milatools 0.1.9

Tools to work with the Mila cluster

milatools

The milatools package provides the mila command, which is meant to help with connecting to and interacting with the Mila cluster.

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Warning

The mila command is meant to be used on your local machine. Trying to run it on the cluster will fail with an error

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Install

Requires Python >= 3.8

We recommend using uv in order to use milatools in its own isolated environment instead of alterring your default Python environment or creating and activating a new one manually.

uv tool install milatools

Alternatively:

pip install milatools

Or, for bleeding edge version:

uv tool install git+https://github.com/mila-iqia/milatools.git
# OR
pip install git+https://github.com/mila-iqia/milatools.git

After installing milatools, start with mila init:

mila init

Commands

mila init

Set up your access to the mila cluster interactively. Have your username and password ready!

• Set up your SSH config for easy connection with ssh mila
• Set up your public key if you don't already have them
• Copy your public key over to the cluster for passwordless auth
• Set up a public key on the login node to enable ssh into compute nodes
• new: Add a special SSH config for direct connection to a compute node with ssh mila-cpu

mila docs/intranet

• Use mila docs <search terms> to search the Mila technical documentation
• Use mila intranet <search terms> to search the Mila intranet

Both commands open a browser window. If no search terms are given you are taken to the home page.

mila code

Connect a VSCode instance to a compute node. mila code first allocates a compute node using slurm (you can pass slurm options as well using --alloc), and then calls the code command with the appropriate options to start a remote coding session on the allocated node.

You can simply Ctrl+C the process to end the session.

usage: mila code [-h] [--cluster {mila,cedar,narval,beluga,graham}] [--alloc ...]
                 [--command VALUE] [--job VALUE] [--node VALUE] [--persist]
                 PATH

positional arguments:
  PATH                  Path to open on the remote machine

options:
  -h, --help            show this help message and exit
  --alloc ...           Extra options to pass to slurm
  --cluster {mila,cedar,narval,beluga,graham}
                        Which cluster to connect to.
  --command VALUE       Command to use to start vscode (defaults to "code" or the value
                        of $MILATOOLS_CODE_COMMAND)
  --job VALUE           Job ID to connect to
  --node VALUE          Node to connect to
  --persist             Whether the server should persist or not

For example:

mila code path/to/my/experiment

The --alloc option may be used to pass extra arguments to salloc when allocating a node (for example, --alloc --gres=gpu:1 to allocate 1 GPU). --alloc should be at the end, because it will take all of the arguments that come after it.

If you already have an allocation on a compute node, you may use the --node NODENAME or --job JOBID options to connect to that node.

mila serve

The purpose of mila serve is to make it easier to start notebooks, logging servers, etc. on the compute nodes and connect to them.

usage: mila serve [-h] {connect,kill,list,lab,notebook,tensorboard,mlflow,aim} ...

positional arguments:
  {connect,kill,list,lab,notebook,tensorboard,mlflow,aim}
    connect             Reconnect to a persistent server.
    kill                Kill a persistent server.
    list                List active servers.
    lab                 Start a Jupyterlab server.
    notebook            Start a Jupyter Notebook server.
    tensorboard         Start a Tensorboard server.
    mlflow              Start an MLFlow server.
    aim                 Start an AIM server.

optional arguments:
  -h, --help            show this help message and exit

For example, to start jupyterlab with one GPU, you may write:

mila serve lab --alloc --gres gpu:1

You can of course write any SLURM arguments after --alloc.

Ending the connection will end the server, but the --persist flag can be used to prevent that. In that case you would be able to write mila serve connect jupyter-lab in order to reconnect to your running instance. Use mila serve list and mila serve kill to view and manage any running instances.