The graph of jobs that Snakemake determines before execution can be partitioned into groups. Such groups will be executed together in cluster or cloud mode, as a so-called group job, i.e., all jobs of a particular group will be submitted at once, to the same computing node. When executing locally, group definitions are ignored.
Groups can be defined along with the workflow definition via the
group keyword, see Defining groups for execution.
This way, queueing and execution time can be saved, in particular by attaching short-running downstream jobs to long running upstream jobs.
Snakemake will request resources for groups by summing across jobs that can be run in parallel, and taking the max of jobs run in series.
The only exception is
runtime, where the max will be taken over parallel jobs, and the sum over series.
If resource contraints are provided (via
--cores), parallel job layers that exceed the constraints will be stacked in series.
For example, if 6 instances of
somerule are being run, each instance requires
1000MB of memory and
30 min runtime, and only
3000MB are available, Snakemake will request
60 min runtime, enough to run 3 instances of
somerule, then another 3 instances of
somerule in series.
Often, the ideal group will be dependent on the specifics of the underlying computing platform. Hence, it is possible to assign groups via the command line. For example, with
snakemake --groups somerule=group0 someotherrule=group0
we assign the two rules
someotherrule to the same group
By default, groups do not span disconnected parts of the DAG.
This means that, for example, jobs of
someotherrule only end in the same group if they are directly connected.
It is, however, possible to configure the number of connected DAG components that are spanned by a group via the flag
This makes it possible to define batches of jobs of the same kind that shall be executed within one group. For instance:
snakemake --groups somerule=group0 --group-components group0=5
means that given
n jobs spawned from rule
somerule, Snakemake will create
n / 5 groups which each execute 5 jobs of
For example, with 10 jobs from
somerule you would end up with 2 groups of 5 jobs that are submitted as one piece each.