tributaries-ml 1.0.5

A library for mass-deploying UnifiedML apps on slurm-enabled servers.

Tributaries

A library for mass-deploying UnifiedML apps on slurm-enabled remote servers.

pip install tributaries-ml

Examples

Server

Simply create and run a python file with a server configuration like this one:

# MyServer.py

from tributaries import my_server


@my_server(sweep='path/to/my/sweep.py')
def main():
    ...
    return server, username, password, func, app_name_paths, commands, sbatch


if __name__ == '__main__':
    main()

That method must return the server, username, and password.

Optionally:

• Any additional func that needs to be run (e.g. connecting to a VPN).
• An app_name_paths dictionary of names and paths to any UnifiedML apps' run scripts you'd like to use, e.g. {'name_of_my_app': 'path/to/name_of_my_app/Run.py'}, or leave this blank to use the remote server's root home directory and ML as the run script.
• A commands list or string of any extra environment-setup commands you may need to pass to the remote server command-line and deploy config such as activating a conda environment for example.
• Any additional sbatch string text you'd like to add to the deploy config.

You may use one of the blueprint server files provided.

Sweep

Note the Server decorator accepts a sweep= file path.

You may define a sweep file like this one:

# path/to/my/sweep.py

from tributaries import my_sweep, my_plots, my_checkpoints

my_sweep.hyperparams = [
    # Hyperparam set 1
    '... experiment=Exp1',

    # Hyperparam set 2
    '... experiment=Exp2'
]

my_sweep.app = 'name_of_my_app'  # Corresponds to an app name in 'app_name_paths' of Server definition

# Logs to download
my_plots.plots = [['Exp1', 'Exp2']]  # Names of experiments to plot together in a single plot

my_checkpoints.experiments = ['Exp1', 'Exp2']  # Names of experiments to download checkpoints for

The my_sweep and my_plots toggles have additional configurations that can be used to further customize the launching and plots.

See here for examples.

Running

That's it. Running it via python MyServer.py will launch the corresponding sweep experiments on your remote server. Add the plot=true flag to instead download plots back down to your local machine.

Add checkpoints=true to download checkpoints.

Launching

python MyServer.py

Plotting & Logs

python MyServer.py plot=true

Checkpoints

python MyServer.py checkpoints=true

Extra

Note: Tributaries launching fully works for non-UnifiedML apps too. Also, for convenience, tributaries hyperparams='...' app='run.py' can be used as a general slurm launcher on your remote servers.

One more thing: if your remote UnifiedML apps are git-ssh enabled, Tributaries will automatically try syncing with the latest branch via a git pull. You can disable automatic GitHub-syncing with the github=false flag.

Licensed under the MIT license.