Workflow Description Language local runner & developer toolkit for Python 3.8+
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
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
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.
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
- Open an issue
- OpenWDL Slack (#miniwdl channel)
- Bioinformatics Stack Exchange
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.
Please disclose security issues responsibly by contacting security@chanzuckerberg.com.