Getting Started

Environment

A lightweight local verification environment can be created with:

make bootstrap-harness
source .venv/bin/activate

For full local usage of the CLI and training stack, install the project and its dependencies:

uv pip install --python .venv/bin/python -e '.[dev]'

Verification

Run the standard local verification loop:

make verify

This currently performs:

• Python syntax checks via compileall
• the lightweight pytest suite

Installation

Editable install:

git clone https://github.com/deepflame-ai/DFODE-kit.git
cd DFODE-kit
uv venv .venv
uv pip install --python .venv/bin/python -e '.[dev]'

CLI entrypoint

If the console script is installed, use:

dfode-kit --help

A reliable fallback inside the repository is:

.venv/bin/python -m dfode_kit.cli.main --help

Runtime environment split

Different stages of the workflow may require different dependencies:

• lightweight repository verification: local .venv
• canonical case initialization: Python environment with cantera
• case execution: configured OpenFOAM + Conda + DeepFlame runtime via dfode-kit config and dfode-kit run-case
• sampling / labeling: Python environment with cantera, numpy, and h5py

If you are starting with the case workflow, continue to:

1. CLI
2. Canonical Case Initialization
3. Runtime Configuration and Case Execution
4. Data Preparation and Training Workflow

Current focus

The project is being refactored toward:

• agent-friendly CLI behavior,
• dataset contracts,
• training/model registries,
• reproducible documentation and CI.