# wf2wf – Universal Workflow-Format Converter
[](https://www.python.org/downloads/)
[](https://github.com/csmcal/wf2wf/actions/workflows/ci.yml)
[](https://csmcal.github.io/wf2wf/)
[](https://pypi.org/project/wf2wf)
[](https://codecov.io/gh/csmcal/wf2wf)
[](LICENSE)
[](wf2wf/schemas/v0.1/wf.json)
[](wf2wf/schemas/v0.1/loss.json)
`wf2wf` is a **format-agnostic converter**: any supported engine → **Intermediate Representation (IR)** → any other engine. The core library handles:
• [Snakemake](https://snakemake.readthedocs.io/) • [Nextflow](https://www.nextflow.io/) • [CWL](https://www.commonwl.org/) • [HTCondor](https://htcondor.org/)/[DAGMan](https://htcondor.readthedocs.io/en/latest/automated-workflows/dagman-introduction.html) • [WDL](https://openwdl.org/) • [Galaxy](https://galaxyproject.org/) • bundled [BioCompute Objects](https://www.biocomputeobject.org/)
```mermaid
graph TD;
A[Snakemake] -->|import| IR((IR));
B[DAGMan] --> IR;
C[CWL] --> IR;
D[Nextflow] --> IR;
E[WDL] --> IR;
F[Galaxy] --> IR;
IR -->|export| GA2[Galaxy];
IR --> SMK2[Snakemake];
IR --> CWL2[CWL];
IR --> DAG2[DAGMan];
IR --> NF2[Nextflow];
IR --> WDL2[WDL];
```
`wf2wf` is called from the command line as:
```bash
# Convert Snakemake → DAGMan and auto-generate Markdown report
wf2wf convert -i pipeline.smk -o pipeline.dag --auto-env build --interactive --report-md
```
---
## 📋 Table of Contents
- [Features](#-features)
- [Installation](#-installation)
- [Quick CLI Tour](#-quick-cli-tour)
- [Commands](#-commands-overview)
- [Examples](#-examples)
- [Contributing](#-contributing)
- [Support](#-support)
- [License](#-license)
- [Acknowledgements](#-acknowledgements)
---
## ✨ Features
- **🔄 Universal Conversion** – Any supported engine → IR → any other engine with a single command.
- **🧬 Loss-Mapping & Round-Trip Fidelity** – Structured loss reports (`*.loss.json`) and automatic reinjection guarantee nothing disappears silently.
- **🐳 Automated Environment Builds** – Optional Conda-to-OCI pipeline (micromamba → conda-pack → buildx/buildah) with digest-pinned image references, SBOMs and Apptainer conversion.
- **⚖ Regulatory & Provenance Support** – BioCompute Object generation, schema validation and side-car provenance for FDA submissions.
- **🧪 Aiming for Quality** – high test coverage, semantic versioning, graceful degradation when optional external tools are missing.
- **🔧 Smart Configuration Analysis** – Automatic detection and warnings for missing resource requirements, containers, error handling, and file transfer modes when converting between shared filesystem and distributed computing workflows.
- **💬 Interactive Mode** – Guided prompts to help users address configuration gaps and optimize workflows for target execution environments.
### Information-loss workflow
`wf2wf` should record every field the target engine cannot express:
```console
⚠ Conversion losses: 2 (lost), 1 (lost again), 7 (reapplied)
```
* `lost` – field dropped in this conversion
* `lost again` – it was already lost by a previous exporter
* `reapplied` – successfully restored from a side-car when converting back to a richer format
Use `--fail-on-loss` to abort if any *lost/lost again* entries remain.
### Configuration Analysis
When converting between different workflow execution environments, `wf2wf` automatically detects potential issues:
```console
## Configuration Analysis
### Potential Issues for Distributed Computing
* **Memory**: 2 tasks without explicit memory requirements
* **Containers**: 3 tasks without container/conda specifications
* **Error Handling**: 3 tasks without retry specifications
* **File Transfer**: 6 files with auto-detected transfer modes
**Recommendations:**
* Add explicit resource requirements for all tasks
* Specify container images or conda environments for environment isolation
* Configure retry policies for fault tolerance
* Review file transfer modes for distributed execution
```
Use `--interactive` to get guided prompts for addressing these issues automatically.
---
## 📦 Installation
```bash
# PyPI (recommended)
pip install wf2wf
# or conda-forge (once feedstock is merged)
conda install -c conda-forge wf2wf
```
Development install:
```bash
git clone https://github.com/csmcal/wf2wf.git && cd wf2wf
pip install -e .[dev]
pre-commit install
pytest -q
```
---
## 🚀 Quick CLI Tour
```bash
# Convert Snakemake → DAGMan and build digest-pinned images
wf2wf convert -i Snakefile -o pipeline.dag --auto-env build --push-registry ghcr.io/myorg --report-md --interactive
# Convert CWL → Nextflow, abort on any information loss
wf2wf convert -i analysis.cwl -o main.nf --out-format nextflow --fail-on-loss
# Validate a workflow and its loss side-car
wf2wf validate pipeline.dag
```
Interactive prompts (`--interactive`) use `y/n/always/quit`; loss prompts appear only for *warn/error* severities.
---
## 🛠 Commands Overview
| Command | Purpose |
|---------|---------|
| `wf2wf convert` | Convert workflows between formats (all conversions go via the IR) |
| `wf2wf validate` | Validate a workflow file or a `.loss.json` side-car |
| `wf2wf info` | Pretty-print summary statistics of a workflow |
| `wf2wf bco sign` | Sign a BioCompute Object and generate provenance attestation |
| `wf2wf bco package` | Bundle a BCO and its artefacts (e.g. eSTAR ZIP) |
Each command accepts `--help` for full usage details.
### Auto-detection matrix
| Extension | Format |
|-----------|--------|
| `.cwl` | CWL |
| `.dag` | DAGMan |
| `.ga` | Galaxy |
| `.json` | IR (JSON) |
| `.nf` | Nextflow |
| `.smk` | Snakemake |
| `.wdl` | WDL |
| `.yaml`, `.yml` | IR (YAML) |
---
## 🔬 Examples
### Example 1 – Snakemake → DAGMan with automatic environment build
```bash
wf2wf convert -i Snakefile \
-o pipeline.dag \
--out-format dagman \
--auto-env build --push-registry ghcr.io/myorg
```
### Example 2 – CWL → Nextflow round-trip
```bash
# CWL → IR → Nextflow
airflow_cwl="workflow.cwl"
wf2wf convert -i "$airflow_cwl" -o main.nf --out-format nextflow
# … do some edits …
# Nextflow → IR → CWL (should restore metadata)
wf2wf convert -i main.nf -o roundtrip.cwl --out-format cwl
```
### Example 3 – WDL → CWL with loss checking
```bash
wf2wf convert -i assembly.wdl -o assembly.cwl --out-format cwl --fail-on-loss
```
More recipes live in the `examples/` directory.
---
## 🔄 Workflow Conversion Differences
When converting between different workflow execution environments, several key differences need to be addressed:
### Shared Filesystem vs Distributed Computing
**Shared Filesystem Workflows** (Snakemake, CWL, Nextflow):
- Assume all files are accessible on a shared filesystem
- Often have minimal resource specifications
- Rely on system-wide software or conda environments
- Basic error handling and retry mechanisms
**Distributed Computing Workflows** (HTCondor/DAGMan):
- Require explicit file transfer specifications
- Need explicit resource allocation (CPU, memory, disk)
- Require container specifications for environment isolation
- Benefit from sophisticated retry policies and error handling
### Key Conversion Challenges
| Challenge | Shared → Distributed | Distributed → Shared |
|-----------|---------------------|---------------------|
| **File Transfer** | Add `transfer_input_files`/`transfer_output_files` | Remove transfer specifications |
| **Resources** | Add `request_cpus`, `request_memory`, `request_disk` | Convert to engine-specific resource formats |
| **Containers** | Specify Docker/Singularity images | Map to conda environments or system packages |
| **Error Handling** | Add retry policies and error strategies | Convert to engine-specific error handling |
| **Scatter/Gather** | Expand to explicit job definitions | Map to wildcards or engine-specific parallelization |
### Interactive Configuration Assistance
Use `--interactive` mode to get guided assistance:
```bash
# Interactive conversion with configuration prompts
wf2wf convert -i Snakefile -o workflow.dag --interactive
# Example prompts you'll see:
# Found 3 tasks without explicit resource requirements.
# Distributed systems require explicit resource allocation.
# Add default resource specifications? (y)es/(n)o/(a)lways/(q)uit: y
```
### Automatic Configuration Analysis
The conversion report includes detailed analysis:
```markdown
## Configuration Analysis
### Potential Issues for Distributed Computing
* **Memory**: 2 tasks without explicit memory requirements
* **Containers**: 3 tasks without container/conda specifications
* **Error Handling**: 3 tasks without retry specifications
* **File Transfer**: 6 files with auto-detected transfer modes
**Recommendations:**
* Add explicit resource requirements for all tasks
* Specify container images or conda environments for environment isolation
* Configure retry policies for fault tolerance
* Review file transfer modes for distributed execution
```
See [File Transfer Handling](docs/user_guide/file_transfers.md) for detailed information about file transfer modes and best practices.
---
## 🤝 Contributing
1. Fork the repository and create a feature branch (`git checkout -b feature/amazing-feature`)
2. Add tests for new functionality
3. Ensure all tests pass (`pytest -q`)
4. Open a Pull Request – GitHub Actions will run the test matrix automatically
Please read `CONTRIBUTING.md` for the full guidelines.
### 🧪 Testing
Run the comprehensive test suite:
```bash
# Run all tests
python -m pytest tests/ -v
# Run specific test categories
python -m pytest tests/test_conversions.py::TestConversions::test_linear_workflow_conversion -v
# Run with coverage
python -m pytest tests/ --cov=wf2wf --cov-report=html
```
---
## 📞 Support
- 📖 **Documentation** – The `docs/` folder contains a growing knowledge-base, rendered on ReadTheDocs.
- 🐛 **Issues** – Found a bug or missing feature? [Open an issue](../../issues).
- 💬 **Discussions** – General questions and ideas live in [GitHub Discussions](../../discussions).
---
## 📄 License
`wf2wf` is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.
---
## 🙏 Acknowledgments
- [CHTC](https://chtc.cs.wisc.edu/) - The Center for High Throughput Computing for testing and feedback
- The [OpenAI](https://openai.com/) and [Anthropic](https://www.anthropic.com/)-powered coding assistants whose suggestions accelerated feature implementation
- [Cursor](https://cursor.sh/) - Interactive IDE used for pair-programming and AI-assisted development
---
*Bridging workflow ecosystems.*
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"description": "# wf2wf \u2013 Universal Workflow-Format Converter\n\n[](https://www.python.org/downloads/)\n[](https://github.com/csmcal/wf2wf/actions/workflows/ci.yml)\n[](https://csmcal.github.io/wf2wf/)\n[](https://pypi.org/project/wf2wf)\n[](https://codecov.io/gh/csmcal/wf2wf)\n[](LICENSE)\n[](wf2wf/schemas/v0.1/wf.json)\n[](wf2wf/schemas/v0.1/loss.json)\n\n`wf2wf` is a **format-agnostic converter**: any supported engine \u2192 **Intermediate Representation (IR)** \u2192 any other engine. The core library handles:\u2003\n\n\u2022 [Snakemake](https://snakemake.readthedocs.io/)\u2003\u2022 [Nextflow](https://www.nextflow.io/)\u2003\u2022 [CWL](https://www.commonwl.org/)\u2003\u2022 [HTCondor](https://htcondor.org/)/[DAGMan](https://htcondor.readthedocs.io/en/latest/automated-workflows/dagman-introduction.html)\u2003\u2022 [WDL](https://openwdl.org/)\u2003\u2022 [Galaxy](https://galaxyproject.org/)\u2003\u2022 bundled [BioCompute Objects](https://www.biocomputeobject.org/)\n\n```mermaid\ngraph TD;\n A[Snakemake] -->|import| IR((IR));\n B[DAGMan] --> IR;\n C[CWL] --> IR;\n D[Nextflow] --> IR;\n E[WDL] --> IR;\n F[Galaxy] --> IR;\n IR -->|export| GA2[Galaxy];\n IR --> SMK2[Snakemake];\n IR --> CWL2[CWL];\n IR --> DAG2[DAGMan];\n IR --> NF2[Nextflow];\n IR --> WDL2[WDL];\n```\n\n`wf2wf` is called from the command line as:\n```bash\n# Convert Snakemake \u2192 DAGMan and auto-generate Markdown report\nwf2wf convert -i pipeline.smk -o pipeline.dag --auto-env build --interactive --report-md\n```\n\n---\n## \ud83d\udccb Table of Contents\n\n- [Features](#-features)\n- [Installation](#-installation)\n- [Quick CLI Tour](#-quick-cli-tour)\n- [Commands](#-commands-overview)\n- [Examples](#-examples)\n- [Contributing](#-contributing)\n- [Support](#-support)\n- [License](#-license)\n- [Acknowledgements](#-acknowledgements)\n\n---\n\n## \u2728 Features\n\n- **\ud83d\udd04 Universal Conversion** \u2013 Any supported engine \u2192 IR \u2192 any other engine with a single command.\n- **\ud83e\uddec Loss-Mapping & Round-Trip Fidelity** \u2013 Structured loss reports (`*.loss.json`) and automatic reinjection guarantee nothing disappears silently.\n- **\ud83d\udc33 Automated Environment Builds** \u2013 Optional Conda-to-OCI pipeline (micromamba \u2192 conda-pack \u2192 buildx/buildah) with digest-pinned image references, SBOMs and Apptainer conversion.\n- **\u2696 Regulatory & Provenance Support** \u2013 BioCompute Object generation, schema validation and side-car provenance for FDA submissions.\n- **\ud83e\uddea Aiming for Quality** \u2013 high test coverage, semantic versioning, graceful degradation when optional external tools are missing.\n- **\ud83d\udd27 Smart Configuration Analysis** \u2013 Automatic detection and warnings for missing resource requirements, containers, error handling, and file transfer modes when converting between shared filesystem and distributed computing workflows.\n- **\ud83d\udcac Interactive Mode** \u2013 Guided prompts to help users address configuration gaps and optimize workflows for target execution environments.\n\n### Information-loss workflow\n\n`wf2wf` should record every field the target engine cannot express:\n\n```console\n\u26a0 Conversion losses: 2 (lost), 1 (lost again), 7 (reapplied)\n```\n\n* `lost` \u2013 field dropped in this conversion\n* `lost again` \u2013 it was already lost by a previous exporter\n* `reapplied` \u2013 successfully restored from a side-car when converting back to a richer format\n\nUse `--fail-on-loss` to abort if any *lost/lost again* entries remain.\n\n### Configuration Analysis\n\nWhen converting between different workflow execution environments, `wf2wf` automatically detects potential issues:\n\n```console\n## Configuration Analysis\n\n### Potential Issues for Distributed Computing\n\n* **Memory**: 2 tasks without explicit memory requirements\n* **Containers**: 3 tasks without container/conda specifications\n* **Error Handling**: 3 tasks without retry specifications\n* **File Transfer**: 6 files with auto-detected transfer modes\n\n**Recommendations:**\n* Add explicit resource requirements for all tasks\n* Specify container images or conda environments for environment isolation\n* Configure retry policies for fault tolerance\n* Review file transfer modes for distributed execution\n```\n\nUse `--interactive` to get guided prompts for addressing these issues automatically.\n\n---\n\n## \ud83d\udce6 Installation\n\n```bash\n# PyPI (recommended)\npip install wf2wf\n\n# or conda-forge (once feedstock is merged)\nconda install -c conda-forge wf2wf\n```\n\nDevelopment install:\n\n```bash\ngit clone https://github.com/csmcal/wf2wf.git && cd wf2wf\npip install -e .[dev]\npre-commit install\npytest -q\n```\n\n---\n\n## \ud83d\ude80 Quick CLI Tour\n\n```bash\n# Convert Snakemake \u2192 DAGMan and build digest-pinned images\nwf2wf convert -i Snakefile -o pipeline.dag --auto-env build --push-registry ghcr.io/myorg --report-md --interactive\n\n# Convert CWL \u2192 Nextflow, abort on any information loss\nwf2wf convert -i analysis.cwl -o main.nf --out-format nextflow --fail-on-loss\n\n# Validate a workflow and its loss side-car\nwf2wf validate pipeline.dag\n```\n\nInteractive prompts (`--interactive`) use `y/n/always/quit`; loss prompts appear only for *warn/error* severities.\n\n---\n\n## \ud83d\udee0 Commands Overview\n\n| Command | Purpose |\n|---------|---------|\n| `wf2wf convert` | Convert workflows between formats (all conversions go via the IR) |\n| `wf2wf validate` | Validate a workflow file or a `.loss.json` side-car |\n| `wf2wf info` | Pretty-print summary statistics of a workflow |\n| `wf2wf bco sign` | Sign a BioCompute Object and generate provenance attestation |\n| `wf2wf bco package` | Bundle a BCO and its artefacts (e.g. eSTAR ZIP) |\n\nEach command accepts `--help` for full usage details.\n\n### Auto-detection matrix\n\n| Extension | Format |\n|-----------|--------|\n| `.cwl` | CWL |\n| `.dag` | DAGMan |\n| `.ga` | Galaxy |\n| `.json` | IR (JSON) |\n| `.nf` | Nextflow |\n| `.smk` | Snakemake |\n| `.wdl` | WDL |\n| `.yaml`, `.yml` | IR (YAML) |\n---\n\n## \ud83d\udd2c Examples\n\n### Example 1 \u2013 Snakemake \u2192 DAGMan with automatic environment build\n\n```bash\nwf2wf convert -i Snakefile \\\n -o pipeline.dag \\\n --out-format dagman \\\n --auto-env build --push-registry ghcr.io/myorg\n```\n\n### Example 2 \u2013 CWL \u2192 Nextflow round-trip\n\n```bash\n# CWL \u2192 IR \u2192 Nextflow\nairflow_cwl=\"workflow.cwl\"\nwf2wf convert -i \"$airflow_cwl\" -o main.nf --out-format nextflow\n\n# \u2026 do some edits \u2026\n# Nextflow \u2192 IR \u2192 CWL (should restore metadata)\nwf2wf convert -i main.nf -o roundtrip.cwl --out-format cwl\n```\n\n### Example 3 \u2013 WDL \u2192 CWL with loss checking\n\n```bash\nwf2wf convert -i assembly.wdl -o assembly.cwl --out-format cwl --fail-on-loss\n```\n\nMore recipes live in the `examples/` directory.\n\n---\n\n## \ud83d\udd04 Workflow Conversion Differences\n\nWhen converting between different workflow execution environments, several key differences need to be addressed:\n\n### Shared Filesystem vs Distributed Computing\n\n**Shared Filesystem Workflows** (Snakemake, CWL, Nextflow):\n- Assume all files are accessible on a shared filesystem\n- Often have minimal resource specifications\n- Rely on system-wide software or conda environments\n- Basic error handling and retry mechanisms\n\n**Distributed Computing Workflows** (HTCondor/DAGMan):\n- Require explicit file transfer specifications\n- Need explicit resource allocation (CPU, memory, disk)\n- Require container specifications for environment isolation\n- Benefit from sophisticated retry policies and error handling\n\n### Key Conversion Challenges\n\n| Challenge | Shared \u2192 Distributed | Distributed \u2192 Shared |\n|-----------|---------------------|---------------------|\n| **File Transfer** | Add `transfer_input_files`/`transfer_output_files` | Remove transfer specifications |\n| **Resources** | Add `request_cpus`, `request_memory`, `request_disk` | Convert to engine-specific resource formats |\n| **Containers** | Specify Docker/Singularity images | Map to conda environments or system packages |\n| **Error Handling** | Add retry policies and error strategies | Convert to engine-specific error handling |\n| **Scatter/Gather** | Expand to explicit job definitions | Map to wildcards or engine-specific parallelization |\n\n### Interactive Configuration Assistance\n\nUse `--interactive` mode to get guided assistance:\n\n```bash\n# Interactive conversion with configuration prompts\nwf2wf convert -i Snakefile -o workflow.dag --interactive\n\n# Example prompts you'll see:\n# Found 3 tasks without explicit resource requirements. \n# Distributed systems require explicit resource allocation. \n# Add default resource specifications? (y)es/(n)o/(a)lways/(q)uit: y\n```\n\n### Automatic Configuration Analysis\n\nThe conversion report includes detailed analysis:\n\n```markdown\n## Configuration Analysis\n\n### Potential Issues for Distributed Computing\n\n* **Memory**: 2 tasks without explicit memory requirements\n* **Containers**: 3 tasks without container/conda specifications \n* **Error Handling**: 3 tasks without retry specifications\n* **File Transfer**: 6 files with auto-detected transfer modes\n\n**Recommendations:**\n* Add explicit resource requirements for all tasks\n* Specify container images or conda environments for environment isolation\n* Configure retry policies for fault tolerance\n* Review file transfer modes for distributed execution\n```\n\nSee [File Transfer Handling](docs/user_guide/file_transfers.md) for detailed information about file transfer modes and best practices.\n\n---\n## \ud83e\udd1d Contributing\n\n1. Fork the repository and create a feature branch (`git checkout -b feature/amazing-feature`)\n2. Add tests for new functionality\n3. Ensure all tests pass (`pytest -q`)\n4. Open a Pull Request \u2013 GitHub Actions will run the test matrix automatically\n\nPlease read `CONTRIBUTING.md` for the full guidelines.\n\n### \ud83e\uddea Testing\n\nRun the comprehensive test suite:\n```bash\n# Run all tests\npython -m pytest tests/ -v\n\n# Run specific test categories\npython -m pytest tests/test_conversions.py::TestConversions::test_linear_workflow_conversion -v\n\n# Run with coverage\npython -m pytest tests/ --cov=wf2wf --cov-report=html\n```\n---\n\n## \ud83d\udcde Support\n\n- \ud83d\udcd6 **Documentation** \u2013 The `docs/` folder contains a growing knowledge-base, rendered on ReadTheDocs.\n- \ud83d\udc1b **Issues** \u2013 Found a bug or missing feature? [Open an issue](../../issues).\n- \ud83d\udcac **Discussions** \u2013 General questions and ideas live in [GitHub Discussions](../../discussions).\n\n---\n\n## \ud83d\udcc4 License\n\n`wf2wf` is licensed under the MIT License \u2013 see the [LICENSE](LICENSE) file for details.\n\n---\n\n## \ud83d\ude4f Acknowledgments\n\n- [CHTC](https://chtc.cs.wisc.edu/) - The Center for High Throughput Computing for testing and feedback\n- The [OpenAI](https://openai.com/) and [Anthropic](https://www.anthropic.com/)-powered coding assistants whose suggestions accelerated feature implementation\n- [Cursor](https://cursor.sh/) - Interactive IDE used for pair-programming and AI-assisted development\n---\n\n*Bridging workflow ecosystems.*\n",
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