SEML is the missing link between the open-source workload scheduling system Slurm, the experiment management tool sacred, and a MongoDB experiment database. It is lightweight, hackable, written in pure Python, and scales to thousands of experiments.
Keeping track of computational experiments can be annoying and failure to do so can lead to lost results, duplicate running of the same experiments, and lots of headaches.
While workload scheduling systems such as Slurm make it easy to run many experiments in parallel on a cluster, it can be hard to keep track of which parameter configurations are running, failed, or completed.
sacred is a great tool to collect and manage experiments and their results, especially when used with a MongoDB. However, it is lacking integration with workload schedulers.
SEML enables you to
- very easily define hyperparameter search spaces using YAML files,
- run these hyperparameter configurations on a compute cluster using
Slurm, - and to track the experimental results using
sacredandMongoDB.
In addition, SEML offers many more features to make your life easier, such as
- automatically saving and loading your source code for reproducibility,
- easy debugging on Slurm or locally,
- automatically checking your experiment configurations,
- extending Slurm with local workers,
- and keeping track of resource usage (experiment runtime, RAM, etc.).
To get started, install SEML either via pip:
pip install semlor conda:
conda install -c conda-forge semlThen configure your MongoDB via:
seml configure # provide your MongoDB credentialsSee our simple example to get familiar with how SEML works.
Contact us at zuegnerd@in.tum.de or klicpera@in.tum.de for any questions.
Copyright (C) 2021
Daniel Zügner and Johannes Klicpera
Technical University of Munich