Features and benefits
Modular approach to job handling
Looper completely divides job handling from pipeline processing. This modular approach simplifies the pipeline-building process because pipelines no longer need to worry about sample metadata parsing.
The power of standard PEP format
Looper inherits a bunch of advantages from standard PEP format: For example, you only need to learn 1 way to format your project metadata, and it will work with any pipeline. PEP format allows subprojects, which make it easy to define two very similar projects without duplicating project metadata. It also makes your project immediately compatible with other tools in pepkit; for example, you can import all your sample metadata (and pipeline results) in an R or python analysis environment with the pepr R package or the peppy python package. Using PEP's derived attributes feature makes projects portable, and can also be used to collate input files across file systems and naming conventions, making it easy to share projects across compute environments or individuals.
Universal parallelization implementation
Looper's sample-level parallelization applies to all pipelines, so individual pipelines do not need reinvent the wheel. By default
looperwill simply run your jobs serially, but
looper employs divvy to let you process your pipelines on any cluster resource manager (SLURM, SGE, etc.). Looper also allows you to specify compute queue/partition on-the-fly, by passing the
--compute parameter to your call to
looper run, making flexible if you have complex resource needs. This provides a convenient interface for submitting pipelines either to local compute or to any cluster resource manager, so individual pipeline authors do not need to worry about cluster job submission.
Use looper with any pipeline, any library, in any domain. We designed it to work with pypiper, but looper has an infinitely flexible command-line argument system that will let you configure it to work with any script (pipeline) that accepts command-line arguments. You can also configure looper to submit multiple pipelines per sample.
Job completion monitoring
Looper is job-aware and will not submit new jobs for samples that are already running or finished, making it easy to add new samples to existing projects, or re-run failed samples.
Looper has an easy-to-use resource requesting scheme. With a few lines to define CPU, memory, clock time, or anything else, pipeline authors can specify different computational resources depending on the size of the input sample and pipeline to run. Or, just use a default if you don't want to mess with setup.
Command line interface
Looper uses a command-line interface so you have total power at your fingertips.
Beautiful linked result reports
Looper automatically creates an internally linked, portable HTML report highlighting all results for your pipeline, for every pipeline.