Submitit

Submitit

Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps submission and provide access to results, logs and more. Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Submitit allows to switch seamlessly between executing on Slurm or locally. Development is in progress for an OAR plugin, to facilitate the passage between OAR and Slurm based resource managers. Source code, issues and pull requests can be found here.

Basic usage

Submitit installation

pip can be used to install the stable release of submitit:

flille:

pip install submitit

otherwise, conda can be used to install submitit from the conda-forge:

flille:

conda install -c conda-forge submitit

an installation from Source can also be used to get the latest version on the main branch:

flille:

pip install git+https://github.com/facebookincubator/submitit@main#egg=submitit

Performing an addition with Submitit

Here is a Python script example which allows to execute an addition job on Slurm, OAR or locally.

import submitit
from operator import truediv

def add(a, b):
    return a + b

# logs are dumped in the folder
executor = submitit.AutoExecutor(folder="log_test")

job_addition = executor.submit(add, 5, 7)  # will compute add(5, 7)
print('job_addition: ', job_addition)  # ID of your job
output = job_addition.result()  # waits for completion and returns output
print('job_addition output: ', output)
assert output == 12  # 5 + 7 = 12...  your addition was computed on the cluster

The example script can be launched on frontend as follow:

flille:

python3 this-script.py

Advanced usage

Parameters and configuration

The AutoExecutor in the basic example above is the submission interface, for OAR/Slurm clusters and for local jobs.

To use the cluster specific options with the AutoExecutor, they must be appended by the cluster name, e.g. slurm_partition="cpu_devel", oar_queue="default". These cluster specific options will be ignored on other clusters.

Otherwise, cluster specific options can be used with cluster specific Executor, without the cluster name prefix, e.g, SlurmExecutor, OarExecutor.

The following table is giving the mapping between Dask parameters and OAR/Slurm parameters.

Comparison with Dask-jobqueue

The key difference with submitit is that dask.distributed distributes the jobs to a pool of workers (see the cluster variable above) while submitit jobs are directly jobs on the cluster. In that sense submitit is a lower level interface than dask.distributed and you get more direct control over your jobs, including individual stdout and stderr, and possibly checkpointing in case of preemption and timeout. On the other hand, you should avoid submitting multiple small tasks with submitit, which would create many independent jobs and possibly overload the cluster, while you can do it without any problem through dask.distributed