miniwdl

Workflow Description Language local runner & developer toolkit for Python 3.8+

Install miniwdl

Installation requires Python 3.8+, pip (or conda) and Docker (or Podman/Singularity/udocker). Linux preferred; macOS compatible with extra steps. More detail in full documentation.

• Install with pip : run pip3 install miniwdl
• Install with conda : setup conda-forge and run conda install miniwdl
• Verify your miniwdl installation: miniwdl run_self_test
• Install from source code for development: see CONTRIBUTING.md

Use miniwdl

Run an example bioinformatics WDL pipeline using miniwdl, or learn more abut miniwdl via a short course (screencast examples). If you are new to the WDL language, see the open source learn-wdl course.

• Run an example using a viral genome assembly workflow
• Learn miniwdl course w/screencasts - shown below
• Learn WDL course w/screencasts

Documentation

The online documentation includes a user tutorial, reference manual, and Python development codelabs:

See the Releases for change logs. The Project board shows the current prioritization of issues.

Scaling up

The miniwdl runner schedules WDL tasks in parallel up to the CPUs & memory available on the local host; so a more-powerful host enables larger workloads. Separately-maintained projects can distribute tasks to cloud & HPC backends with a shared filesystem:

• AWS:
  • miniwdl-omics-run tool for the Amazon Omics workflow service
  • AWS Batch plugin (DIY)
• SLURM

Getting Help

• Open an issue
• OpenWDL Slack (#miniwdl channel)
• Bioinformatics Stack Exchange

Contributing

Feedback and contributions to miniwdl are welcome, via issues and pull requests on this repository. See CONTRIBUTING.md for guidelines, instructions to set up your development environment, and a codebase overview.

Security

Please disclose security issues responsibly by contacting security@chanzuckerberg.com.