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Hello World! example for looper

Looper is a pipeline submission engine (see source code and documentation). This repository demonstrates how to install looper and use it to run the included pipeline on the included PEP project. This repository contains:

  1. a functional basic example project (in /project), and
  2. a looper-compatible pipeline (in /pipeline) that can run on that project.

Run the example

  1. Install the latest version of looper (this pipeline requires looper version >= 1.1.0):
pip install --user --upgrade looper
  1. Download and unzip this repository
  1. Run it:
cd hello_looper
looper run project/project_config.yaml

How it works

You should see output that looks like this. Here's what you've just accomplished:

This repository has 3 components (corresponding to the 3 subfolders):

  • /project -- contains 2 files that describe metadata for the project (project_config.yaml) and the samples (sample_annotation.csv). This particular project describes just two samples listed in the annotation file. These files together make up a PEP-formatted project, and can therefore be read by any PEP-compatible tool, including looper.
  • /data -- contains 2 data files for 2 samples. These input files were each passed to the pipeline.
  • /pipeline -- contains the script we want to run on each sample in our project. Our pipeline is a very simple shell script named, which (duh!) counts the number of lines in an input file.

When we invoke looper from the command line we told it to run project/project_config.yaml. looper reads the project/project_config.yaml file, which points to a few things:

  • the project/sample_annotation.csv file, which specifies a few samples, their type, and path to data file
  • the output_dir, which is where looper results are saved. Results will be saved in $HOME/hello_looper_results.
  • the pipeline_interface.yaml file, (pipeline/pipeline_interface.yaml), which tells looper how to connect to the pipeline (which is also in pipeline/).

The 3 folders (data, project, and pipeline) are modular; there is no need for these to live in any predetermined folder structure. For this example, the data and pipeline are included locally, but in practice, they are usually in a separate folder; you can point to anything (so data, pipelines, and projects may reside in distinct spaces on disk). You may also include more than one pipeline interface in your project_config.yaml, so in a looper project, many-to-many relationships are possible.

A few more basic looper options

Looper also provides a few other simple arguments that let you adjust what it does. You can find a complete reference of usage in the docs. Here are a few of the more common options:

For looper run:

  • -d: Dry run mode (creates submission scripts, but does not execute them)
  • --limit: Only run a few samples

There are also other commands:

  • looper check: checks on the status (running, failed, completed) of your jobs
  • looper summarize: produces an output file that summarizes your project results
  • looper destroy: completely erases all results so you can restart

More information


A Hello World! example repository for looper






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