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Remote Jupyter Tutorial

In this tutorial we will learn how to launch a remote interactive session on Jupyter Notebook using clearml-session. We will be using two machines. A local one, where we will be using an interactive session of Jupyter, and a remote machine, where a clearml-agent will run and spin an instance of the remote session.

Prerequisites#

  • clearml-session package installed (pip install clearml-session)
  • At least one clearml-agent running on a remote host. See installation details. Configure the clearml-agent to listen to the default queue (clearml-agent daemon --queue default)
  • An SSH client installed on machine being used. To verify, open terminal, execute ssh, and if no error is received, it should be good to go.

Steps#

Step 1: Launch clearml-session#

Execute the clearml-session command with the following command line options:

clearml-session --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 --packages "clearml" "tensorflow>=2.2" "keras" --queue default
  • Enter a docker image --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
  • Enter required python packages --packages "clearml" "tensorflow>=2.2" "keras"
  • Specify the resource queue --queue default.
note

There is an option to enter a project name using --project <name>. If no project is input, the default project name is "DevOps"

After launching the command, the clearml-agent listening to the default queue spins a remote Jupyter environment with the specifications. It will automatically connect to the docker on the remote machine.

The terminal should return output with the session's configuration details, which should look something like this:

Interactive session config:
{
"base_task_id": null,
"git_credentials": false,
"jupyter_lab": true,
"password": "0879348ae41fb944004ff89b9103f09592ec799f39ae34e5b71afb46976d5c83",
"queue": "default",
"vscode_server": true
}

Step 2: Launch Interactive Session#

When the CLI asks whether to Launch interactive session [Y]/n?, press 'Y' or 'Enter'.

The terminal should output information regarding the status of the environment-building process, which should look something like this:

Creating new session
New session created [id=35c0af81ae6541589dbae1efb747f388]
Waiting for remote machine allocation [id=35c0af81ae6541589dbae1efb747f388]
.Status [queued]
...Status [in_progress]
Remote machine allocated
Setting remote environment [Task id=35c0af81ae6541589dbae1efb747f388]
Setup process details: https://app.community.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log
Waiting for environment setup to complete [usually about 20-30 seconds]

Step 3: Connect to Remote Notebook#

Then the CLI will output a link to the ready environment:

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/

Click on the JupyterLab link, which will open the remote session

Now, let's execute some code in the remote session!

Step 4: Execute Code#

  1. Open up a new Notebook.

  2. In the first cell of the notebook, clone the ClearML Repo.

    !git clone https://github.com/allegroai/clearml.git
  3. In the second cell of the notebook, we are going to run this script from the repository that we cloned.

    %run clearml/examples/frameworks/keras/keras_tensorboard.py

    Look in the script, and notice that it makes use of ClearML, Keras, and TensorFlow, but we don't need to install these packages in Jupyter, because we specified them in the --packages flag of clearml-session.

Step 5: Shut Down Remote Session#

To shut down the remote session, which will free the clearml-agent and close the CLI, enter "Shutdown".

Connection is up and running
Enter "r" (or "reconnect") to reconnect the session (for example after suspend)
Ctrl-C (or "quit") to abort (remote session remains active)
or "Shutdown" to shutdown remote interactive session