Running on a cluster

Default computing options

When you run your PEPPRO project using looper run, by default it will simply run each sample locally. You can change that using looper run --compute PACKAGE, where PACKAGE is an option described below. This enables you to adjust your computing preferences on-the-fly. You have several built-in packages, which you can view by typing divvy list. Default packages include:

  • --compute slurm. Submit the jobs to a SLURM cluster using sbatch.
  • --compute sge. Submit the jobs to a SGE cluster using qsub.
  • --compute docker. Submit the jobs locally using the databio/peppro docker image.
  • --compute singularity. Submit the jobs locally using the singularity image.
  • --compute singularity_slurm. Submit jobs using sbatch, but run them using the singularity image.

To show how this works, let's run the example project using the slurm compute package. Used -d for a dry run to create the submits scripts but not run them:

cd peppro
looper run examples/meta/peppro_test.yaml -d \
  --compute slurm

This will produce a job script:

cat peppro_test/submission/peppro_test.sub

If all looks well, run looper without -d to actually submit the jobs. To use the docker or singularity options, see running PEPPRO in containers.

Customizing compute options

These default computing options may not fit your needs exactly. PEPPRO allows you to very easily change templates or add your own, so you can run PEPPRO in any possible computing environment. PEPPRO uses a standardized computing configuration called divvy. The instructions for changing these computing configuration options are universal for any software that relies on divvy.

To customize your compute packages, you first create a divvy computing configuration file and point an environment variable (DIVCFG) to that file:

export DIVCFG="divvy_config.yaml"
divvy init -c $DIVCFG

Next, you edit that config file to add in any compute packages you need. PEPPRO will then give you access to any of your custom packages with looper --compute <package>. For complete instructions on how to create a custom compute package, read how to configure divvy.