Snakemake executor plugin: slurm PyPI - Version PyPI - License

SLURM is a widely used batch system for performance compute clusters. This executor plugin allows to use it in a seamless and straightforward way.


Install this plugin by installing it with pip or mamba, e.g.:

pip install snakemake-executor-plugin-slurm


In order to use the plugin, run Snakemake (>=8.0) with the corresponding value for the executor flag:

snakemake --executor slurm ...

with ... being any additional arguments you want to use.

Further details

The Executor Plugin for HPC Clusters using the SLURM Batch System

The general Idea

To use this plugin, log in to your cluster’s head node (sometimes called the “login” node), activate your environment as usual and start Snakemake. Snakemake will then submit your jobs as cluster jobs.

Specifying Account and Partition

Most SLURM clusters have two mandatory resource indicators for accounting and scheduling, the account and a partition, respectively. These resources are usually omitted from Snakemake workflows in order to keep the workflow definition independent of the platform. However, it is also possible to specify them inside of the workflow as resources in the rule definition.

To specify them at the command line, define them as default resources:

$ snakemake --executor slurm --default-resources slurm_account=<your SLURM account> slurm_partition=<your SLURM partition>

If individual rules require e.g. a different partition, you can override the default per rule:

$ snakemake --executor slurm --default-resources slurm_account=<your SLURM account> slurm_partition=<your SLURM partition> --set-resources <somerule>:slurm_partition=<some other partition>

Usually, it is advisable to persist such settings via a configuration profile, which can be provided system-wide, per user, and in addition per workflow.

Ordinary SMP jobs

Most jobs will be carried out by programs that are either single-core scripts or threaded programs, hence SMP (shared memory programs) in nature. Any given threads and mem_mb requirements will be passed to SLURM:

rule a:
    input: ...
    output: ...
    threads: 8

This will give jobs from this rule 14GB of memory and 8 CPU cores. Using the SLURM executor plugin, we can alternatively define:

rule a:
    input: ...
    output: ...

instead of the threads parameter. Parameters in the resources section will take precedence.

MPI jobs

Snakemake's SLURM backend also supports MPI jobs, see snakefiles-mpi{.interpreted-text role=”ref”} for details. When using MPI with SLURM, it is advisable to use srun as an MPI starter.

rule calc_pi:
      "{resources.mpi} -n {resources.tasks} calc-pi-mpi > {output} 2> {log}"

Note that the -n {resources.tasks} is not necessary in the case of SLURM, but it should be kept in order to allow execution of the workflow on other systems, e.g. by replacing srun with mpiexec:

$ snakemake --set-resources calc_pi:mpi="mpiexec" ...

Running Jobs locally

Not all Snakemake workflows are adapted for heterogeneous environments, particularly clusters. Users might want to avoid the submission of all rules as cluster jobs. Non-cluster jobs should usually include short jobs, e.g. internet downloads or plotting rules.

To label a rule as a non-cluster rule, use the localrules directive. Place it on top of a Snakefile as a comma-separated list like:

localrules: <rule_a>, <rule_b>

Advanced Resource Specifications

A workflow rule may support several resource specifications. For a SLURM cluster, a mapping between Snakemake and SLURM needs to be performed.

You can use the following specifications:






the partition a rule/job is to use



the walltime per job in minutes



may hold features on some clusters


mem, mem_mb

memory a cluster node must

provide (mem: string with unit), mem_mb: i



memory per reserved CPU



number of concurrent tasks / ranks



number of cpus per task (in case of SMP, rather use threads)



number of nodes

Each of these can be part of a rule, e.g.:

    input: ...
    output: ...
        partition=<partition name>
        runtime=<some number>

Please note: as --mem and --mem-per-cpu are mutually exclusive on SLURM clusters, their corresponding resource flags mem/mem_mb and mem_mb_per_cpu are mutually exclusive, too. You can either reserve the memory a compute node has to provide(--mem flag) or the memory required per CPU (--mem-per-cpu flag). Depending on your cluster’s settings hyperthreads are enabled. SLURM does not make any distinction between real CPU cores and those provided by hyperthreads. SLURM will try to satisfy a combination of mem_mb_per_cpu and cpus_per_task and nodes if the nodes parameter is not given.

Note that it is usually advisable to avoid specifying SLURM (and compute infrastructure) specific resources (like constraint) inside your workflow because that can limit the reproducibility when executed on other systems. Consider using the --default-resources and --set-resources flags to specify such resources at the command line or (ideally) within a profile.

A sample configuration file as specified by the --workflow-profile flag might look like this:

    slurm_partition: "<your default partition>"
    slurm_account:   "<your account>

        slurm_partition: "<other partition>" # deviating partition for this rule
        runtime: 60 # 1 hour
        slurm_extra: "'--nice=150'"
        mem_mb_per_cpu: 1800
        cpus_per_task: 40

Additional Custom Job Configuration

SLURM installations can support custom plugins, which may add support for additional flags to sbatch. In addition, there are various sbatch options not directly supported via the resource definitions shown above. You may use the slurm_extra resource to specify additional flags to sbatch:

rule myrule:
    input: ...
    output: ...
        slurm_extra="'--qos=long --mail-type=ALL --mail-user=<your email>'"

Again, rather use a profile to specify such resources.

Inquiring about Job Information and Adjusting the Rate Limiter

The executor plugin for SLURM uses unique job names to inquire about job status. It ensures inquiring about job status for the series of jobs of a workflow does not put too much strain on the batch system’s database. Human readable information is stored in the comment of a particular job. It is a combination of the rule name and wildcards. You can ask for it with the sacct or squeue commands, e.g.:

sacct -o JobID,State,Comment%40

Note, the “%40” after Comment ensures a width of 40 characters. This setting may be changed at will. If the width is too small, SLURM will abbreviate the column with a + sign.

For running jobs, the squeue command:

squeue -u $USER -o %i,%P,%.10j,%.40k

Here, the .<number> settings for the ID and the comment ensure a sufficient width, too.

Snakemake will check the status of your jobs 40 seconds after submission. Another attempt will be made in 10 seconds, then 20, etcetera with an upper limit of 180 seconds.

Using Profiles

When using profiles, a command line may become shorter. A sample profile could look like this:

__use_yte__: true
executor: slurm
latency-wait: 60
default-storage-provider: fs
  - persistence
  - software-deployment
  - sources
  - source-cache
local-storage-prefix: "<your node local storage prefix>"

It will set the executor to be this SLURM executor, ensure sufficient file system latency and allow automatic stage-in of files using the file system storage plugin.

Note, that you need to set the SNAKEMAKE_PROFILE environment variable in your ~/.bashrc file, e.g.:

export SNAKEMAKE_PROFILE="$HOME/.config/snakemake"

Further note, that there is further development ongoing to enable differentiation of file access patterns.


When put together, a frequent command line looks like:

$ snakemake --workflow-profile <path> \
> -j unlimited # assuming an unlimited number of jobs
> --default-resources slurm_account=<account> slurm_partition=<default partition> \
> --configfile config/config.yaml \
> --directory <path> # assuming a data path not relative to the workflow