slurm_sweep: hyperparameter sweeps with W&B on SLURM clusters

slurm_sweep is the missing (small) piece to efficiently run hyperparameter sweeps on SLURM clusters by combining the power of weights and biases (W&B) and simple_slurm. It allows you to efficiently parallelize sweeps with job arrays, while tracking experiments and results on W&B. All you need is:

• W&B account.
• config.yaml file that defines your sweep.
• train.py script, that specifies the actual training and evaluation.

Getting started

Create an account on W&B and take a look at our examples in the examples folder. These contain both config.yaml and train.py scripts.

The config file

You need config file in yaml format. This file should have three sections:

• general: you need to define at least the project_name and the entity for the sweep on wandB.
• slurm: any valid slurm option. Depends on your cluster, see the simple_slurm docs.
• wandb: standard W&B config for a hyperparameter sweep.

The training script

This needs to be a python script that defines the training and evaluation logic. It should call wandb.init() and retrieve parameters from wandb.config. It can log values using wandb.log. See the W&B docs.

Submission

Once you're ready, you can test your config file using slurm-sweep validate_config config.yaml. If this passes, create a submission script using slurm-sweep configure-sweep config.yaml, and submit with sbatch submit.sh.

Installation

You need to have Python 3.10 or newer installed on your system. If you don't have Python installed, we recommend installing uv.

There are two alternative options to install slurm_sweep:

• Install the latest release from PyPI:

  pip install slurm_sweep

• Install the latest development version:

  pip install git+https://github.com/quadbio/slurm_sweep.git@main

Release notes

See the changelog.

Contact

If you found a bug, please use the issue tracker.