Installation

Python ≥ 3.10. This is a Warp-first library: warp-lang and numpy are the only required runtime dependencies. torch, scipy, matplotlib, and pytest live in the dev extra for tests, benchmarks, and oracle comparisons; gradio lives in the ui extra. The runtime path runs on the Warp cpu device (GPU-free, for tests/CI) and on cuda.

From scratch with uv (recommended)

uv is the recommended way to set up the project.

# 1. install uv (skip if you already have it)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. create the project venv — uv fetches Python 3.12 if it isn't present
uv venv --python 3.12

# 3. install track_gen (editable) with the dev extras (warp-lang comes in as a core dep)
uv pip install -e ".[dev]"

# 4. verify the fast lane
.venv/bin/python -m pytest -q -m "not slow and not benchmark and not cuda"

# Optional: add the Gradio UI and open the parameter explorer
uv pip install -e ".[ui]"
uv run python -m viz.param_explorer   # opens a local URL (default http://127.0.0.1:7860)

Add to an existing uv project

If you already have a uv-managed project (one with a pyproject.toml and a uv.lock), add track_gen as a dependency with uv add from inside that project. It records the dependency in your pyproject.toml, resolves it into uv.lock, and syncs the environment in one step.

# from a Git tag/branch (recommended for reproducibility — pin a ref)
uv add "git+https://github.com/AntoineRichard/TrackGen.git"
uv add "git+https://github.com/AntoineRichard/TrackGen.git@main"

# with an extra (e.g. the Gradio UI)
uv add "track_gen[ui] @ git+https://github.com/AntoineRichard/TrackGen.git"

# or from a local checkout, tracked as an editable path dependency
uv add --editable /path/to/TrackGen

uv add updates pyproject.toml and uv.lock for you. To materialise the environment from the lockfile later (e.g. on a fresh clone or in CI), run uv sync. warp-lang and numpy come in automatically as core dependencies; request the dev or ui extras only if you need them.

With venv + pip

python -m venv .venv
.venv/bin/pip install -e ".[dev]"

Both approaches create a .venv/; run anything in it with .venv/bin/python … (or source .venv/bin/activate, or uv run …).

Extras

Extra	Packages added
dev	pytest, matplotlib, scipy, torch
ui	gradio

CPU and CUDA

Core deps are numpy and warp-lang. The runtime path runs on the Warp cpu device (GPU-free, suitable for tests and CI) and on cuda (production). No CUDA installation is required to install or run the CPU path.