How to configure computing resources

PEPATAC comes packaged with default compute settings (memory, cores, and time) for both the sample and project-level pipeline. These values will automatically be populated by looper based on the input file size. In that way, smaller samples (e.g. fewer reads) will request less resources and vice-versa for large samples. You can also specify these values on the command-line.

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 as 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:

cd pepatac
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.

Configure computing resource requests at the command-line

You can specify the the memory (-M) and number of cores (-P) directly on the command-line.

pipelines/pepatac.py -O /path/to/processed/data/ -S "compute_example" -I /path/to/fastq.fq -G "hg38" -P 16 -M 16000

Configure computing resource requests with looper

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.

Default computing resource requests for PEPATAC are defined in the resources-sample.tsv and resources-project.tsv for sample and project-level pipeline calls, respectively. Looper checks these files based on the size_dependent_variables section in the sample and project pipeline_interface.yaml files. For default pipeline settings, these resources should be more than sufficient, but for different pipeline settings you may desire to request different resources. This could be accomplished two ways:
1. You can override universal compute settings when you call looper by specifying the resources using the --compute variable:

looper run <project_config.yaml> --compute mem=24000 time=00-12:00:00 --cpus-per-task=36 --ntasks=1
  1. You could modify the resources-sample.tsv or resources-project.tsv manually and looper will use these updated values. resources-sample.tsv default: | max_file_size | cores | mem | time | |---------------|-------|-------|-------------| | 0.05 | 4 | 10000 | 00-03:00:00 | | 0.5 | 8 | 12000 | 00-08:00:00 | | 1 | 16 | 16000 | 00-12:00:00 | | 10 | 32 | 24000 | 01-00:00:00 | | NaN | 32 | 32000 | 02-00:00:00 |

resources-project.tsv default: | max_file_size | cores | mem | time | |---------------|-------|-------|-------------| | 0.05 | 1 | 16000 | 00-01:00:00 | | 0.5 | 1 | 32000 | 00-01:00:00 | | 1 | 1 | 56000 | 00-01:00:00 | | 10 | 1 | 64000 | 00-01:00:00 | | NaN | 1 | 64000 | 00-02:00:00 |