mlflow-slurm 1.0.5

Backend implementation for running MLFlow projects on Slurm

MLFlow-Slurm

Backend for executing MLFlow projects on Slurm batch system

Usage

Install this package in the environment from which you will be submitting jobs. If you are submitting jobs from inside jobs, make sure you have this package listed in your conda or pip environment.

Just list this as your --backend in the job run. You should include a json config file to control how the batch script is constructed:

mlflow run --backend slurm \
          --backend-config slurm_config.json \
          examples/sklearn_elasticnet_wine

It will generate a batch script named after the job id and submit it via the Slurm sbatch command. It will tag the run with the Slurm JobID

Configure Jobs

You can set values in a json file to control job submission. The supported properties in this file are:

Config File Setting	Use
partition	Which Slurm partition should the job run in?
account	What account name to run under
environment	List of additional environment variables to add to the job
exports	List of environment variables to export to the job
gpus_per_node	On GPU partitions how many GPUs to allocate per node
gres	SLURM Generic RESources requests
mem	Amount of memory to allocate to CPU jobs
modules	List of modules to load before starting job
nodes	Number of nodes to request from SLURM
ntasks	Number of tasks to run on each node
exclusive	Set to true to insure jobs don't share a node with other jobs
time	Max CPU time job may run
sbatch-script-file	Name of batch file to be produced. Leave blank to have service generate a script file name based on the run ID

Sequential Worker Jobs

There are occasions where you have a job that can't finish in the maximum allowable wall time. If you are able to write out a checkpoint file, you can use sequential worker jobs to continue the job where it left off. This is useful for training deep learning models or other long running jobs.

To use this, you just need to provide a parameter to the mlflow run command

  mlflow run --backend slurm -c ../../slurm_config.json -P sequential_workers=3 .

This will the submit the job as normal, but also submit 3 additional jobs that each depend on the previous job. As soon as the first job terminates, the next job will start. This will continue until all jobs have completed.

Development

The slurm docker deployment is handy for testing and development. You can start up a slurm environment with the included docker-compose file