47372443ff
Upstream: https://github.com/pollen-robotics/microduck_rl Upstream-Commit: d424a0c899f6b33cbd3daeb279913134349c0b63 Upstream-Branch: develop
101 lines
4.5 KiB
TOML
101 lines
4.5 KiB
TOML
[build-system]
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requires = ["uv_build>=0.8.19,<0.9.0"]
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build-backend = "uv_build"
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[project]
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name = "mjlab-microduck"
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version = "0.1.0"
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description = "RL training environments for the Microduck robot, built on mjlab"
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readme = "README.md"
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license = "Apache-2.0"
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license-files = ["LICENSE"]
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# <3.13: bam (better-actuator-models) pins requires-python <3.13, and jobs must
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# run the same interpreter we test locally (3.12) — a floating upper bound let
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# HF jobs pick 3.13.14 (2026-07-21).
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requires-python = ">=3.12, <3.13"
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dependencies = [
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"mjlab==1.3.0",
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"warp-lang==1.12.0",
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# BAM distribution name is `better-actuator-models`; the import stays `bam`.
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"better-actuator-models",
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"onnxruntime>=1.24.4",
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"rustypot>=1.4.2",
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"huggingface_hub>=0.27.0",
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"matplotlib>=3.10.9",
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# mjlab 1.3.0 imports scipy (terrains/heightfield_terrains.py) but forgets
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# to declare it — without this line a fresh `uv sync` (e.g. on HF jobs)
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# can't even `import mjlab_microduck` (found 2026-07-21).
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"scipy>=1.16",
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# Direct dep ONLY so [tool.uv.sources] can bind torch to the CUDA index on
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# aarch64 (see the torch source below) — uv applies sources to DIRECT
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# dependencies only, so as a purely transitive dep (mjlab, rsl_rl) the
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# source entry is silently ignored.
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# Pinned to the exact version uv.lock already resolved from PyPI so this
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# changes only the SOURCE of the wheel on aarch64, not the version: a
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# floating `>=` lets the CUDA index (which carries newer builds than the
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# PyPI pin) drag torch 2.9.1 -> 2.13.0, an unvetted bump for mjlab/rsl_rl.
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"torch==2.9.1",
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]
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[project.entry-points."mjlab.tasks"]
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mjlab_microduck = "mjlab_microduck.tasks"
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[project.scripts]
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# Shadows mjlab's `train` entry point: identical behavior, plus a --hf-jobs
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# flag that submits the run to Hugging Face Jobs instead (see train_cli.py).
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train = "mjlab_microduck.train_cli:main"
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[tool.ruff]
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src = ["src"] # Helpful for recognizing first-party imports.
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indent-width = 4
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exclude = [
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"src/mjlab/third_party",
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"typings",
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]
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[tool.uv]
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override-dependencies = [
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# NOTE: do NOT override mujoco — mjlab 1.3.0's mujoco-warp pins a compatible
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# mujoco (3.10.x); an override here previously forced mujoco down to 3.4.0,
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# which lacks mjDSBL_MULTICCD that mujoco-warp 3.8.1 imports → import crash.
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# bam pins protobuf<4.0 (for its zmq/dynamixel messaging, which we don't
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# use — we only import bam.model/bam.actuator). Without this override the
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# downgrade cascades onnx down to 1.17.0, which has no wheel and fails to
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# build from source.
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"protobuf>=4.0,<7.0",
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# Keep onnx on a version with prebuilt wheels (1.17.0 has no py3.13 wheel and
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# fails to build from source). Pulling in bam otherwise nudges it downward.
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"onnx>=1.20.1",
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# bam depends on the PyPI stub `zmq==0.0.0`, whose prebuilt wheel is invalid
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# (missing .dist-info) -> `uv sync` fails on a FRESH install (e.g. HF Jobs
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# remote build), even though a warm local cache tolerated it. We only use
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# bam.mjlab (no zmq/erob messaging), so drop zmq via an always-false marker.
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"zmq ; python_version < '3.0'",
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]
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[tool.uv.sources]
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# mjlab now comes from PyPI (==1.3.0, pinned in [project.dependencies]); no git source.
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# Official BAM actuator model. Switch to `main` (or a tag) once the
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# mjlab-facing API lands there.
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better-actuator-models = { git = "https://github.com/Rhoban/bam.git", branch = "mjlab_frictionloss" }
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# For local BAM development, comment the line above and use:
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# better-actuator-models = { path = "/home/antoine/Rhoban/bam", editable = true }
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# On linux-aarch64 (DGX Spark / GB10) PyPI's torch wheel is CPU-ONLY:
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# torch.__version__ == "2.9.1+cpu", torch.version.cuda is None, so
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# torch.cuda.device_count() == 0 and mjlab's select_gpus() indexes an empty
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# list -> `IndexError: list index out of range` before training even starts
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# (mjlab/utils/gpu.py:70). Route torch to PyTorch's CUDA index there.
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# cu129 (not cu130) matches the CUDA toolkit warp 1.12.0 bundles, so the
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# zero-copy warp<->torch interop stays on one runtime major version.
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# The marker keeps x86_64 (HF Jobs) on PyPI, where the wheel already bundles
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# CUDA via its nvidia-*-cu12 deps — that resolution is unchanged.
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torch = [
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{ index = "pytorch-cu129", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" },
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]
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[[tool.uv.index]]
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name = "pytorch-cu129"
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url = "https://download.pytorch.org/whl/cu129"
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# explicit: only packages that name this index resolve from it.
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explicit = true
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