Running on a cluster

Default computing options

When you run your PEPATAC project using looper run, by default it will simply run each sample locally. You can change that using looper run --package COMPUTE_PACKAGE, where COMPUTE_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:

  • --package slurm. Submit the jobs to a SLURM cluster using sbatch.
  • --package sge. Submit the jobs to a SGE cluster using qsub.

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.

Using the manually downloaded assets (from the pepatac/ repository):

looper run examples/test_project/test_config.yaml -d \
  --package slurm

This will produce a job script:

cat pepatac_test/submission/PEPATAC_test1.sub

If all looks well, run looper without -d to actually submit the jobs. Read more to learn how to run PEPATAC in containers.

Using refgenie managed assets (from the pepatac/ repository):

looper run examples/test_project/test_config_refgenie.yaml -d \
  --package slurm

This will produce a job script:

cat pepatac_test/submission/PEPATAC_test1.sub

Customizing compute options

These default computing options may not fit your needs exactly. PEPATAC allows you to very easily change templates or add your own, so you can run PEPATAC in any possible computing environment. PEPATAC 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 $DIVCFG

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

Alternatively, you can specify compute parameters via the CLI:

looper run examples/test_project/test_config_refgenie.yaml -d \
  --package slurm --compute PARTITION="your_cluster_partition_name"