| # CLAUDE.md — Isaac GR00T N1.7 |
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|
| ## Project overview |
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| Isaac GR00T N1.7 is an open vision-language-action (VLA) model for generalized humanoid robot skills. |
| The repo contains the model, training pipeline, evaluation harness, and deployment tooling. |
|
|
| - **Language:** Python 3.12 (dGPU, Thor, DGX Spark); Python 3.10 (Orin — see deployment dir) |
| - **Package manager:** [uv](https://docs.astral.sh/uv/) |
| - **Build system:** setuptools (see `pyproject.toml`) |
|
|
| ## Quick-start commands |
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|
| ```bash |
| # Install (dev mode with all extras) |
| uv sync --all-extras |
| |
| # Lint and format (uses ruff via pre-commit) |
| pre-commit run --all-files |
| |
| # Run CPU tests |
| python -m pytest tests/ -m "not gpu" -v --timeout=300 |
| |
| # Run GPU tests |
| python -m pytest tests/ -m gpu -v --timeout=300 |
| |
| # Build package |
| uv build |
| |
| # Validate lockfile |
| uv lock --locked |
| ``` |
|
|
| ## Code style |
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|
| - Formatter: `ruff format` (double quotes, spaces, line-length 100) |
| - Linter: `ruff check` with rules E, F, I (ignores E501) |
| - Config lives in `pyproject.toml` under `[tool.ruff]` |
| - Run `pre-commit run --all-files` before committing |
|
|
| ## Directory layout |
|
|
| ``` |
| gr00t/ # Main package |
| configs/ # Training, data, and model configs |
| data/ # Data loading, embodiment tags, dataset processing |
| eval/ # Evaluation (run_gr00t_server.py) |
| experiment/ # Training pipeline (launch_finetune.py, trainer.py) |
| model/ # Model architecture (N1.7, base, modules) |
| policy/ # Policy inference (Gr00tPolicy, server/client) |
| examples/ # Per-embodiment example configs and READMEs |
| scripts/ # Deployment, conversion, and utility scripts |
| deployment/ # Platform install scripts (dgpu, orin, thor, spark) |
| tests/ # pytest suite (markers: gpu, not gpu) |
| getting_started/ # User-facing guides and notebooks |
| ``` |
|
|
| ## Key entry points |
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| - **Fine-tune:** `bash examples/finetune.sh --base-model-path <path> --dataset-path <path> --embodiment-tag <tag> --output-dir <dir>` |
| - **Inference server:** `python gr00t/eval/run_gr00t_server.py --model-path <path> --embodiment-tag <tag>` |
| - **ONNX export:** `python scripts/deployment/export_onnx_n1d7.py` |
| - **TensorRT build:** `python scripts/deployment/build_trt_pipeline.py` |
| - **Benchmark:** `python scripts/deployment/benchmark_inference.py` |
|
|
| ## Testing |
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| - Test markers: `gpu` (requires GPU), default is CPU-safe |
| - Fixtures live in `tests/fixtures/` and `demo_data/` |
| - CI runs CPU and GPU tests in separate jobs with 300s timeout |
|
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| ## Deployment platforms |
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| - **dGPU (H100, A100, RTX):** CUDA 12.8 — install via `scripts/deployment/dgpu/install_deps.sh`, container via top-level `docker/Dockerfile` (supports x86_64 and aarch64) |
| - **Jetson Orin:** CUDA 12.6 — install via `scripts/deployment/orin/install_deps.sh`, container via `scripts/deployment/orin/Dockerfile` |
| - **Jetson Thor:** CUDA 13.0 — install via `scripts/deployment/thor/install_deps.sh`, container via `scripts/deployment/thor/Dockerfile` |
| - **DGX Spark:** CUDA 13.0 — install via `scripts/deployment/spark/install_deps.sh`, container via `scripts/deployment/spark/Dockerfile` |
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| Each Jetson/Spark platform ships an `activate_*.sh` helper (`scripts/activate_orin.sh`, `scripts/activate_spark.sh`, `scripts/activate_thor.sh`) that exports platform-specific library paths. For dGPU, the standard `source .venv/bin/activate` is sufficient. |
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