can run and debug your code on your own machine, congrats you are lucky! Continue doing that, then clone your code in the UI and send it for long-term training on a remote machine.

If you are not that lucky, this section is for you :)

What Does ClearML Session Do?#

clearml-session is a feature that allows to launch a session of JupyterLab and VS Code, and to execute code on a remote machine that better meets resource needs. This feature provides local links to access JupyterLab and VS Code on a remote machine over a secure and encrypted SSH connection. By default, the JupyterLab and VS Code remote sessions use ports 8878 and 8898 respectively.

JupyterLab Window


VS Code Window


Remote PyCharm

You can also work with PyCharm in a remote session over SSH. Use the PyCharm Plugin to automatically sync local configurations with a remote session.

How it Works#

ClearML allows to leverage a resource (e.g. GPU or CPU machine) by utilizing the ClearML Agent. A ClearML Agent runs on a target machine, and ClearML Session instructs it to execute the Jupyter / VS Code server to develop remotely. After entering a clearml-session command with all specifications:

  1. clearml-session creates a new Task that is responsible for setting up the SSH and JupyterLab / VS Code environment according to your specifications on the host machine.

  2. The Task is enqueued, and a ClearML Agent pulls and executes it. The agent downloads the appropriate server and launches it.

  3. Once the agent finishes the initial setup of the interactive Task, the local cleaml-session connects to the host machine via SSH, and tunnels both SSH and JupyterLab over the SSH connection. If a Docker is specified, the JupyterLab environment runs inside the Docker.

  4. The CLI outputs access links to the remote JupyterLab and VS Code sessions:

    Interactive session is running:
    SSH: ssh root@localhost -p 8022 [password: c5d19b3c0fa9784ba4f6aeb568c1e036a4fc2a4bc7f9bfc54a2c198d64ceb9c8]
    Jupyter Lab URL: http://localhost:8878/?token=ff7e5e8b9e5493a01b1a72530d18181320630b95f442b419
    VSCode server available at http://localhost:8898/

    Notice the links are to 'localhost' since all communication to the remote server itself is done over secure SSH connection.

  5. Now start working on the code as if you're running on the target machine itself!


Running in Docker#

To run a session inside a Docker container, use the --docker flag and enter the docker image to use in the interactive session.

Installing Requirements#

clearml-session can install required Python packages when setting up the remote environment. Specify requirements in one of the following ways:

  • Attach a requirement.txt file to the command using --requirements </file/location.txt>.
  • Manually specify packages using --packages "<package_name>" (for example --packages "keras" "clearml"), and they'll be automatically installed.

Accessing a Git Repository#

To access a git repository remotely, add a --git-credentials flag and set it to true, so the local .git-credentials file is sent to the interactive session. This is helpful if working on private git repositories, and it allows for seamless cloning and tracking of git references, including untracked changes.

Re-launching and Shutting Down Sessions#

If a clearml-session was launched locally and is still running on a remote machine, users can easily reconnect to it. To reconnect to a previous session, execute clearml-session with no additional flags, and the option of reconnecting to an existing session will show up:

Connect to active session id=c7302b564aa945408aaa40ac5c69399c [Y]/n?`

If multiple sessions were launched from a local machine and are still active, choose the desired session:

Active sessions:
0*] 2021-05-09 12:24:11 id=ed48fb83ad76430686b1abdbaa6eb1dd
1] 2021-05-09 12:06:48 id=009eb34abde74182a8be82f62af032ea
Connect to session [0-1] or 'N' to skip

To shut down a remote session, which frees the clearml-agent and closes the CLI, enter "Shutdown". If a session is shutdown, there is no option to reconnect to it.

Connecting to an Existing Session#

If a clearml-session is running remotely, you can continue working on the session from any machine. When clearml-session is launched, it initializes a task with a unique ID in the ClearML Server.

To connect to an existing session:

  1. Go to the web UI, find the interactive session task (by default, it's in project "DevOps").
  2. Click on the ID button to the right of the task name, and copy the unique ID.
  3. Enter the following command: clearml-session --attach <session_id>.
  4. Click on the JupyterLab / VS Code link that is outputted, or connect directly to the SSH session

Starting a Debugging Session#

You can debug previously executed experiments registered in the ClearML system on a remote interactive session. Input into clearml-session the ID of a Task to debug, then clearml-session clones the experiment's git repository and replicates the environment on a remote machine. Then the code can be interactively executed and debugged on JupyterLab / VS Code.


The Task must be connected to a git repository, since currently single script debugging is not supported.

  1. In the ClearML web UI, find the experiment (Task) that needs debugging.
  2. Click on the ID button next to the Task name, and copy the unique ID.
  3. Enter the following command: clearml-session --debugging-session <experiment_id_here>
  4. Click on the JupyterLab / VS Code link, or connect directly to the SSH session.
  5. In JupyterLab / VS Code, access the experiment's repository in the environment/task_repository folder.

Command Line Options#

Command line optionsDescriptionDefault value
--jupyter-labDownload a JupyterLab environmenttrue
--vscode-serverDownload a VSCode environmenttrue
--public-ipRegister the public IP of the remote machine (if you are running the session on a public cloud)Session runs on the machine whose agent is executing the session
--init-scriptSpecify a BASH init script file to be executed when the interactive session is being set upnone or previously entered BASH script
--user-folderSpecify the path for the session's remote base folder for the sessionHome folder(~/) or previously entered user folder path
--config-fileSpecify a path to another configuration file for clearml-session to store its previous state.clearml_session.json or previously entered configuration file
--remote-gatewaySpecify a gateway IP to pass to the interactive session, if an external address needs to be accessednone
--base-task-idPass the ID of a task that will become the base task, so the session will use its configurationsnone or the previously entered base task
--disable-keepaliveDisable transparent proxy that keep sockets alive to maintain the connection to the remote resourcefalse
--queue-excluded-tagThe queue option list will exclude queues with specified tags. See the tags parameter in the queues.create API callnone
--queue-include-tagThe queue option list will include only queues with specified tags. See the tags parameter in the queues.create API callnone
--skip-docker-networkDon't pass the --network host flag to the Docker that is launching the remote session. See Networking using the host networkfalse
--usernameSet your own SSH username for the interactive sessionroot or a previously used username
--passwordSet your own SSH password for the interactive sessionA randomly generated password or a previously used one