Running Snakemake on the cluster

Last updated on 2024-02-01 | Edit this page

Overview

Questions

“How do I run my Snakemake rule on the cluster?”

Objectives

“Define rules to run locally and on the cluster”

What happens when we want to make our rule run on the cluster rather than the login node? The cluster we are using uses Slurm, and it happens that Snakemake has built in support for Slurm, we just need to tell it that we want to use it.

Snakemake uses the executor option to allow you to select the plugin that you wish to execute the rule. The quickest way to apply this to your Snakefile is to define this on the command line. Let’s try it out

BASH

[ocaisa@node1 ~]$ snakemake -j1 -p --executor slurm hostname_login

OUTPUT

Building DAG of jobs...
Retrieving input from storage.
Nothing to be done (all requested files are present and up to date).

Nothing happened! Why not? When it is asked to build a target, Snakemake checks the ‘last modification time’ of both the target and its dependencies. If any dependency has been updated since the target, then the actions are re-run to update the target. Using this approach, Snakemake knows to only rebuild the files that, either directly or indirectly, depend on the file that changed. This is called an incremental build.

Incremental Builds Improve Efficiency

By only rebuilding files when required, Snakemake makes your processing more efficient.

Running on the cluster

We need another rule now that executes the hostname on the cluster. Create a new rule in your Snakefile and try to execute it on cluster with the option --executor slurm to snakemake.

Show me the solution

The rule is almost identical to the previous rule save for the rule name and output file:

PYTHON

rule hostname_remote:
    output: "hostname_remote.txt"
    input:
    shell:
        "hostname > hostname_remote.txt"

You can then execute the rule with

BASH

[ocaisa@node1 ~]$ snakemake -j1 -p --executor slurm hostname_remote

OUTPUT

Building DAG of jobs...
Retrieving input from storage.
Using shell: /cvmfs/software.eessi.io/versions/2023.06/compat/linux/x86_64/bin/bash
Provided remote nodes: 1
Job stats:
job                count
---------------  -------
hostname_remote        1
total                  1

Select jobs to execute...
Execute 1 jobs...

[Mon Jan 29 18:03:46 2024]
rule hostname_remote:
    output: hostname_remote.txt
    jobid: 0
    reason: Missing output files: hostname_remote.txt
    resources: tmpdir=<TBD>

hostname > hostname_remote.txt
No SLURM account given, trying to guess.
Guessed SLURM account: def-users
No wall time information given. This might or might not work on your cluster. If not, specify the resource runtime in your rule or as a reasonable default via --default-resources.
No job memory information ('mem_mb' or 'mem_mb_per_cpu') is given - submitting without. This might or might not work on your cluster.
Job 0 has been submitted with SLURM jobid 326 (log: /home/ocaisa/.snakemake/slurm_logs/rule_hostname_remote/326.log).
[Mon Jan 29 18:04:26 2024]
Finished job 0.
1 of 1 steps (100%) done
Complete log: .snakemake/log/2024-01-29T180346.788174.snakemake.log

Note all the warnings that Snakemake is giving us about the fact that the rule may not be able to execute on our cluster as we may not have given enough information. Luckily for us, this actually works on our cluster and we can take a look in the output file the new rule creates, hostname_remote.txt:

BASH

[ocaisa@node1 ~]$ cat hostname_remote.txt

OUTPUT

tmpnode1.int.jetstream2.hpc-carpentry.org

Snakemake profile

Adapting Snakemake to a particular environment can entail many flags and options. Therefore, it is possible to specify a configuration profile to be used to obtain default options. This looks like

BASH

snakemake --profile myprofileFolder ...

The profile folder must contain a file called config.yaml which is what will store our options. The folder may also contain other files necessary for the profile. Let’s create the file cluster_profile/config.yaml and insert some of our existing options:

YAML

printshellcmds: True
jobs: 3
executor: slurm

We should now be able rerun our workflow by pointing to the profile rather than the listing out the options. To force our workflow to rerun, we first need to remove the output file hostname_remote.txt, and then we can try out our new profile

BASH

[ocaisa@node1 ~]$ rm hostname_remote.txt 
[ocaisa@node1 ~]$ snakemake --profile cluster_profile hostname_remote

The profile is extremely useful in the context of our cluster, as the Slurm executor has lots of options, and sometimes you need to use them to be able to submit jobs to the cluster you have access to. Unfortunately, the names of the options in Snakemake are not exactly the same as those of Slurm, so we need the help of a translation table:

SLURM	Snakemake	Description
`--partition`	`slurm_partition`	the partition a rule/job is to use
`--time`	`runtime`	the walltime per job in minutes
`--constraint`	`constraint`	may hold features on some clusters
`--mem`	`mem, mem_mb`	memory a cluster node must
		provide (mem: string with unit), mem_mb: int
`--mem-per-cpu`	`mem_mb_per_cpu`	memory per reserved CPU
`--ntasks`	`tasks`	number of concurrent tasks / ranks
`--cpus-per-task`	`cpus_per_task`	number of cpus per task (in case of SMP, rather use `threads`)
`--nodes`	`nodes`	number of nodes

The warnings given by Snakemake hinted that we may need to provide these options. One way to do it is to provide them is as part of the Snakemake rule using the keyword resources, e.g.,

PYTHON

rule:
    input: ...
    output: ...
    resources:
        partition: <partition name>
        runtime: <some number>

and we can also use the profile to define default values for these options to use with our project, using the keyword default-resources. For example, the available memory on our cluster is about 4GB per core, so we can add that to our profile:

YAML

printshellcmds: True
jobs: 3
executor: slurm
default-resources:
  - mem_mb_per_cpu=3600

Challenge

We know that our problem runs in a very short time. Change the default length of our jobs to two minutes for Slurm.

Show me the solution

YAML

printshellcmds: True
jobs: 3
executor: slurm
default-resources:
  - mem_mb_per_cpu=3600
  - runtime=2

There are various sbatch options not directly supported by the resource definitions in the table above. You may use the slurm_extra resource to specify any of these additional flags to sbatch:

PYTHON

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

Local rule execution

Our initial rule was to get the hostname of the login node. We always want to run that rule on the login node for that to make sense. If we tell Snakemake to run all rules via the Slurm executor (which is what we are doing via our new profile) this won’t happen any more. So how do we force the rule to run on the login node?

Well, in the case where a Snakemake rule performs a trivial task job submission might be overkill (e.g., less than 1 minute worth of compute time). Similar to our case, it would be a better idea to have these rules execute locally (i.e. where the snakemake command is run) instead of as a job. Snakemake lets you indicate which rules should always run locally with the localrules keyword. Let’s define hostname_login as a local rule near the top of our Snakefile.

PYTHON

localrules: hostname_login

Key Points

“Snakemake generates and submits its own batch scripts for your scheduler.”
“You can store default configuration settings in a Snakemake profile”
“localrules defines rules that are executed locally, and never submitted to a cluster.”