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
--package sge. Submit the jobs to a SGE cluster using
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:
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
PEPATAC uses a standardized computing configuration called divvy. The instructions for changing these computing configuration options are universal for any software that relies on
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
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
looper run <project_config.yaml> --compute mem=24000 time=00-12:00:00 --cpus-per-task=36 --ntasks=1
- You could modify the
looperwill use these updated values.
resources-sample.tsvdefault: | 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 |
| 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 |