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wuji-mjlab installs through pixi, which resolves the full CUDA and PyTorch stack from a lockfile.
This project is pixi-only. conda + pip install -e . is not tested and not supported.

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Requirements

  • Linux x86_64
  • NVIDIA GPU with CUDA 12.8 (Blackwell sm_120 / RTX 50-series supported)
  • pixi ≥ 0.66 (the version CI uses)
For sim-to-real deployment, you also need Wuji Hand hardware and a camera rig. See Hardware Setup for the full bill of materials.

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Install

This produces a default environment for training and evaluation. For the sim-to-real bridge, add the optional deploy environment:

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Verify

This lists the registered tasks and confirms the mjlab and tyro stack imports cleanly.

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Slow Dependency Downloads

If pixi install stalls while pulling the PyTorch wheel, add mirror entries to the existing [pypi-options] block in pixi.toml. Keep index-strategy and the [pypi-options.dependency-overrides] block, and point the indexes at a mirror:
This is a local edit. Don’t commit it — the CI lockfile check fails if pixi.toml and pixi.lock drift.

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Development Setup

To contribute, install the pre-commit hooks after cloning:
Every git commit then runs ruff, codespell, and the YAML, TOML, and large-file checks locally, before CI sees the change. Run the full tree manually with pixi run pre-commit run --all-files.
Don’t pip install packages into the pixi environment. Pip dependencies aren’t tracked by pixi.toml or pixi.lock and disappear on the next resolve. Add dependencies by editing pixi.toml, then run pixi install.