diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 4b76f7a..bef944b 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -21,7 +21,7 @@ ## Validation -- [ ] `uv run pytest -q` +- [ ] `python -m pytest -q` - [ ] `prek run --all-files` - [ ] CI checks pass on the target branch - [ ] Documentation updated (if applicable) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f1d6925..7262ed2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -33,7 +33,7 @@ jobs: run: uv python install 3.10 - name: Install workspace and test dependencies - run: uv sync --all-packages --all-groups --all-extras + run: sh install.sh --all - name: Run tests - run: uv run pytest -q --disable-warnings --maxfail=1 + run: .venv/bin/python -m pytest -q --disable-warnings --maxfail=1 diff --git a/AGENTS.md b/AGENTS.md index c9973e3..f01dc25 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -104,20 +104,22 @@ Python 方法，由 manager 运行时契约定义（`wiki/design/manager/runtime 项目使用 UV 管理依赖，Python 版本要求 3.10.\*。可用的依赖组与 extras 见 `pyproject.toml`。 -完整安装（含全部 package、依赖组与 extras）： +完整开发环境（全部 package、依赖组与 extras；`--gpu rocm` 切换 AMD wheel）： ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` -按框架安装： +运行环境（无开发工具链；GPU 厂商自动探测，`--rslrl` 换后端）： ```bash -uv sync --all-packages --extra skrl-jax # SKRL JAX backend -uv sync --all-packages --extra skrl-torch # SKRL PyTorch backend -uv sync --all-packages --extra rslrl # RSLRL（PyTorch） +sh install.sh ``` +避免裸 `uv run` / `uv sync`：`cuda`/`rocm` extras 声明为互斥，裸命令会解析到默认 fork（PyPI torch） +并重装环境。运行命令先 `source .venv/bin/activate`，之后直接 `python` / `pytest`（下文示例即此写法）， +或对单条命令使用 `uv run --no-sync`。依赖变更（`pyproject.toml` / `uv.lock`）后重新执行对应 install 命令。 + ## 常用命令 train/play/view CLI 由 [Hydra](https://hydra.cc/) 驱动，参数使用 `key=value` 语法（不是 `--flag`）。可用选项见 @@ -128,20 +130,20 @@ train/play/view CLI 由 [Hydra](https://hydra.cc/) 驱动，参数使用 `key=va SKRL（默认）： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo ``` RSLRL： ```bash -uv run scripts/train.py task=cartpole/rslrl.ppo +python scripts/train.py task=cartpole/rslrl.ppo ``` 直接覆写框架运行时设置和类型化 RL 参数： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo num_envs=64 algo.agent.learning_rate=1e-3 -uv run scripts/train.py task=cartpole/skrl.ppo logging.interval=20 checkpoint.interval=100 +python scripts/train.py task=cartpole/skrl.ppo num_envs=64 algo.agent.learning_rate=1e-3 +python scripts/train.py task=cartpole/skrl.ppo logging.interval=20 checkpoint.interval=100 ``` ### 环境可视化 @@ -149,31 +151,31 @@ uv run scripts/train.py task=cartpole/skrl.ppo logging.interval=20 checkpoint.in 不训练只查看环境： ```bash -uv run scripts/view.py env=cartpole +python scripts/view.py env=cartpole ``` 查看内置机器人（不创建 RL 环境）： ```bash -uv run scripts/view.py robot=g1-29dof +python scripts/view.py robot=g1-29dof ``` ### 评估 ```bash -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole ``` 指定 policy 文件： ```bash -uv run scripts/play.py env=cartpole policy= +python scripts/play.py env=cartpole policy= ``` ### ONNX 导出 ```bash -uv run scripts/export_onnx.py run_dir= output=/tmp/policy.onnx +python scripts/export_onnx.py run_dir= output=/tmp/policy.onnx ``` ### 渲染 @@ -181,19 +183,19 @@ uv run scripts/export_onnx.py run_dir= output=/tmp/policy.onnx 训练时开启可视化： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo render=true +python scripts/train.py task=cartpole/skrl.ppo render=true ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/{env-name} +tensorboard --logdir runs/{env-name} ``` ### 测试 ```bash -uv run pytest +python -m pytest ``` ## 架构要点 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index bf24d77..9a2ac37 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -26,17 +26,16 @@ git lfs pull Create the complete development environment from the repository root: ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all # --gpu rocm switches to the AMD wheels ``` -Optional training backends can be installed separately when a full environment -is unnecessary: - -```bash -uv sync --all-packages --extra skrl-torch -uv sync --all-packages --extra skrl-jax # Linux only -uv sync --all-packages --extra rslrl -``` +A runtime-only environment without the dev toolchain is available via `sh install.sh`: it +auto-detects the GPU vendor (NVIDIA/AMD) for the torch wheels and `--rslrl` +selects the RSLRL backend. On Windows, run `install.ps1` in PowerShell. Avoid bare +`uv sync` / `uv run`: the `cuda`/`rocm` extras are declared conflicting, so a bare +command resolves the default fork (PyPI torch) and reinstalls the environment. Run +commands from the activated environment (`source .venv/bin/activate`); the examples +below assume it is active, or pass `--no-sync` to one-off `uv run` calls. The workspace contains nine packages. Package-local changes should use the smallest required extra; changes involving the simulator, built-in assets, or @@ -168,15 +167,15 @@ branch unless the change specifically requires them. Run the full test suite before opening a pull request: ```bash -uv run pytest -q +python -m pytest -q ``` For an iteration on one package, run its tests directly, then run the full suite before requesting review: ```bash -uv run pytest motrix_env_core/tests -q -uv run pytest motrix_deploy/tests motrix_deploy_mujoco/tests -q +python -m pytest motrix_env_core/tests -q +python -m pytest motrix_deploy/tests motrix_deploy_mujoco/tests -q ``` Run the repository's formatting, license-header, and lint hooks: @@ -190,17 +189,16 @@ prek run --all-files The hooks run Copywrite, Ruff, and dprint. For individual checks, use: ```bash -uv run ruff check . -uv run ruff format --check . -uv run mypy +ruff check . +ruff format --check . +mypy ``` -When changing documentation, install the `docs` extra and build with warnings -treated as errors: +When changing documentation, build with warnings treated as errors (`sh install.sh --all` +or a runtime install with `--docs` provides the required docs toolchain): ```bash -uv sync --all-packages --extra docs -uv run sphinx-build -W -b html docs/source docs/build/html +sphinx-build -W -b html docs/source docs/build/html ``` The public CI runs the complete workspace test command on Linux for both diff --git a/README.md b/README.md index fda0ef2..d526285 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ _Microduck locomotion policies trained with MotrixLab, rendered in MotrixRender
- MotrixLab architecture: define an environment once, train it with SKRL, RSL-RL or FastSAC on thousands of parallel MotrixSim environments, then deploy the same policy artifact to MuJoCo or Unitree hardware + MotrixLab architecture: define an environment once, train it with SKRL, RSL-RL or FastSAC on thousands of parallel MotrixSim environments running on NVIDIA CUDA or AMD ROCm GPUs, then deploy the same policy artifact to MuJoCo or Unitree hardware
@@ -60,6 +60,7 @@ _Microduck locomotion policies trained with MotrixLab, rendered in MotrixRender | [uv](https://docs.astral.sh/uv/) | Python project and dependency manager — [installation guide](https://docs.astral.sh/uv/getting-started/installation/) | | [Git LFS](https://git-lfs.com) | Robot meshes, motion data, and videos are tracked by LFS | | OS | Linux x86_64 or Windows x86_64; the JAX training backend is Linux-only | +| GPU | NVIDIA (CUDA) or AMD (ROCm) — the matching wheels are selected automatically by `sh install.sh` | ### 1. Clone the repository @@ -71,16 +72,29 @@ git lfs pull ### 2. Install dependencies +Linux: + ```bash -uv sync --all-packages +sh install.sh ``` -This installs all workspace packages together with **PyTorch**, the default training backend used by the built-in FastSAC. Third-party frameworks such as SKRL and RSLRL are optional extras. +Windows (PowerShell): + +```powershell +.\install.ps1 +# if blocked by the execution policy: +powershell -ExecutionPolicy Bypass -File install.ps1 +``` + +This auto-detects your GPU vendor (NVIDIA → CUDA, AMD → ROCm) and installs all workspace packages with the matching PyTorch wheels. Use `--gpu cuda|rocm` to override detection and `--skrl-jax` / `--rslrl` to add training backends — see `sh install.sh --help`. ### 3. Train your first policy +Activate the installed environment (Windows PowerShell: `.venv\Scripts\Activate.ps1`): + ```bash -uv run scripts/train.py task=microduck-walk-flat/motrix.fastsac play=true +source .venv/bin/activate +python scripts/train.py task=microduck-walk-flat/motrix.fastsac play=true ``` While training, the built-in dashboard shows live run progress, episode statistics, throughput, rewards, and system health: @@ -92,7 +106,7 @@ While training, the built-in dashboard shows live run progress, episode statisti Training runs thousands of parallel environment instances; when it finishes, the trained policy is loaded and played in the viewer automatically. Checkpoints and TensorBoard logs are saved under `runs/microduck-walk-flat/`; watch the curves with: ```bash -uv run tensorboard --logdir runs/microduck-walk-flat +tensorboard --logdir runs/microduck-walk-flat ``` Training finishes in minutes: mean return and episode length typically converge after about 4,000 iterations: @@ -106,7 +120,7 @@ Training finishes in minutes: mean return and episode length typically converge Replay the latest trained policy without retraining (for example, after stopping training early with Ctrl+C): ```bash -uv run scripts/play.py env=microduck-walk-flat +python scripts/play.py env=microduck-walk-flat ``` A trained microduck policy replayed in the viewer: @@ -124,7 +138,7 @@ MotrixLab ships 50+ built-in simulation environments spanning basic control, qua | g1-wbt-dance | Whole-body tracking (WBT) | `g1-wbt-dance` · `k1-wbt-freekick` · `g1-29dof-wbt-largebox` | ```bash -uv run scripts/view.py env=go2-walk-rough +python scripts/view.py env=go2-walk-rough ``` See the [full environment gallery](https://motrixlab.readthedocs.io/en/latest/user_guide/envs/index.html) for all registered environments and their supported training algorithms. @@ -144,7 +158,7 @@ Seven reusable robot models are registered out of the box and can be combined in | microduck | `microduck` | Humanoid | 14 | ```bash -uv run scripts/view.py robot=go2 +python scripts/view.py robot=go2 ``` See [Supported Robots](https://motrixlab.readthedocs.io/en/latest/user_guide/robots.html) for configuration details and how to add your own model. diff --git a/README.zh-CN.md b/README.zh-CN.md index 72db638..544e528 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -37,7 +37,7 @@ _使用 MotrixLab 训练的 microduck 行走策略，由 MotrixRender 实时渲
- MotrixLab 架构：环境只需定义一次，即可用 SKRL、RSL-RL 或 FastSAC 在数千个并行 MotrixSim 环境上训练，同一策略产物可部署到 MuJoCo 或 Unitree 硬件 + MotrixLab 架构：环境只需定义一次，即可用 SKRL、RSL-RL 或 FastSAC 在数千个并行 MotrixSim 环境上训练（支持 NVIDIA CUDA 与 AMD ROCm GPU），同一策略产物可部署到 MuJoCo 或 Unitree 硬件
@@ -60,6 +60,7 @@ _使用 MotrixLab 训练的 microduck 行走策略，由 MotrixRender 实时渲 | [uv](https://docs.astral.sh/uv/) | Python 项目与依赖管理工具 — [安装指南](https://docs.astral.sh/uv/getting-started/installation/) | | [Git LFS](https://git-lfs.com) | 机器人网格、运动数据与视频由 LFS 管理 | | 操作系统 | Linux x86_64 或 Windows x86_64；JAX 训练后端仅支持 Linux | +| GPU | NVIDIA（CUDA）或 AMD（ROCm）——`sh install.sh` 自动选择对应 wheel | ### 1. 克隆仓库 @@ -71,16 +72,29 @@ git lfs pull ### 2. 安装依赖 +Linux： + ```bash -uv sync --all-packages +sh install.sh ``` -该命令会安装全部 workspace package，以及内置 FastSAC 所需的默认训练后端 **PyTorch**。SKRL、RSLRL 等第三方训练框架为可选 extras。 +Windows（PowerShell）： + +```powershell +.\install.ps1 +# 若提示执行策略受限： +powershell -ExecutionPolicy Bypass -File install.ps1 +``` + +脚本会自动探测 GPU 厂商（NVIDIA → CUDA，AMD → ROCm），安装全部 workspace package 及对应的 PyTorch wheel。可用 `--gpu cuda|rocm` 显式指定 GPU，`--skrl-jax` / `--rslrl` 追加训练后端——详见 `sh install.sh --help`。 ### 3. 训练第一个策略 +先激活安装好的环境（Windows PowerShell：`.venv\Scripts\Activate.ps1`）： + ```bash -uv run scripts/train.py task=microduck-walk-flat/motrix.fastsac play=true +source .venv/bin/activate +python scripts/train.py task=microduck-walk-flat/motrix.fastsac play=true ``` 训练过程中，内置面板会实时显示运行进度、回合统计、吞吐、奖励与系统健康状态： @@ -92,7 +106,7 @@ uv run scripts/train.py task=microduck-walk-flat/motrix.fastsac play=true 训练会启动数千个并行环境实例；训练结束后会自动加载策略并在查看器中回放。checkpoint 与 TensorBoard 日志保存在 `runs/microduck-walk-flat/` 目录下，通过以下命令查看训练曲线： ```bash -uv run tensorboard --logdir runs/microduck-walk-flat +tensorboard --logdir runs/microduck-walk-flat ``` microduck 的训练数分钟内即可完成：平均回报与回合长度通常在约 4,000 次迭代后收敛： @@ -106,7 +120,7 @@ microduck 的训练数分钟内即可完成：平均回报与回合长度通常 无需重新训练即可回放最近一次训练得到的策略（例如提前 Ctrl+C 中断训练之后）： ```bash -uv run scripts/play.py env=microduck-walk-flat +python scripts/play.py env=microduck-walk-flat ``` 训练好的 microduck 策略在查看器中的回放效果： @@ -124,7 +138,7 @@ MotrixLab 内置 50+ 个仿真环境，覆盖基础控制、四足、人形、 | g1-wbt-dance | 全身动作跟踪（WBT） | `g1-wbt-dance` · `k1-wbt-freekick` · `g1-29dof-wbt-largebox` | ```bash -uv run scripts/view.py env=go2-walk-rough +python scripts/view.py env=go2-walk-rough ``` 完整环境列表与各环境支持的训练算法见[环境总览](https://motrixlab.readthedocs.io/zh-cn/latest/user_guide/envs/index.html)。 @@ -144,7 +158,7 @@ uv run scripts/view.py env=go2-walk-rough | microduck | `microduck` | 人形机器人 | 14 | ```bash -uv run scripts/view.py robot=go2 +python scripts/view.py robot=go2 ``` 机器人配置细节与自定义新模型的方法见[支持的机器人](https://motrixlab.readthedocs.io/zh-cn/latest/user_guide/robots.html)。 diff --git a/configs/task/microduck-walk-flat/motrix.fastsac.yaml b/configs/task/microduck-walk-flat/motrix.fastsac.yaml index f995fe0..1c19406 100644 --- a/configs/task/microduck-walk-flat/motrix.fastsac.yaml +++ b/configs/task/microduck-walk-flat/motrix.fastsac.yaml @@ -16,4 +16,8 @@ algo: agent: alpha_init: 0.01 target_entropy_ratio: -0.1 + batch_size: 2048 num_updates: 4 + trainer: + async_options: + utd_mode: strict diff --git a/docs/source/_static/images/architecture-dark.svg b/docs/source/_static/images/architecture-dark.svg index 85612d1..08a3ab1 100644 --- a/docs/source/_static/images/architecture-dark.svg +++ b/docs/source/_static/images/architecture-dark.svg @@ -1,4 +1,4 @@ - + @@ -13,102 +13,113 @@ .body { font-size: 13.5px; fill: #e6edf3; } .sub { font-size: 12px; fill: #9198a1; } .tag { font-size: 11.5px; font-weight: 600; } - .badge { font-size: 12.5px; font-weight: 600; fill: #a5b4fc; } + .label { font-size: 11px; font-weight: 600; letter-spacing: 1.5px; fill: #9198a1; } + .pill { font-size: 11.5px; fill: #e6edf3; } + .slogan { font-size: 17px; font-weight: 800; fill: #ffffff; } .card { fill: #161b22; stroke: #30363d; stroke-width: 1.2; } .flow { fill: none; stroke: #818cf8; stroke-width: 1.8; marker-end: url(#arrow); } .flow-future { fill: none; stroke: #818cf8; stroke-width: 1.8; stroke-dasharray: 5 4; opacity: 0.7; marker-end: url(#arrow); } - + - - - One CLI, end to end:  train  ·  play  ·  view  ·  export  ·  deploy + + + One definition, every framework + + + Minutes to converge, not hours + + + From sim to real, one artifact - DEFINE ONCE - TRAIN FAST - DEPLOY ANYWHERE + DEFINE ONCE + TRAIN FAST + DEPLOY ANYWHERE - - Environment definition - - ▸Observations & actions - ▸Rewards & terminations - ▸Scene, robot & assets - ▸One shared config schema - - BUILT-IN LIBRARY - - Basic control - - Locomotion - - Manipulation - - Whole-body tracking + + Environment definition + + ▸Observations & actions + ▸Rewards & terminations + ▸Scene, robot & assets + ▸One shared config schema + + BUILT-IN LIBRARY + + Basic control + + Locomotion + + Manipulation + + Whole-body tracking - - SKRL - - JAX · PyTorch - - - RSL-RL - - PyTorch - - - FastSAC - - built-in ★ - - - Thousands of parallel environments - on MotrixSim + + SKRL + + JAX · PyTorch + + + RSL-RL + + PyTorch + + + FastSAC + + built-in ★ + + + Thousands of parallel environments + on MotrixSim + + + GPU BACKENDS + + + NVIDIA · CUDA + + + AMD · ROCm - - policy artifact - .onnx / .pt + + policy artifact + .onnx / .pt - - MuJoCo - simulation replay - - SIM - - - Unitree - real hardware · DDS - - REAL - - - + more hardware - on the roadmap + + MuJoCo + simulation replay + + SIM + + + Unitree + real hardware · DDS + + REAL + + + + more hardware + on the roadmap - - - - - - - - - - - - - One definition, every framework - - - Minutes to converge, not hours - - - From sim to real, one artifact + + + + + + + + + + + + + One CLI, end to end:  train  ·  play  ·  deploy diff --git a/docs/source/_static/images/architecture-light.svg b/docs/source/_static/images/architecture-light.svg index 9aea7c8..942a33e 100644 --- a/docs/source/_static/images/architecture-light.svg +++ b/docs/source/_static/images/architecture-light.svg @@ -1,4 +1,4 @@ - + @@ -13,102 +13,113 @@ .body { font-size: 13.5px; fill: #1f2328; } .sub { font-size: 12px; fill: #57606a; } .tag { font-size: 11.5px; font-weight: 600; } - .badge { font-size: 12.5px; font-weight: 600; fill: #4338ca; } + .label { font-size: 11px; font-weight: 600; letter-spacing: 1.5px; fill: #57606a; } + .pill { font-size: 11.5px; fill: #1f2328; } + .slogan { font-size: 17px; font-weight: 800; fill: #ffffff; } .card { fill: #ffffff; stroke: #d0d7de; stroke-width: 1.2; } .flow { fill: none; stroke: #4f46e5; stroke-width: 1.8; marker-end: url(#arrow); } .flow-future { fill: none; stroke: #4f46e5; stroke-width: 1.8; stroke-dasharray: 5 4; opacity: 0.7; marker-end: url(#arrow); } - + - - - One CLI, end to end:  train  ·  play  ·  view  ·  export  ·  deploy + + + One definition, every framework + + + Minutes to converge, not hours + + + From sim to real, one artifact - DEFINE ONCE - TRAIN FAST - DEPLOY ANYWHERE + DEFINE ONCE + TRAIN FAST + DEPLOY ANYWHERE - - Environment definition - - ▸Observations & actions - ▸Rewards & terminations - ▸Scene, robot & assets - ▸One shared config schema - - BUILT-IN LIBRARY - - Basic control - - Locomotion - - Manipulation - - Whole-body tracking + + Environment definition + + ▸Observations & actions + ▸Rewards & terminations + ▸Scene, robot & assets + ▸One shared config schema + + BUILT-IN LIBRARY + + Basic control + + Locomotion + + Manipulation + + Whole-body tracking - - SKRL - - JAX · PyTorch - - - RSL-RL - - PyTorch - - - FastSAC - - built-in ★ - - - Thousands of parallel environments - on MotrixSim + + SKRL + + JAX · PyTorch + + + RSL-RL + + PyTorch + + + FastSAC + + built-in ★ + + + Thousands of parallel environments + on MotrixSim + + + GPU BACKENDS + + + NVIDIA · CUDA + + + AMD · ROCm - - policy artifact - .onnx / .pt + + policy artifact + .onnx / .pt - - MuJoCo - simulation replay - - SIM - - - Unitree - real hardware · DDS - - REAL - - - + more hardware - on the roadmap + + MuJoCo + simulation replay + + SIM + + + Unitree + real hardware · DDS + + REAL + + + + more hardware + on the roadmap - - - - - - - - - - - - - One definition, every framework - - - Minutes to converge, not hours - - - From sim to real, one artifact + + + + + + + + + + + + + One CLI, end to end:  train  ·  play  ·  deploy diff --git a/docs/source/en/user_guide/envs/basic/acrobot.md b/docs/source/en/user_guide/envs/basic/acrobot.md index 0b876c8..4c51de5 100644 --- a/docs/source/en/user_guide/envs/basic/acrobot.md +++ b/docs/source/en/user_guide/envs/basic/acrobot.md @@ -63,36 +63,36 @@ Order: `upper_arm_horizontal, lower_arm_horizontal, upper_arm_vertical, lower_ar ### 1. Environment Preview ```bash -uv run scripts/view.py env=acrobot +python scripts/view.py env=acrobot ``` ### 2. Start Training ```bash # Train with default parameters -uv run scripts/train.py task=acrobot/skrl.ppo +python scripts/train.py task=acrobot/skrl.ppo # Customize parallel environments -uv run scripts/train.py task=acrobot/skrl.ppo num_envs=1024 +python scripts/train.py task=acrobot/skrl.ppo num_envs=1024 # Enable rendering during training -uv run scripts/train.py task=acrobot/skrl.ppo render=true +python scripts/train.py task=acrobot/skrl.ppo render=true ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/acrobot +tensorboard --logdir runs/acrobot ``` ### 4. Test Training Results ```bash # Auto-discover best policy (recommended) -uv run scripts/play.py env=acrobot +python scripts/play.py env=acrobot # Manually specify a checkpoint from a metadata-backed run -uv run scripts/play.py env=acrobot policy=/path/to/run/checkpoints/policy-file +python scripts/play.py env=acrobot policy=/path/to/run/checkpoints/policy-file ``` > **Tip**: Policies are auto-selected from `runs/acrobot/`. Use `policy=...` to select a checkpoint whose parent run contains `metadata.json`. diff --git a/docs/source/en/user_guide/envs/basic/cartpole.md b/docs/source/en/user_guide/envs/basic/cartpole.md index f5145f6..cc28a16 100644 --- a/docs/source/en/user_guide/envs/basic/cartpole.md +++ b/docs/source/en/user_guide/envs/basic/cartpole.md @@ -15,25 +15,25 @@ CartPole is a classic control task in reinforcement learning. The goal is to kee ### 1. Environment Preview ```bash -uv run scripts/view.py env=cartpole +python scripts/view.py env=cartpole ``` ### 2. Start Training ```bash -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/cartpole +tensorboard --logdir runs/cartpole ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole ``` > **Tip**: The system finds the latest metadata-backed run under `runs/cartpole/` and loads its `best_policy` artifact. Use `policy=...` to select another checkpoint from a metadata-backed run. diff --git a/docs/source/en/user_guide/envs/basic/pendulum.md b/docs/source/en/user_guide/envs/basic/pendulum.md index 155c188..0b05fad 100644 --- a/docs/source/en/user_guide/envs/basic/pendulum.md +++ b/docs/source/en/user_guide/envs/basic/pendulum.md @@ -62,36 +62,36 @@ Order: `cos(theta), sin(theta), angular velocity`. ### 1. Environment Preview ```bash -uv run scripts/view.py env=pendulum +python scripts/view.py env=pendulum ``` ### 2. Start Training ```bash # Train with default parameters -uv run scripts/train.py task=pendulum/skrl.ppo +python scripts/train.py task=pendulum/skrl.ppo # Customize parallel environments -uv run scripts/train.py task=pendulum/skrl.ppo num_envs=1024 +python scripts/train.py task=pendulum/skrl.ppo num_envs=1024 # Enable rendering during training -uv run scripts/train.py task=pendulum/skrl.ppo render=true +python scripts/train.py task=pendulum/skrl.ppo render=true ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/pendulum +tensorboard --logdir runs/pendulum ``` ### 4. Test Training Results ```bash # Auto-discover best policy (recommended) -uv run scripts/play.py env=pendulum +python scripts/play.py env=pendulum # Manually specify a checkpoint from a metadata-backed run -uv run scripts/play.py env=pendulum policy=/path/to/run/checkpoints/policy-file +python scripts/play.py env=pendulum policy=/path/to/run/checkpoints/policy-file ``` > **Tip**: Policies are auto-selected from `runs/pendulum/`. Use `policy=...` to select a checkpoint whose parent run contains `metadata.json`. diff --git a/docs/source/en/user_guide/envs/basic/stewart.md b/docs/source/en/user_guide/envs/basic/stewart.md index 20d9b54..33a78ab 100644 --- a/docs/source/en/user_guide/envs/basic/stewart.md +++ b/docs/source/en/user_guide/envs/basic/stewart.md @@ -156,15 +156,15 @@ An episode terminates when any of the following is true: ### 1. Environment Preview ```bash -uv run scripts/view.py env=stewart-static -uv run scripts/view.py env=stewart-disturb-xy +python scripts/view.py env=stewart-static +python scripts/view.py env=stewart-disturb-xy ``` ### 2. Start Training ```bash -uv run scripts/train.py task=stewart-static/skrl.ppo -uv run scripts/train.py task=stewart-disturb-xy/skrl.ppo +python scripts/train.py task=stewart-static/skrl.ppo +python scripts/train.py task=stewart-disturb-xy/skrl.ppo ``` ### Training Notes @@ -176,15 +176,15 @@ uv run scripts/train.py task=stewart-disturb-xy/skrl.ppo ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/stewart-static -uv run tensorboard --logdir runs/stewart-disturb-xy +tensorboard --logdir runs/stewart-static +tensorboard --logdir runs/stewart-disturb-xy ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=stewart-static -uv run scripts/play.py env=stewart-disturb-xy +python scripts/play.py env=stewart-static +python scripts/play.py env=stewart-disturb-xy ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/bring_ball.md b/docs/source/en/user_guide/envs/dm_control/bring_ball.md index 1f62920..63907aa 100644 --- a/docs/source/en/user_guide/envs/dm_control/bring_ball.md +++ b/docs/source/en/user_guide/envs/dm_control/bring_ball.md @@ -137,25 +137,25 @@ Default weights (from `BringBallCfg`): ### 1. Environment Preview (random actions) ```bash -uv run scripts/view.py env=dm-manipulator-bring-ball +python scripts/view.py env=dm-manipulator-bring-ball ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-manipulator-bring-ball/skrl.ppo.torch +python scripts/train.py task=dm-manipulator-bring-ball/skrl.ppo.torch ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-manipulator-bring-ball +tensorboard --logdir runs/dm-manipulator-bring-ball ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-manipulator-bring-ball +python scripts/play.py env=dm-manipulator-bring-ball ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_cheetah.md b/docs/source/en/user_guide/envs/dm_control/dm_cheetah.md index 633fc6d..75ddf3a 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_cheetah.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_cheetah.md @@ -102,25 +102,25 @@ The cheetah's reward function consists of the following parts: ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-cheetah +python scripts/view.py env=dm-cheetah ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-cheetah/skrl.ppo +python scripts/train.py task=dm-cheetah/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-cheetah +tensorboard --logdir runs/dm-cheetah ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-cheetah +python scripts/play.py env=dm-cheetah ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_finger.md b/docs/source/en/user_guide/envs/dm_control/dm_finger.md index 686a13f..491a3b8 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_finger.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_finger.md @@ -131,35 +131,35 @@ If `NaN` appears in the observations ### 1. Environment Preview (random actions) ```bash -uv run scripts/view.py env=dm-finger-spin +python scripts/view.py env=dm-finger-spin ``` ```bash -uv run scripts/view.py env=dm-finger-turn-easy +python scripts/view.py env=dm-finger-turn-easy ``` ```bash -uv run scripts/view.py env=dm-finger-turn-hard +python scripts/view.py env=dm-finger-turn-hard ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-finger-spin/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-spin/skrl.ppo task.train_backend=torch ``` ```bash -uv run scripts/train.py task=dm-finger-turn-easy/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-turn-easy/skrl.ppo task.train_backend=torch ``` ```bash -uv run scripts/train.py task=dm-finger-turn-hard/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-turn-hard/skrl.ppo task.train_backend=torch ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-finger-spin +tensorboard --logdir runs/dm-finger-spin ``` ### 4. Test Training Results @@ -167,7 +167,7 @@ uv run tensorboard --logdir runs/dm-finger-spin `scripts/play.py` auto-discovers the latest metadata-backed run under `runs/{env-name}/` and loads its `best_policy` artifact. Use `policy=...` to select a checkpoint explicitly: ```bash -uv run scripts/play.py env=dm-finger-turn-hard +python scripts/play.py env=dm-finger-turn-hard ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_hopper.md b/docs/source/en/user_guide/envs/dm_control/dm_hopper.md index d2184bc..e1fcbb3 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_hopper.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_hopper.md @@ -108,28 +108,28 @@ The Hopper reward consists of the following terms: ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-hopper-stand -uv run scripts/view.py env=dm-hopper-hop +python scripts/view.py env=dm-hopper-stand +python scripts/view.py env=dm-hopper-hop ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-hopper-stand/skrl.ppo -uv run scripts/train.py task=dm-hopper-hop/skrl.ppo +python scripts/train.py task=dm-hopper-stand/skrl.ppo +python scripts/train.py task=dm-hopper-hop/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-hopper-stand +tensorboard --logdir runs/dm-hopper-stand ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-hopper-stand -uv run scripts/play.py env=dm-hopper-hop +python scripts/play.py env=dm-hopper-stand +python scripts/play.py env=dm-hopper-hop ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_humanoid.md b/docs/source/en/user_guide/envs/dm_control/dm_humanoid.md index 3c7e8a0..62f8fde 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_humanoid.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_humanoid.md @@ -185,31 +185,31 @@ total_reward = posture_reward * speed_reward * energy_reward * gait_reward ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-humanoid-stand -uv run scripts/view.py env=dm-humanoid-walk -uv run scripts/view.py env=dm-humanoid-run +python scripts/view.py env=dm-humanoid-stand +python scripts/view.py env=dm-humanoid-walk +python scripts/view.py env=dm-humanoid-run ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-humanoid-stand/skrl.ppo -uv run scripts/train.py task=dm-humanoid-walk/skrl.ppo -uv run scripts/train.py task=dm-humanoid-run/skrl.ppo +python scripts/train.py task=dm-humanoid-stand/skrl.ppo +python scripts/train.py task=dm-humanoid-walk/skrl.ppo +python scripts/train.py task=dm-humanoid-run/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-humanoid-walk +tensorboard --logdir runs/dm-humanoid-walk ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-humanoid-stand -uv run scripts/play.py env=dm-humanoid-walk -uv run scripts/play.py env=dm-humanoid-run +python scripts/play.py env=dm-humanoid-stand +python scripts/play.py env=dm-humanoid-walk +python scripts/play.py env=dm-humanoid-run ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_lqr.md b/docs/source/en/user_guide/envs/dm_control/dm_lqr.md index be29300..1f15671 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_lqr.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_lqr.md @@ -157,29 +157,29 @@ An episode terminates and resets when any of the following conditions is met: ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-lqr-2-1 -uv run scripts/view.py env=dm-lqr-6-2 +python scripts/view.py env=dm-lqr-2-1 +python scripts/view.py env=dm-lqr-6-2 ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-lqr-2-1/skrl.ppo -uv run scripts/train.py task=dm-lqr-6-2/skrl.ppo +python scripts/train.py task=dm-lqr-2-1/skrl.ppo +python scripts/train.py task=dm-lqr-6-2/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-lqr-2-1 -uv run tensorboard --logdir runs/dm-lqr-6-2 +tensorboard --logdir runs/dm-lqr-2-1 +tensorboard --logdir runs/dm-lqr-6-2 ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-lqr-2-1 -uv run scripts/play.py env=dm-lqr-6-2 +python scripts/play.py env=dm-lqr-2-1 +python scripts/play.py env=dm-lqr-6-2 ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_point_mass.md b/docs/source/en/user_guide/envs/dm_control/dm_point_mass.md index b55446e..dc10b04 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_point_mass.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_point_mass.md @@ -115,25 +115,25 @@ rwd = distance_reward + target_bonus + continuous_reward + path_reward - center_ ### 1. Environment Preview ```bash -uv run scripts/view.py env=point_mass +python scripts/view.py env=point_mass ``` ### 2. Start Training ```bash -uv run scripts/train.py task=point_mass/skrl.ppo +python scripts/train.py task=point_mass/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/point_mass +tensorboard --logdir runs/point_mass ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=point_mass +python scripts/play.py env=point_mass ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_quadruped.md b/docs/source/en/user_guide/envs/dm_control/dm_quadruped.md index 82075af..6046738 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_quadruped.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_quadruped.md @@ -228,37 +228,37 @@ In addition, `fetch` uses a `stability` gate on torso uprightness and torso heig ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-quadruped-walk -uv run scripts/view.py env=dm-quadruped-run -uv run scripts/view.py env=dm-quadruped-escape -uv run scripts/view.py env=dm-quadruped-fetch +python scripts/view.py env=dm-quadruped-walk +python scripts/view.py env=dm-quadruped-run +python scripts/view.py env=dm-quadruped-escape +python scripts/view.py env=dm-quadruped-fetch ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-quadruped-walk/skrl.ppo -uv run scripts/train.py task=dm-quadruped-run/skrl.ppo -uv run scripts/train.py task=dm-quadruped-escape/skrl.ppo -uv run scripts/train.py task=dm-quadruped-fetch/skrl.ppo +python scripts/train.py task=dm-quadruped-walk/skrl.ppo +python scripts/train.py task=dm-quadruped-run/skrl.ppo +python scripts/train.py task=dm-quadruped-escape/skrl.ppo +python scripts/train.py task=dm-quadruped-fetch/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-quadruped-walk -uv run tensorboard --logdir runs/dm-quadruped-run -uv run tensorboard --logdir runs/dm-quadruped-escape -uv run tensorboard --logdir runs/dm-quadruped-fetch +tensorboard --logdir runs/dm-quadruped-walk +tensorboard --logdir runs/dm-quadruped-run +tensorboard --logdir runs/dm-quadruped-escape +tensorboard --logdir runs/dm-quadruped-fetch ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-quadruped-walk -uv run scripts/play.py env=dm-quadruped-run -uv run scripts/play.py env=dm-quadruped-escape -uv run scripts/play.py env=dm-quadruped-fetch +python scripts/play.py env=dm-quadruped-walk +python scripts/play.py env=dm-quadruped-run +python scripts/play.py env=dm-quadruped-escape +python scripts/play.py env=dm-quadruped-fetch ``` --- diff --git a/docs/source/en/user_guide/envs/dm_control/dm_reacher.md b/docs/source/en/user_guide/envs/dm_control/dm_reacher.md index 944d2ea..944abf3 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_reacher.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_reacher.md @@ -102,25 +102,25 @@ terminated = True ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-reacher +python scripts/view.py env=dm-reacher ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-reacher/skrl.ppo +python scripts/train.py task=dm-reacher/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-reacher +tensorboard --logdir runs/dm-reacher ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-reacher +python scripts/play.py env=dm-reacher ``` ## Expected Training Results diff --git a/docs/source/en/user_guide/envs/dm_control/dm_walker.md b/docs/source/en/user_guide/envs/dm_control/dm_walker.md index 45c2603..be2b6fb 100644 --- a/docs/source/en/user_guide/envs/dm_control/dm_walker.md +++ b/docs/source/en/user_guide/envs/dm_control/dm_walker.md @@ -26,19 +26,19 @@ Walker2D is a 2D planar bipedal robot with multiple joints and actuators: 1. **dm-stander**: Static standing task (move_speed = 0.0) ```bash -uv run scripts/train.py task=dm-stander/skrl.ppo +python scripts/train.py task=dm-stander/skrl.ppo ``` 2. **dm-walker**: Walking task (move_speed = 1.0) ```bash -uv run scripts/train.py task=dm-walker/skrl.ppo +python scripts/train.py task=dm-walker/skrl.ppo ``` 3. **dm-runner**: Running task (move_speed = 5.0) ```bash -uv run scripts/train.py task=dm-runner/skrl.ppo +python scripts/train.py task=dm-runner/skrl.ppo ``` ## Quick Start @@ -46,31 +46,31 @@ uv run scripts/train.py task=dm-runner/skrl.ppo ### 1. Environment Preview ```bash -uv run scripts/view.py env=dm-stander -uv run scripts/view.py env=dm-walker -uv run scripts/view.py env=dm-runner +python scripts/view.py env=dm-stander +python scripts/view.py env=dm-walker +python scripts/view.py env=dm-runner ``` ### 2. Start Training ```bash -uv run scripts/train.py task=dm-stander/skrl.ppo -uv run scripts/train.py task=dm-walker/skrl.ppo -uv run scripts/train.py task=dm-runner/skrl.ppo +python scripts/train.py task=dm-stander/skrl.ppo +python scripts/train.py task=dm-walker/skrl.ppo +python scripts/train.py task=dm-runner/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/dm-walker +tensorboard --logdir runs/dm-walker ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=dm-stander -uv run scripts/play.py env=dm-walker -uv run scripts/play.py env=dm-runner +python scripts/play.py env=dm-stander +python scripts/play.py env=dm-walker +python scripts/play.py env=dm-runner ``` ## Reward Function Design diff --git a/docs/source/en/user_guide/envs/humanoid_velocity_tracking.md b/docs/source/en/user_guide/envs/humanoid_velocity_tracking.md index 803abb8..6291dbd 100644 --- a/docs/source/en/user_guide/envs/humanoid_velocity_tracking.md +++ b/docs/source/en/user_guide/envs/humanoid_velocity_tracking.md @@ -93,15 +93,15 @@ weight and mean return may decrease; this is a change in reward scale rather tha Choose an environment ID and one of its training configs from the table, then replace `ENV_ID` and `TRAINING_CONFIG`: ```bash -uv run scripts/view.py env=ENV_ID num_envs=1 -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/view.py env=ENV_ID num_envs=1 +python scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/play.py env=ENV_ID num_envs=16 ``` For example, train the K1 uneven-terrain task with asynchronous FastSAC: ```bash -uv run scripts/train.py task=k1-walk-rough/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=k1-walk-rough/motrix.fastsac algo.asynchronous=true ``` `view.py` applies random actions and is intended for scene and model inspection. Use `play.py` with a trained policy to diff --git a/docs/source/en/user_guide/envs/humanoid_velocity_tracking/adding_robot.md b/docs/source/en/user_guide/envs/humanoid_velocity_tracking/adding_robot.md index a56e81c..7820175 100644 --- a/docs/source/en/user_guide/envs/humanoid_velocity_tracking/adding_robot.md +++ b/docs/source/en/user_guide/envs/humanoid_velocity_tracking/adding_robot.md @@ -118,7 +118,7 @@ environment construction. Preview the robot and verify joint ordering, the default pose, PD parameters, sole sites, and collision-geom names: ```bash -uv run scripts/view.py robot= +python scripts/view.py robot= ``` ## 4. Register the environment implementation @@ -164,13 +164,13 @@ dynamics and training behavior. Use this progression: ```bash -uv run scripts/view.py robot= -uv run scripts/view.py env=-walk-flat -uv run scripts/view.py env=-walk-terrain -uv run scripts/train.py task=-walk-flat/motrix.fastsac -uv run scripts/train.py task=-walk-flat/motrix.fastsac algo.asynchronous=false -uv run pytest motrix_envs/tests/test_humanoid_walk.py -q -uv run pytest motrix_rl/tests/test_task_configs.py -q +python scripts/view.py robot= +python scripts/view.py env=-walk-flat +python scripts/view.py env=-walk-terrain +python scripts/train.py task=-walk-flat/motrix.fastsac +python scripts/train.py task=-walk-flat/motrix.fastsac algo.asynchronous=false +python -m pytest motrix_envs/tests/test_humanoid_walk.py -q +python -m pytest motrix_rl/tests/test_task_configs.py -q ``` During preview, inspect the default pose, sole height, ground contacts, action direction, and terrain spawn position. For diff --git a/docs/source/en/user_guide/envs/index.md b/docs/source/en/user_guide/envs/index.md index 29a510c..cb73f1c 100644 --- a/docs/source/en/user_guide/envs/index.md +++ b/docs/source/en/user_guide/envs/index.md @@ -5,7 +5,7 @@ A MotrixLab Environment is a simulation task created through the Env Registry. I Preview an environment without starting training: ```bash -uv run scripts/view.py env= +python scripts/view.py env= ``` ## Environment topics diff --git a/docs/source/en/user_guide/envs/manipulation/bounce_ball.md b/docs/source/en/user_guide/envs/manipulation/bounce_ball.md index 3089957..3adc7ae 100644 --- a/docs/source/en/user_guide/envs/manipulation/bounce_ball.md +++ b/docs/source/en/user_guide/envs/manipulation/bounce_ball.md @@ -411,25 +411,25 @@ Target height for each environment is randomly sampled in [0.4, 0.6] m range to ### 1. Environment Preview ```bash -uv run scripts/view.py env=bounce_ball +python scripts/view.py env=bounce_ball ``` ### 2. Start Training ```bash -uv run scripts/train.py task=bounce_ball/skrl.ppo +python scripts/train.py task=bounce_ball/skrl.ppo ``` ### 3. View Training Progress ```bash -uv run tensorboard --logdir runs/bounce_ball +tensorboard --logdir runs/bounce_ball ``` ### 4. Test Training Results ```bash -uv run scripts/play.py env=bounce_ball +python scripts/play.py env=bounce_ball ``` --- diff --git a/docs/source/en/user_guide/envs/manipulation/franka_lift_cube.md b/docs/source/en/user_guide/envs/manipulation/franka_lift_cube.md index e9c1216..32b43f5 100644 --- a/docs/source/en/user_guide/envs/manipulation/franka_lift_cube.md +++ b/docs/source/en/user_guide/envs/manipulation/franka_lift_cube.md @@ -233,17 +233,17 @@ Target position is randomly sampled in the following range: ### Training ```bash -uv run scripts/train.py task=franka-lift-cube/skrl.ppo +python scripts/train.py task=franka-lift-cube/skrl.ppo ``` ### Policy Evaluation ```bash -uv run scripts/play.py env=franka-lift-cube +python scripts/play.py env=franka-lift-cube ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/franka-lift-cube +tensorboard --logdir runs/franka-lift-cube ``` diff --git a/docs/source/en/user_guide/envs/manipulation/franka_open_cabinet.md b/docs/source/en/user_guide/envs/manipulation/franka_open_cabinet.md index c34f9d2..5ec45c0 100644 --- a/docs/source/en/user_guide/envs/manipulation/franka_open_cabinet.md +++ b/docs/source/en/user_guide/envs/manipulation/franka_open_cabinet.md @@ -234,17 +234,17 @@ Cabinet is fixed on ground with drawer in closed state (joint position at 0). ### Training ```bash -uv run scripts/train.py task=franka-open-cabinet/skrl.ppo +python scripts/train.py task=franka-open-cabinet/skrl.ppo ``` ### Policy Evaluation ```bash -uv run scripts/play.py env=franka-open-cabinet +python scripts/play.py env=franka-open-cabinet ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/franka-open-cabinet +tensorboard --logdir runs/franka-open-cabinet ``` diff --git a/docs/source/en/user_guide/envs/manipulation/rm65_insert_peg.md b/docs/source/en/user_guide/envs/manipulation/rm65_insert_peg.md index 5ca388b..fa814f3 100644 --- a/docs/source/en/user_guide/envs/manipulation/rm65_insert_peg.md +++ b/docs/source/en/user_guide/envs/manipulation/rm65_insert_peg.md @@ -269,17 +269,17 @@ The task currently provides an Async FastSAC configuration with `2048` parallel ### Training ```bash -uv run scripts/train.py task=rm65_insert_peg/motrix.fastsac +python scripts/train.py task=rm65_insert_peg/motrix.fastsac ``` ### Policy Evaluation ```bash -uv run scripts/play.py env=rm65_insert_peg num_envs=16 +python scripts/play.py env=rm65_insert_peg num_envs=16 ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/rm65_insert_peg/fastsac +tensorboard --logdir runs/rm65_insert_peg/fastsac ``` diff --git a/docs/source/en/user_guide/envs/manipulation/rm65_open_cabinet.md b/docs/source/en/user_guide/envs/manipulation/rm65_open_cabinet.md index 2a780f9..4c1c26e 100644 --- a/docs/source/en/user_guide/envs/manipulation/rm65_open_cabinet.md +++ b/docs/source/en/user_guide/envs/manipulation/rm65_open_cabinet.md @@ -241,17 +241,17 @@ In addition, the maximum episode length is `30s`. ### Training ```bash -uv run scripts/train.py task=rm65-open-cabinet/skrl.ppo task.train_backend=torch +python scripts/train.py task=rm65-open-cabinet/skrl.ppo task.train_backend=torch ``` ### Policy Evaluation ```bash -uv run scripts/play.py env=rm65-open-cabinet +python scripts/play.py env=rm65-open-cabinet ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/rm65_open_cabinet +tensorboard --logdir runs/rm65_open_cabinet ``` diff --git a/docs/source/en/user_guide/envs/manipulation/shadow_hand_repose.md b/docs/source/en/user_guide/envs/manipulation/shadow_hand_repose.md index ea0e982..3e94016 100644 --- a/docs/source/en/user_guide/envs/manipulation/shadow_hand_repose.md +++ b/docs/source/en/user_guide/envs/manipulation/shadow_hand_repose.md @@ -308,25 +308,25 @@ The environment uses a consecutive success counter: ### Training ```bash -uv run scripts/train.py task=shadow-hand-repose/skrl.ppo +python scripts/train.py task=shadow-hand-repose/skrl.ppo ``` ### Policy Evaluation ```bash -uv run scripts/play.py env=shadow-hand-repose +python scripts/play.py env=shadow-hand-repose ``` ### Environment Visualization ```bash -uv run scripts/view.py env=shadow-hand-repose +python scripts/view.py env=shadow-hand-repose ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/shadow-hand-repose +tensorboard --logdir runs/shadow-hand-repose ``` --- diff --git a/docs/source/en/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md b/docs/source/en/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md index 93e6a18..0bf2650 100644 --- a/docs/source/en/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md +++ b/docs/source/en/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md @@ -26,7 +26,7 @@ horizontal linear-speed sum exceeds $10^8$. The default `20 s` limit produces tr physical parameters. ```bash -uv run scripts/view.py env=go1-stairs-terrain-walk -uv run scripts/train.py task=go1-stairs-terrain-walk/skrl.ppo -uv run scripts/play.py env=go1-stairs-terrain-walk +python scripts/view.py env=go1-stairs-terrain-walk +python scripts/train.py task=go1-stairs-terrain-walk/skrl.ppo +python scripts/play.py env=go1-stairs-terrain-walk ``` diff --git a/docs/source/en/user_guide/envs/quadruped_velocity_tracking.md b/docs/source/en/user_guide/envs/quadruped_velocity_tracking.md index 22366ef..9ca0244 100644 --- a/docs/source/en/user_guide/envs/quadruped_velocity_tracking.md +++ b/docs/source/en/user_guide/envs/quadruped_velocity_tracking.md @@ -114,15 +114,15 @@ sustained rough-terrain locomotion. Under the current training configs and hardw Choose an environment ID and one of its training configs from the table, then replace `ENV_ID` and `TRAINING_CONFIG`: ```bash -uv run scripts/view.py env=ENV_ID num_envs=1 -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/view.py env=ENV_ID num_envs=1 +python scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/play.py env=ENV_ID num_envs=16 ``` For example, train the Go2 rough-terrain task with RSL-RL PPO: ```bash -uv run scripts/train.py task=go2-walk-rough/rslrl.ppo +python scripts/train.py task=go2-walk-rough/rslrl.ppo ``` `view.py` applies random actions and is intended for scene and model inspection. Use `play.py` with a trained policy to diff --git a/docs/source/en/user_guide/envs/quadruped_velocity_tracking/adding_robot.md b/docs/source/en/user_guide/envs/quadruped_velocity_tracking/adding_robot.md index 5660538..07e1be8 100644 --- a/docs/source/en/user_guide/envs/quadruped_velocity_tracking/adding_robot.md +++ b/docs/source/en/user_guide/envs/quadruped_velocity_tracking/adding_robot.md @@ -69,7 +69,7 @@ rejected. Every actuator must target a joint and expose a valid control range. After registering the robot config, preview it independently: ```bash -uv run scripts/view.py robot= +python scripts/view.py robot= ``` ## 2. Provide task sensors @@ -197,10 +197,10 @@ inherit the same robot's flat-ground recipe and override only `task.env`. Use this progression: ```bash -uv run scripts/view.py robot= -uv run scripts/view.py env=-walk-flat -uv run scripts/view.py env=-walk-rough -uv run scripts/train.py task=-walk-flat/rslrl.ppo +python scripts/view.py robot= +python scripts/view.py env=-walk-flat +python scripts/view.py env=-walk-rough +python scripts/train.py task=-walk-flat/rslrl.ppo ``` During preview, inspect the default pose, action direction, all four foot contacts, foot-position reference frames, and diff --git a/docs/source/en/user_guide/envs/whole_body_tracking/adding_wbt_task.md b/docs/source/en/user_guide/envs/whole_body_tracking/adding_wbt_task.md index 932dc88..ca8c3b5 100644 --- a/docs/source/en/user_guide/envs/whole_body_tracking/adding_wbt_task.md +++ b/docs/source/en/user_guide/envs/whole_body_tracking/adding_wbt_task.md @@ -43,7 +43,7 @@ Built-in WBT uses `ctrl_dt=0.02` s, so convert this motion to 50 FPS. Run kinema entry point: ```bash -uv run scripts/motion/replay.py \ +python scripts/motion/replay.py \ --robot g1-29dof \ --motion motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject1.npz ``` @@ -134,7 +134,7 @@ Task for the same robot first, and tune algorithm hyperparameters only after the Use a small smoke test for Hydra composition, registry creation, tensor shapes, and the training path: ```bash -uv run scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac \ +python scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac \ algo.asynchronous=true num_envs=64 algo.trainer.num_learning_iterations=100 ``` @@ -142,13 +142,13 @@ Check for missing motion, joint, or body names; systematic NaNs, joint-limit vio reset; and verify that `info["Reward"]` and `info["metrics"]` reach the logs. Then start the default training run: ```bash -uv run scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac algo.asynchronous=true ``` After training creates a metadata-backed run, play the policy: ```bash -uv run scripts/play.py env=g1-wbt-dance1-subject1 num_envs=16 +python scripts/play.py env=g1-wbt-dance1-subject1 num_envs=16 ``` Play mode starts at frame 0, disables reset noise and adaptive sampling, and restarts from the beginning at the clip end. A diff --git a/docs/source/en/user_guide/envs/whole_body_tracking/index.md b/docs/source/en/user_guide/envs/whole_body_tracking/index.md index 989cce4..0b4c7f0 100644 --- a/docs/source/en/user_guide/envs/whole_body_tracking/index.md +++ b/docs/source/en/user_guide/envs/whole_body_tracking/index.md @@ -126,7 +126,7 @@ Motion files are managed by Git LFS. If an `.npz` is still pointer text after cl Check a reference motion with the target robot's `RobotCfg`: ```bash -uv run scripts/motion/replay.py \ +python scripts/motion/replay.py \ --robot g1-29dof \ --motion motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject2.npz ``` @@ -142,20 +142,20 @@ Choose an environment ID and one of its training configs from the built-in task `TRAINING_CONFIG`: ```bash -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/train.py task=ENV_ID/TRAINING_CONFIG ``` For example, train the G1 dance-tracking task with asynchronous FastSAC: ```bash -uv run scripts/train.py task=g1-wbt-dance/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=g1-wbt-dance/motrix.fastsac algo.asynchronous=true ``` The built-in configs use 2048 parallel environments and task-specific learning-iteration budgets. Use a small smoke test only to validate registry creation, tensor shapes, and the training entry point; it is not expected to produce a useful policy: ```bash -uv run scripts/train.py task=g1-wbt-dance/motrix.fastsac \ +python scripts/train.py task=g1-wbt-dance/motrix.fastsac \ algo.asynchronous=true num_envs=64 algo.trainer.num_learning_iterations=100 ``` @@ -166,7 +166,7 @@ adaptive-sampling statistics are written to `info["metrics"]`. See [Task Environ ### Play the policy ```bash -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/play.py env=ENV_ID num_envs=16 ``` `play.py` selects the best policy from the latest metadata-backed run for the environment. The WBT play config starts at diff --git a/docs/source/en/user_guide/envs/whole_body_tracking/motion_format.md b/docs/source/en/user_guide/envs/whole_body_tracking/motion_format.md index b099a7f..6c39ed0 100644 --- a/docs/source/en/user_guide/envs/whole_body_tracking/motion_format.md +++ b/docs/source/en/user_guide/envs/whole_body_tracking/motion_format.md @@ -76,7 +76,7 @@ print(motion.body_names) target robot to check the floating-root layout, model joint set, and actual kinematics: ```bash -uv run scripts/motion/replay.py --robot g1-29dof --motion /path/to/motion.npz +python scripts/motion/replay.py --robot g1-29dof --motion /path/to/motion.npz ``` Supported replay `--robot` values are `g1-29dof`, `dex-evt`, and `k1`. @@ -86,13 +86,13 @@ Supported replay `--robot` values are `g1-29dof`, `dex-evt`, and `k1`. List available G1 clips: ```bash -uv run scripts/motion/download_lafan.py --list +python scripts/motion/download_lafan.py --list ``` Download and convert one clip to 50 FPS: ```bash -uv run scripts/motion/download_lafan.py \ +python scripts/motion/download_lafan.py \ --motion dance1_subject1 \ --output motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject1.npz \ --output-fps 50 @@ -101,7 +101,7 @@ uv run scripts/motion/download_lafan.py \ You can also convert an existing G1 CSV: ```bash -uv run scripts/motion/convert.py \ +python scripts/motion/convert.py \ --from lafan \ --input /path/to/dance1_subject1.csv \ --output /path/to/dance1_subject1.npz \ diff --git a/docs/source/en/user_guide/getting_started/hello_motrixlab.md b/docs/source/en/user_guide/getting_started/hello_motrixlab.md index 3816d12..ad71cc9 100644 --- a/docs/source/en/user_guide/getting_started/hello_motrixlab.md +++ b/docs/source/en/user_guide/getting_started/hello_motrixlab.md @@ -2,12 +2,14 @@ This tutorial demonstrates the MotrixLab workflow through a simple example - loading and training a cartpole environment: +Complete [installation](installation.md) first, then activate the environment with `source .venv/bin/activate` (Windows PowerShell: `.venv\Scripts\Activate.ps1`). + ## Environment Preview We provide a simple script to visualize an environment without executing any training. This helps you verify that system dependencies are correctly configured: ```bash -uv run scripts/view.py env=cartpole +python scripts/view.py env=cartpole ``` This will open a visualization window showing the cartpole physics simulation environment with random actions for demonstration. @@ -17,7 +19,7 @@ This will open a visualization window showing the cartpole physics simulation en Start training the cartpole balancing task: ```bash -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo ``` The training process will automatically: @@ -36,7 +38,7 @@ Training results will be saved in the `runs/cartpole/` directory, including: If you want to observe the model's learning process during training, you can enable visualization rendering: ```bash -uv run scripts/train.py task=cartpole/skrl.ppo render=true +python scripts/train.py task=cartpole/skrl.ppo render=true ``` ### 🎮 Interactive Rendering Control @@ -56,7 +58,7 @@ This interactive control allows you to observe training effects when needed and Use TensorBoard to view training progress: ```bash -uv run tensorboard --logdir runs/cartpole +tensorboard --logdir runs/cartpole ``` ## Test Trained Model @@ -65,10 +67,10 @@ After training is complete, test the trained policy: ```bash # Automatically find best policy for testing (recommended) -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole # Manually specify a checkpoint from a metadata-backed run -uv run scripts/play.py env=cartpole policy=/path/to/run/checkpoints/policy-file +python scripts/play.py env=cartpole policy=/path/to/run/checkpoints/policy-file ``` > **Tip**: The system automatically finds the latest run and its best policy under `runs/cartpole/`. A manually selected checkpoint must belong to a run containing `metadata.json` and `task_config.yaml`. diff --git a/docs/source/en/user_guide/getting_started/installation.md b/docs/source/en/user_guide/getting_started/installation.md index b410842..71e87cc 100644 --- a/docs/source/en/user_guide/getting_started/installation.md +++ b/docs/source/en/user_guide/getting_started/installation.md @@ -41,40 +41,58 @@ git clone https://github.com/Motphys/MotrixLab.git cd MotrixLab ``` -### Configure Dependencies +### Install the Runtime Environment -Execute the following command to install complete dependencies: +Install the runtime environment from the repository root (on Windows, run `install.ps1` in PowerShell; if the execution policy blocks it, use `powershell -ExecutionPolicy Bypass -File install.ps1`): ```bash -# Install all dependencies -uv sync --all-packages --all-groups --all-extras +sh install.sh ``` -If you only need specific training frameworks, you can selectively install to reduce dependency size: +This auto-detects your GPU vendor (NVIDIA → CUDA, AMD → ROCm) and installs all workspace packages, +the matching PyTorch wheels, the SKRL training framework, and the built-in FastSAC algorithm. + +### Activate the Environment + +After installing, activate the environment before running commands (Windows PowerShell: +`.venv\Scripts\Activate.ps1`): ```bash +source .venv/bin/activate +``` + +```{note} +Avoid bare `uv sync` / `uv run`: the CUDA and ROCm wheels are selected by mutually exclusive +extras, so a bare command resolves the default PyPI fork and reinstalls the environment. Run +commands from the activated environment, or pass `--no-sync` to one-off `uv run` calls. +``` -# Install SKRL JAX (Linux only) -uv sync --all-packages --extra skrl-jax +## Development Environment -# Install SKRL PyTorch -uv sync --all-packages --extra skrl-torch +The runtime environment only contains what training and deployment need. To contribute to the +project (run tests, modify code, build the docs), install the full development environment: -# Install RSLRL (PyTorch only) -uv sync --all-packages --extra rslrl +```bash +sh install.sh --all ``` -## Package Boundaries +`--all` enables everything in one go: the dev toolchain, test dependencies, all training backends +(`--skrl-jax` / `--rslrl`), and the docs tooling. You can also extend the runtime environment +incrementally, e.g. `sh install.sh --docs` adds only the docs toolchain. -- `motrix-env-core` provides the environment framework without built-in tasks or assets. -- `motrix-envs` depends on the core package and contains all built-in environments, robot models, and task data. -- `motrix-rl` depends on the core package and does not require the built-in environments. +## Option Reference -In an external project, install only `motrix-env-core` when implementing custom environments. Install -`motrix-envs` when using the built-in tasks. Importing `motrix_envs` performs built-in environment registration. +Every mode installs all workspace packages; the runtime environment enables one GPU extra plus the +training backend extra on top of them, and each option only selects or appends to that combination: -```python -from motrix_envs import registry -from motrix_env_core.direct.env import DirectEnv -from motrix_envs.core import EnvCfg, SceneCfg, configclass -``` +| Option | Values | Description | +| ------ | ------ | ----------- | +| `--all` | — | Full development environment: everything below is enabled in one go (dev toolchain, test dependencies, all training backends, and docs tooling) | +| *(none)* | — | Runtime environment; the GPU vendor is auto-detected (NVIDIA → CUDA, AMD → ROCm; CUDA as fallback when detection is impossible) | +| `--gpu` | `cuda`
`rocm` | Select the torch wheel flavor explicitly, overriding auto-detection | +| `--skrl-jax` | — | SKRL on JAX backend, Linux only | +| `--rslrl` | — | RSL-RL on PyTorch backend | +| `--docs` | — | Add the toolchain (sphinx) needed to build the documentation locally | +| `-h`, `--help` | — | Show the help message | + +Run `sh install.sh --help` for the full option reference. diff --git a/docs/source/en/user_guide/robots.md b/docs/source/en/user_guide/robots.md index fe4f952..f3bdddf 100644 --- a/docs/source/en/user_guide/robots.md +++ b/docs/source/en/user_guide/robots.md @@ -137,8 +137,8 @@ zero-argument factory. Construct the config through the registry and preview it before using it in a task: ```bash -uv run scripts/view.py robot=my-robot -uv run pytest motrix_envs/tests/test_robot_cfg.py -q +python scripts/view.py robot=my-robot +python -m pytest motrix_envs/tests/test_robot_cfg.py -q ``` At minimum, verify that the model builds, `base_link_name` exists, joints and actuators correspond, the default key @@ -150,8 +150,8 @@ To include the robot in the generated table above, add its type and screenshot p `docs/scripts/generate_robot_docs.py`, then generate the screenshot and table: ```bash -uv run docs/scripts/generate_robot_docs.py --screenshots my-robot -uv run docs/scripts/generate_robot_docs.py --check +python docs/scripts/generate_robot_docs.py --screenshots my-robot +python docs/scripts/generate_robot_docs.py --check ``` ## Preview a robot @@ -159,7 +159,7 @@ uv run docs/scripts/generate_robot_docs.py --check Use `view.py` to inspect a registered robot in its default pose without creating an RL environment: ```bash -uv run scripts/view.py robot=go2 +python scripts/view.py robot=go2 ``` Robot view mode builds a static standard scene. It does not sample actions or run a physics rollout. diff --git a/docs/source/en/user_guide/tutorial/basic_frame.md b/docs/source/en/user_guide/tutorial/basic_frame.md index dc78d1d..b012ace 100644 --- a/docs/source/en/user_guide/tutorial/basic_frame.md +++ b/docs/source/en/user_guide/tutorial/basic_frame.md @@ -88,7 +88,7 @@ The trainer owns framework-specific model construction, optimization, checkpoint For example: ```bash -uv run scripts/train.py task=cartpole/skrl.ppo num_envs=1024 +python scripts/train.py task=cartpole/skrl.ppo num_envs=1024 ``` This command performs the following steps: @@ -104,8 +104,8 @@ This command performs the following steps: The same environment can have multiple Task recipes without changing its implementation: ```bash -uv run scripts/train.py task=cartpole/skrl.ppo -uv run scripts/train.py task=cartpole/rslrl.ppo +python scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/rslrl.ppo ``` SKRL supports JAX and Torch providers, RSLRL uses Torch, and `motrix.fastsac` selects its synchronous or asynchronous Torch trainer through `algo.asynchronous`. The selected Task and provider determine the algorithm configuration and output metadata. diff --git a/docs/source/en/user_guide/tutorial/custom_training_backend.md b/docs/source/en/user_guide/tutorial/custom_training_backend.md index 1460ee5..86ba3a0 100644 --- a/docs/source/en/user_guide/tutorial/custom_training_backend.md +++ b/docs/source/en/user_guide/tutorial/custom_training_backend.md @@ -192,7 +192,7 @@ The Task's `rllib`, `algo`, and `train_backend` values must match the framework If the backend is registered on the startup path, use the shared CLI: ```bash -uv run scripts/train.py task=cartpole/myrl.ppo +python scripts/train.py task=cartpole/myrl.ppo ``` For an external experiment package, use its Hydra config root and import the backend registration module before entering the training function. No separate Python Task registry is required. diff --git a/docs/source/en/user_guide/tutorial/export_onnx.md b/docs/source/en/user_guide/tutorial/export_onnx.md index fd4bd5f..1a567f8 100644 --- a/docs/source/en/user_guide/tutorial/export_onnx.md +++ b/docs/source/en/user_guide/tutorial/export_onnx.md @@ -18,17 +18,9 @@ SKRL/JAX checkpoints cannot be exported yet. The command accepts a complete run ## Install Dependencies -Install the ONNX dependencies and the backend used for training from the repository root: - -```bash -# SKRL/Torch or Motrix FastSAC -uv sync --all-packages --extra onnx --extra skrl-torch - -# RSL-RL -uv sync --all-packages --extra onnx --extra rslrl -``` - -No additional step is needed if the environment was installed with `uv sync --all-packages --all-groups --all-extras`. +No extra step is needed: the ONNX export and inference dependencies (`onnx`, `onnxruntime`) are part +of the default runtime environment installed by `sh install.sh` (see +[Installation](../getting_started/installation.md)). ## Export the Model @@ -41,7 +33,7 @@ runs/cartpole/skrl/torch/ppo// Export it to an explicit output path: ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir=runs/cartpole/skrl/torch/ppo/ \ output=artifacts/cartpole.onnx ``` @@ -51,7 +43,7 @@ If a path contains spaces, quote the complete Hydra argument, for example `"run_ When `output` is omitted, the model is written next to the best checkpoint as `policy.onnx`: ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir=runs/cartpole/skrl/torch/ppo/ ``` @@ -113,7 +105,7 @@ In most cases, only `run_dir` and `output` are needed. Override these Hydra para For example, export with opset 17 and more validation samples: ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir= \ output=policy.onnx \ opset=17 \ diff --git a/docs/source/en/user_guide/tutorial/motrix_deploy.md b/docs/source/en/user_guide/tutorial/motrix_deploy.md index b12635a..d6e0e20 100644 --- a/docs/source/en/user_guide/tutorial/motrix_deploy.md +++ b/docs/source/en/user_guide/tutorial/motrix_deploy.md @@ -8,7 +8,7 @@ it on a real robot. If you already have a training run, start at “Export the a Run this command from the repository root: ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` This installs the training, MuJoCo, ONNX Runtime, and Unitree SDK2 dependencies. @@ -18,7 +18,7 @@ This installs the training, MuJoCo, ONNX Runtime, and Unitree SDK2 dependencies. If you do not have a run yet, train the flat-terrain Go2 policy: ```bash -uv run scripts/train.py task=go2-walk-flat/rslrl.ppo +python scripts/train.py task=go2-walk-flat/rslrl.ppo ``` Training results are saved under `runs/go2-walk-flat/`. @@ -28,7 +28,7 @@ Training results are saved under `runs/go2-walk-flat/`. Export the latest run: ```bash -uv run scripts/export_deploy.py env=go2-walk-flat +python scripts/export_deploy.py env=go2-walk-flat ``` The output is written to `artifacts/go2-walk-flat.deploy/`. This artifact is the only policy bundle needed for deployment; @@ -37,7 +37,7 @@ it contains the model and its runtime configuration. ## 4. Inspect the artifact ```bash -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-flat.deploy ``` @@ -46,7 +46,7 @@ Make sure the output reports `valid: true`. ## 5. Run Sim2Sim first ```bash -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ --config-name go2_walk_flat_sim2sim \ artifact=artifacts/go2-walk-flat.deploy ``` @@ -60,10 +60,10 @@ Put the robot in low-level/debug mode, connect Ethernet, and keep an emergency s actual network interface: ```bash -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-flat.deploy -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ --config-name go2_walk_flat_sim2real \ artifact=artifacts/go2-walk-flat.deploy \ backend.network_interface=enp5s0 \ @@ -76,7 +76,7 @@ to send motion commands. Press B to enter the lie-down sequence; Select triggers You can also inspect the robot state before sending policy commands: ```bash -uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 +python -m motrix_deploy_unitree.read_lowstate enp5s0 ``` ## Advanced usage @@ -88,7 +88,7 @@ The physical-runtime base configuration, `configs/deploy/sim2real/base.yaml`, cu gains, or pass 12 non-negative values on the command line: ```bash -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ artifact=artifacts/go2-walk-flat.deploy \ backend.network_interface=enp5s0 \ 'backend.kp=[20,25,30,20,25,30,22,27,32,22,27,32]' \ @@ -101,7 +101,7 @@ uv run motrix-deploy sim2real \ Run the read-only diagnostic before sending any motion command: ```bash -uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 +python -m motrix_deploy_unitree.read_lowstate enp5s0 ``` ### Send a single-joint motion command @@ -111,7 +111,7 @@ To bypass the policy and debug a single joint-position motion, use the bounded h does not require a training run, checkpoint, policy, or deployment artifact: ```bash -uv run python -m motrix_deploy_unitree.go2_joint_control \ +python -m motrix_deploy_unitree.go2_joint_control \ enp5s0 \ FL_thigh_joint \ 0.9 \ diff --git a/docs/source/en/user_guide/tutorial/runs_and_checkpoints.md b/docs/source/en/user_guide/tutorial/runs_and_checkpoints.md index 318c327..27d3528 100644 --- a/docs/source/en/user_guide/tutorial/runs_and_checkpoints.md +++ b/docs/source/en/user_guide/tutorial/runs_and_checkpoints.md @@ -106,16 +106,16 @@ The actual checkpoint files live under the `checkpoints/` subdirectory, and `man ```bash # Auto-discover and play the best policy of the latest run - uv run scripts/play.py env=g1-walk-flat + python scripts/play.py env=g1-walk-flat # Specify a checkpoint (must be able to locate metadata.json above it) - uv run scripts/play.py env=g1-walk-flat policy=/path/to/run/checkpoints/latest.pt + python scripts/play.py env=g1-walk-flat policy=/path/to/run/checkpoints/latest.pt ``` - **Resume**: set `resume=` to a run directory or checkpoint path, and the framework resolves `latest_training_state` from it to continue training. ```bash - uv run scripts/train.py task=g1-walk-flat/motrix.fastsac \ + python scripts/train.py task=g1-walk-flat/motrix.fastsac \ resume=/path/to/run ``` @@ -124,5 +124,5 @@ The actual checkpoint files live under the `checkpoints/` subdirectory, and `man TensorBoard logs (`events.out.tfevents.*`) are written directly under the run root and can be viewed per environment: ```bash -uv run tensorboard --logdir runs/g1-walk-flat +tensorboard --logdir runs/g1-walk-flat ``` diff --git a/docs/source/en/user_guide/tutorial/training_and_result.md b/docs/source/en/user_guide/tutorial/training_and_result.md index 9db14e4..35279c3 100644 --- a/docs/source/en/user_guide/tutorial/training_and_result.md +++ b/docs/source/en/user_guide/tutorial/training_and_result.md @@ -10,12 +10,12 @@ The training entry point uses Hydra's `task=/. **提示**：策略会从 `runs/acrobot/` 中自动选择。使用 `policy=...` 时，checkpoint 所属 run 必须包含 `metadata.json`。 diff --git a/docs/source/zh_CN/user_guide/envs/basic/cartpole.md b/docs/source/zh_CN/user_guide/envs/basic/cartpole.md index 46a6993..208cf28 100644 --- a/docs/source/zh_CN/user_guide/envs/basic/cartpole.md +++ b/docs/source/zh_CN/user_guide/envs/basic/cartpole.md @@ -88,25 +88,25 @@ reward = 1.0 # 每步固定奖励 ### 1. 环境预览 ```bash -uv run scripts/view.py env=cartpole +python scripts/view.py env=cartpole ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/cartpole +tensorboard --logdir runs/cartpole ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/basic/pendulum.md b/docs/source/zh_CN/user_guide/envs/basic/pendulum.md index c266e93..758f4ce 100644 --- a/docs/source/zh_CN/user_guide/envs/basic/pendulum.md +++ b/docs/source/zh_CN/user_guide/envs/basic/pendulum.md @@ -64,36 +64,36 @@ ### 1. 环境预览 ```bash -uv run scripts/view.py env=pendulum +python scripts/view.py env=pendulum ``` ### 2. 开始训练 ```bash # 默认参数训练 -uv run scripts/train.py task=pendulum/skrl.ppo +python scripts/train.py task=pendulum/skrl.ppo # 自定义并行环境数 -uv run scripts/train.py task=pendulum/skrl.ppo num_envs=1024 +python scripts/train.py task=pendulum/skrl.ppo num_envs=1024 # 开启训练时渲染 -uv run scripts/train.py task=pendulum/skrl.ppo render=true +python scripts/train.py task=pendulum/skrl.ppo render=true ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/pendulum +tensorboard --logdir runs/pendulum ``` ### 4. 测试训练结果 ```bash # 自动寻找最新/最优策略（推荐） -uv run scripts/play.py env=pendulum +python scripts/play.py env=pendulum # 手动指定带 metadata 的 run 中的 checkpoint -uv run scripts/play.py env=pendulum policy=/path/to/run/checkpoints/policy-file +python scripts/play.py env=pendulum policy=/path/to/run/checkpoints/policy-file ``` > **提示**：策略默认在 `runs/pendulum/` 下自动发现。使用 `policy=...` 时，checkpoint 所属 run 必须包含 `metadata.json`。 diff --git a/docs/source/zh_CN/user_guide/envs/basic/stewart.md b/docs/source/zh_CN/user_guide/envs/basic/stewart.md index 6f01d05..5997fa1 100644 --- a/docs/source/zh_CN/user_guide/envs/basic/stewart.md +++ b/docs/source/zh_CN/user_guide/envs/basic/stewart.md @@ -156,15 +156,15 @@ reward = fall_penalty if fallen else reward ### 1. 环境预览 ```bash -uv run scripts/view.py env=stewart-static -uv run scripts/view.py env=stewart-disturb-xy +python scripts/view.py env=stewart-static +python scripts/view.py env=stewart-disturb-xy ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=stewart-static/skrl.ppo -uv run scripts/train.py task=stewart-disturb-xy/skrl.ppo +python scripts/train.py task=stewart-static/skrl.ppo +python scripts/train.py task=stewart-disturb-xy/skrl.ppo ``` ### 训练说明 @@ -176,15 +176,15 @@ uv run scripts/train.py task=stewart-disturb-xy/skrl.ppo ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/stewart-static -uv run tensorboard --logdir runs/stewart-disturb-xy +tensorboard --logdir runs/stewart-static +tensorboard --logdir runs/stewart-disturb-xy ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=stewart-static -uv run scripts/play.py env=stewart-disturb-xy +python scripts/play.py env=stewart-static +python scripts/play.py env=stewart-disturb-xy ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/bring_ball.md b/docs/source/zh_CN/user_guide/envs/dm_control/bring_ball.md index 0cff426..b29d2bc 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/bring_ball.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/bring_ball.md @@ -137,7 +137,7 @@ penalty_side + penalty_hover ### 1. 环境预览（随机动作） ```bash -uv run scripts/view.py env=dm-manipulator-bring-ball +python scripts/view.py env=dm-manipulator-bring-ball ``` ### 2. 开始训练 @@ -145,19 +145,19 @@ uv run scripts/view.py env=dm-manipulator-bring-ball 建议显式指定训练后端（JAX / PyTorch 二选一）： ```bash -uv run scripts/train.py task=dm-manipulator-bring-ball/skrl.ppo.torch +python scripts/train.py task=dm-manipulator-bring-ball/skrl.ppo.torch ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-manipulator-bring-ball +tensorboard --logdir runs/dm-manipulator-bring-ball ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-manipulator-bring-ball +python scripts/play.py env=dm-manipulator-bring-ball ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_cheetah.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_cheetah.md index fcc358e..9589284 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_cheetah.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_cheetah.md @@ -102,25 +102,25 @@ cheetah 的奖励函数由以下几个部分组成： ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-cheetah +python scripts/view.py env=dm-cheetah ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-cheetah/skrl.ppo +python scripts/train.py task=dm-cheetah/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-cheetah +tensorboard --logdir runs/dm-cheetah ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-cheetah +python scripts/play.py env=dm-cheetah ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_finger.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_finger.md index a8ad222..d2242d7 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_finger.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_finger.md @@ -143,15 +143,15 @@ Turn 的核心是 tip 触达并对准目标点：目标点位于 spinner 周围 ### 1. 环境预览（随机动作） ```bash -uv run scripts/view.py env=dm-finger-spin +python scripts/view.py env=dm-finger-spin ``` ```bash -uv run scripts/view.py env=dm-finger-turn-easy +python scripts/view.py env=dm-finger-turn-easy ``` ```bash -uv run scripts/view.py env=dm-finger-turn-hard +python scripts/view.py env=dm-finger-turn-hard ``` ### 2. 开始训练 @@ -159,21 +159,21 @@ uv run scripts/view.py env=dm-finger-turn-hard 建议显式指定训练后端（JAX / PyTorch 二选一）： ```bash -uv run scripts/train.py task=dm-finger-spin/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-spin/skrl.ppo task.train_backend=torch ``` ```bash -uv run scripts/train.py task=dm-finger-turn-easy/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-turn-easy/skrl.ppo task.train_backend=torch ``` ```bash -uv run scripts/train.py task=dm-finger-turn-hard/skrl.ppo task.train_backend=torch +python scripts/train.py task=dm-finger-turn-hard/skrl.ppo task.train_backend=torch ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-finger-spin +tensorboard --logdir runs/dm-finger-spin ``` ### 4. 测试训练结果 @@ -181,7 +181,7 @@ uv run tensorboard --logdir runs/dm-finger-spin `scripts/play.py` 默认会在 `runs/{env-name}/` 下查找最新的 metadata-backed run，并加载其 `best_policy` artifact；也可以用 `policy=...` 显式指定 checkpoint： ```bash -uv run scripts/play.py env=dm-finger-turn-hard +python scripts/play.py env=dm-finger-turn-hard ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_hopper.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_hopper.md index 257dae5..c91a80b 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_hopper.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_hopper.md @@ -109,28 +109,28 @@ hopper 的奖励函数由以下几个部分组成： ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-hopper-stand -uv run scripts/view.py env=dm-hopper-hop +python scripts/view.py env=dm-hopper-stand +python scripts/view.py env=dm-hopper-hop ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-hopper-stand/skrl.ppo -uv run scripts/train.py task=dm-hopper-hop/skrl.ppo +python scripts/train.py task=dm-hopper-stand/skrl.ppo +python scripts/train.py task=dm-hopper-hop/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-hopper-hop +tensorboard --logdir runs/dm-hopper-hop ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-hopper-stand -uv run scripts/play.py env=dm-hopper-hop +python scripts/play.py env=dm-hopper-stand +python scripts/play.py env=dm-hopper-hop ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_humanoid.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_humanoid.md index 72fb188..6c271db 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_humanoid.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_humanoid.md @@ -185,31 +185,31 @@ total_reward = posture_reward * speed_reward * energy_reward * gait_reward ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-humanoid-stand -uv run scripts/view.py env=dm-humanoid-walk -uv run scripts/view.py env=dm-humanoid-run +python scripts/view.py env=dm-humanoid-stand +python scripts/view.py env=dm-humanoid-walk +python scripts/view.py env=dm-humanoid-run ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-humanoid-stand/skrl.ppo -uv run scripts/train.py task=dm-humanoid-walk/skrl.ppo -uv run scripts/train.py task=dm-humanoid-run/skrl.ppo +python scripts/train.py task=dm-humanoid-stand/skrl.ppo +python scripts/train.py task=dm-humanoid-walk/skrl.ppo +python scripts/train.py task=dm-humanoid-run/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-humanoid-walk +tensorboard --logdir runs/dm-humanoid-walk ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-humanoid-stand -uv run scripts/play.py env=dm-humanoid-walk -uv run scripts/play.py env=dm-humanoid-run +python scripts/play.py env=dm-humanoid-stand +python scripts/play.py env=dm-humanoid-walk +python scripts/play.py env=dm-humanoid-run ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_lqr.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_lqr.md index 5dfaee0..9b9c686 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_lqr.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_lqr.md @@ -157,29 +157,29 @@ reward -= out_of_bounds_penalty ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-lqr-2-1 -uv run scripts/view.py env=dm-lqr-6-2 +python scripts/view.py env=dm-lqr-2-1 +python scripts/view.py env=dm-lqr-6-2 ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-lqr-2-1/skrl.ppo -uv run scripts/train.py task=dm-lqr-6-2/skrl.ppo +python scripts/train.py task=dm-lqr-2-1/skrl.ppo +python scripts/train.py task=dm-lqr-6-2/skrl.ppo ``` ### 3. 查看训练曲线 ```bash -uv run tensorboard --logdir runs/dm-lqr-2-1 -uv run tensorboard --logdir runs/dm-lqr-6-2 +tensorboard --logdir runs/dm-lqr-2-1 +tensorboard --logdir runs/dm-lqr-6-2 ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-lqr-2-1 -uv run scripts/play.py env=dm-lqr-6-2 +python scripts/play.py env=dm-lqr-2-1 +python scripts/play.py env=dm-lqr-6-2 ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_point_mass.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_point_mass.md index 79ec8b2..dad63f7 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_point_mass.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_point_mass.md @@ -115,25 +115,25 @@ rwd = distance_reward + target_bonus + continuous_reward + path_reward - center_ ### 1. 环境预览 ```bash -uv run scripts/view.py env=point_mass +python scripts/view.py env=point_mass ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=point_mass/skrl.ppo +python scripts/train.py task=point_mass/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/point_mass +tensorboard --logdir runs/point_mass ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=point_mass +python scripts/play.py env=point_mass ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_quadruped.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_quadruped.md index 6098b20..ac00e0d 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_quadruped.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_quadruped.md @@ -228,37 +228,37 @@ total_reward = stage_terms + ready_terms + push_terms - penalty_terms ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-quadruped-walk -uv run scripts/view.py env=dm-quadruped-run -uv run scripts/view.py env=dm-quadruped-escape -uv run scripts/view.py env=dm-quadruped-fetch +python scripts/view.py env=dm-quadruped-walk +python scripts/view.py env=dm-quadruped-run +python scripts/view.py env=dm-quadruped-escape +python scripts/view.py env=dm-quadruped-fetch ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-quadruped-walk/skrl.ppo -uv run scripts/train.py task=dm-quadruped-run/skrl.ppo -uv run scripts/train.py task=dm-quadruped-escape/skrl.ppo -uv run scripts/train.py task=dm-quadruped-fetch/skrl.ppo +python scripts/train.py task=dm-quadruped-walk/skrl.ppo +python scripts/train.py task=dm-quadruped-run/skrl.ppo +python scripts/train.py task=dm-quadruped-escape/skrl.ppo +python scripts/train.py task=dm-quadruped-fetch/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-quadruped-walk -uv run tensorboard --logdir runs/dm-quadruped-run -uv run tensorboard --logdir runs/dm-quadruped-escape -uv run tensorboard --logdir runs/dm-quadruped-fetch +tensorboard --logdir runs/dm-quadruped-walk +tensorboard --logdir runs/dm-quadruped-run +tensorboard --logdir runs/dm-quadruped-escape +tensorboard --logdir runs/dm-quadruped-fetch ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-quadruped-walk -uv run scripts/play.py env=dm-quadruped-run -uv run scripts/play.py env=dm-quadruped-escape -uv run scripts/play.py env=dm-quadruped-fetch +python scripts/play.py env=dm-quadruped-walk +python scripts/play.py env=dm-quadruped-run +python scripts/play.py env=dm-quadruped-escape +python scripts/play.py env=dm-quadruped-fetch ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_reacher.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_reacher.md index 2d07ff4..b222485 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_reacher.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_reacher.md @@ -93,25 +93,25 @@ reward = tolerance(|| fingertip - target ||) ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-reacher +python scripts/view.py env=dm-reacher ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-reacher/skrl.ppo +python scripts/train.py task=dm-reacher/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-reacher +tensorboard --logdir runs/dm-reacher ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-reacher +python scripts/play.py env=dm-reacher ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/dm_control/dm_walker.md b/docs/source/zh_CN/user_guide/envs/dm_control/dm_walker.md index 7ebd4a3..c96bcd4 100644 --- a/docs/source/zh_CN/user_guide/envs/dm_control/dm_walker.md +++ b/docs/source/zh_CN/user_guide/envs/dm_control/dm_walker.md @@ -94,31 +94,31 @@ walker 的奖励函数由以下几个部分组成： ### 1. 环境预览 ```bash -uv run scripts/view.py env=dm-stander -uv run scripts/view.py env=dm-walker -uv run scripts/view.py env=dm-runner +python scripts/view.py env=dm-stander +python scripts/view.py env=dm-walker +python scripts/view.py env=dm-runner ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=dm-stander/skrl.ppo -uv run scripts/train.py task=dm-walker/skrl.ppo -uv run scripts/train.py task=dm-runner/skrl.ppo +python scripts/train.py task=dm-stander/skrl.ppo +python scripts/train.py task=dm-walker/skrl.ppo +python scripts/train.py task=dm-runner/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/dm-walker +tensorboard --logdir runs/dm-walker ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=dm-stander -uv run scripts/play.py env=dm-walker -uv run scripts/play.py env=dm-runner +python scripts/play.py env=dm-stander +python scripts/play.py env=dm-walker +python scripts/play.py env=dm-runner ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking.md b/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking.md index ea8b756..9232cc3 100644 --- a/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking.md +++ b/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking.md @@ -90,15 +90,15 @@ MotrixLab 已为下列机器人提供平地和程序化起伏高度场配置。 从“内置机器人”表格中选择 Env ID 和对应的训练配置，并替换下列命令中的 `ENV_ID` 与 `TRAINING_CONFIG`： ```bash -uv run scripts/view.py env=ENV_ID num_envs=1 -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/view.py env=ENV_ID num_envs=1 +python scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/play.py env=ENV_ID num_envs=16 ``` 例如，使用 FastSAC 异步训练 K1 起伏地形任务： ```bash -uv run scripts/train.py task=k1-walk-rough/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=k1-walk-rough/motrix.fastsac algo.asynchronous=true ``` `view.py` 使用随机动作，仅用于检查场景和模型；训练后的步态需要通过 `play.py` 回放策略查看。 diff --git a/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking/adding_robot.md b/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking/adding_robot.md index 1616bc4..e0ffcf8 100644 --- a/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking/adding_robot.md +++ b/docs/source/zh_CN/user_guide/envs/humanoid_velocity_tracking/adding_robot.md @@ -114,7 +114,7 @@ asset = AssetCfg( 建议先运行机器人预览命令检查关节顺序、默认姿态、PD 参数、脚底 site 和碰撞体名称： ```bash -uv run scripts/view.py robot= +python scripts/view.py robot= ``` ## 4. 注册环境实现 @@ -156,13 +156,13 @@ algo: 建议按以下顺序验证： ```bash -uv run scripts/view.py robot= -uv run scripts/view.py env=-walk-flat -uv run scripts/view.py env=-walk-terrain -uv run scripts/train.py task=-walk-flat/motrix.fastsac -uv run scripts/train.py task=-walk-flat/motrix.fastsac algo.asynchronous=false -uv run pytest motrix_envs/tests/test_humanoid_walk.py -q -uv run pytest motrix_rl/tests/test_task_configs.py -q +python scripts/view.py robot= +python scripts/view.py env=-walk-flat +python scripts/view.py env=-walk-terrain +python scripts/train.py task=-walk-flat/motrix.fastsac +python scripts/train.py task=-walk-flat/motrix.fastsac algo.asynchronous=false +python -m pytest motrix_envs/tests/test_humanoid_walk.py -q +python -m pytest motrix_rl/tests/test_task_configs.py -q ``` 预览阶段重点检查默认姿态、脚底高度、地面碰撞、动作方向和起伏地形出生位置。新增内置环境配置时，还应在共享人形环境 diff --git a/docs/source/zh_CN/user_guide/envs/index.md b/docs/source/zh_CN/user_guide/envs/index.md index efa1f82..67662f9 100644 --- a/docs/source/zh_CN/user_guide/envs/index.md +++ b/docs/source/zh_CN/user_guide/envs/index.md @@ -5,7 +5,7 @@ MotrixLab Environment 定义可通过 Env Registry 创建的仿真任务，包 使用以下命令可以在不启动训练的情况下预览一个环境： ```bash -uv run scripts/view.py env= +python scripts/view.py env= ``` ## 环境主题 diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/bounce_ball.md b/docs/source/zh_CN/user_guide/envs/manipulation/bounce_ball.md index b53e7f5..fe1ff00 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/bounce_ball.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/bounce_ball.md @@ -411,25 +411,25 @@ $$ ### 1. 环境预览 ```bash -uv run scripts/view.py env=bounce_ball +python scripts/view.py env=bounce_ball ``` ### 2. 开始训练 ```bash -uv run scripts/train.py task=bounce_ball/skrl.ppo +python scripts/train.py task=bounce_ball/skrl.ppo ``` ### 3. 查看训练进度 ```bash -uv run tensorboard --logdir runs/bounce_ball +tensorboard --logdir runs/bounce_ball ``` ### 4. 测试训练结果 ```bash -uv run scripts/play.py env=bounce_ball +python scripts/play.py env=bounce_ball ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/franka_lift_cube.md b/docs/source/zh_CN/user_guide/envs/manipulation/franka_lift_cube.md index 665bba0..c44e60f 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/franka_lift_cube.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/franka_lift_cube.md @@ -239,17 +239,17 @@ p = 1 / (1 + exp(-action)) ### 训练 ```bash -uv run scripts/train.py task=franka-lift-cube/skrl.ppo +python scripts/train.py task=franka-lift-cube/skrl.ppo ``` ### 策略评估 ```bash -uv run scripts/play.py env=franka-lift-cube +python scripts/play.py env=franka-lift-cube ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/franka-lift-cube +tensorboard --logdir runs/franka-lift-cube ``` diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/franka_open_cabinet.md b/docs/source/zh_CN/user_guide/envs/manipulation/franka_open_cabinet.md index 3689d6a..125cd22 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/franka_open_cabinet.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/franka_open_cabinet.md @@ -234,17 +234,17 @@ Franka Emika Panda 是一个 7 自由度机械臂，由以下主要部分组成 ### 训练 ```bash -uv run scripts/train.py task=franka-open-cabinet/skrl.ppo +python scripts/train.py task=franka-open-cabinet/skrl.ppo ``` ### 策略评估 ```bash -uv run scripts/play.py env=franka-open-cabinet +python scripts/play.py env=franka-open-cabinet ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/franka-open-cabinet +tensorboard --logdir runs/franka-open-cabinet ``` diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/rm65_insert_peg.md b/docs/source/zh_CN/user_guide/envs/manipulation/rm65_insert_peg.md index 0e61bb2..f704078 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/rm65_insert_peg.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/rm65_insert_peg.md @@ -269,17 +269,17 @@ RM65 机器人在该环境中包含以下主要部分： ### 训练 ```bash -uv run scripts/train.py task=rm65_insert_peg/motrix.fastsac +python scripts/train.py task=rm65_insert_peg/motrix.fastsac ``` ### 策略评估 ```bash -uv run scripts/play.py env=rm65_insert_peg num_envs=16 +python scripts/play.py env=rm65_insert_peg num_envs=16 ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/rm65_insert_peg/fastsac +tensorboard --logdir runs/rm65_insert_peg/fastsac ``` diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/rm65_open_cabinet.md b/docs/source/zh_CN/user_guide/envs/manipulation/rm65_open_cabinet.md index 2cd7e4f..a3d281f 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/rm65_open_cabinet.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/rm65_open_cabinet.md @@ -242,17 +242,17 @@ RM65 机器人在该环境中包含以下主要部分： ### 训练 ```bash -uv run scripts/train.py task=rm65-open-cabinet/skrl.ppo task.train_backend=torch +python scripts/train.py task=rm65-open-cabinet/skrl.ppo task.train_backend=torch ``` ### 策略评估 ```bash -uv run scripts/play.py env=rm65-open-cabinet +python scripts/play.py env=rm65-open-cabinet ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/rm65_open_cabinet +tensorboard --logdir runs/rm65_open_cabinet ``` diff --git a/docs/source/zh_CN/user_guide/envs/manipulation/shadow_hand_repose.md b/docs/source/zh_CN/user_guide/envs/manipulation/shadow_hand_repose.md index 1919a23..51b3f03 100644 --- a/docs/source/zh_CN/user_guide/envs/manipulation/shadow_hand_repose.md +++ b/docs/source/zh_CN/user_guide/envs/manipulation/shadow_hand_repose.md @@ -297,25 +297,25 @@ actuator_ctrls = targets ### 训练 ```bash -uv run scripts/train.py task=shadow-hand-repose/skrl.ppo +python scripts/train.py task=shadow-hand-repose/skrl.ppo ``` ### 策略评估 ```bash -uv run scripts/play.py env=shadow-hand-repose +python scripts/play.py env=shadow-hand-repose ``` ### 环境可视化 ```bash -uv run scripts/view.py env=shadow-hand-repose +python scripts/view.py env=shadow-hand-repose ``` ### TensorBoard ```bash -uv run tensorboard --logdir runs/shadow-hand-repose +tensorboard --logdir runs/shadow-hand-repose ``` --- diff --git a/docs/source/zh_CN/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md b/docs/source/zh_CN/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md index 4fe7e15..866e578 100644 --- a/docs/source/zh_CN/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md +++ b/docs/source/zh_CN/user_guide/envs/quadruped_locomotion/go1_rough_terrain.md @@ -24,7 +24,7 @@ $v_y$ 和 `yaw_rate` 固定为 0。 该任务不随机化物理参数。 ```bash -uv run scripts/view.py env=go1-stairs-terrain-walk -uv run scripts/train.py task=go1-stairs-terrain-walk/skrl.ppo -uv run scripts/play.py env=go1-stairs-terrain-walk +python scripts/view.py env=go1-stairs-terrain-walk +python scripts/train.py task=go1-stairs-terrain-walk/skrl.ppo +python scripts/play.py env=go1-stairs-terrain-walk ``` diff --git a/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking.md b/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking.md index 840b32f..b09bd33 100644 --- a/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking.md +++ b/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking.md @@ -110,15 +110,15 @@ MotrixLab 已为下列机器人提供平地和程序化粗糙高度场配置。 从“内置机器人”表格中选择 Env ID 和对应的训练配置，并替换下列命令中的 `ENV_ID` 与 `TRAINING_CONFIG`： ```bash -uv run scripts/view.py env=ENV_ID num_envs=1 -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/view.py env=ENV_ID num_envs=1 +python scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/play.py env=ENV_ID num_envs=16 ``` 例如，使用 RSL-RL PPO 训练 Go2 粗糙地形任务： ```bash -uv run scripts/train.py task=go2-walk-rough/rslrl.ppo +python scripts/train.py task=go2-walk-rough/rslrl.ppo ``` `view.py` 使用随机动作，仅用于检查场景和模型；训练后的步态需要通过 `play.py` 回放策略查看。 diff --git a/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking/adding_robot.md b/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking/adding_robot.md index fb0b3fc..0be7cb0 100644 --- a/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking/adding_robot.md +++ b/docs/source/zh_CN/user_guide/envs/quadruped_velocity_tracking/adding_robot.md @@ -67,7 +67,7 @@ class MyQuadruped(QuadrupedRobotCfg): 完成 robot registry 注册后，先独立预览模型： ```bash -uv run scripts/view.py robot= +python scripts/view.py robot= ``` ## 2. 提供任务传感器 @@ -191,10 +191,10 @@ SKRL 配置选择 `/algo_base@algo: skrl.ppo`，并将 `task.rllib` 设为 `skrl 建议按以下顺序验证： ```bash -uv run scripts/view.py robot= -uv run scripts/view.py env=-walk-flat -uv run scripts/view.py env=-walk-rough -uv run scripts/train.py task=-walk-flat/rslrl.ppo +python scripts/view.py robot= +python scripts/view.py env=-walk-flat +python scripts/view.py env=-walk-rough +python scripts/train.py task=-walk-flat/rslrl.ppo ``` 预览时重点检查默认姿态、动作方向、四脚接触、足端位置参考系和粗糙地形出生高度。 diff --git a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/adding_wbt_task.md b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/adding_wbt_task.md index 14caf08..e7e3661 100644 --- a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/adding_wbt_task.md +++ b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/adding_wbt_task.md @@ -41,7 +41,7 @@ motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject1.npz 内置 WBT 的 `ctrl_dt` 为 0.02 s，因此该 motion 应转换为 50 FPS。注册训练入口前先进行运动学 replay： ```bash -uv run scripts/motion/replay.py \ +python scripts/motion/replay.py \ --robot g1-29dof \ --motion motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject1.npz ``` @@ -131,7 +131,7 @@ algo: 先用小规模 smoke test 验证 Hydra composition、registry、tensor shape 和一步训练流程： ```bash -uv run scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac \ +python scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac \ algo.asynchronous=true num_envs=64 algo.trainer.num_learning_iterations=100 ``` @@ -139,13 +139,13 @@ uv run scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac \ `info["Reward"]` 与 `info["metrics"]` 能进入日志。随后使用默认规模训练： ```bash -uv run scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=g1-wbt-dance1-subject1/motrix.fastsac algo.asynchronous=true ``` 生成 metadata-backed run 后回放策略： ```bash -uv run scripts/play.py env=g1-wbt-dance1-subject1 num_envs=16 +python scripts/play.py env=g1-wbt-dance1-subject1 num_envs=16 ``` Play 模式自动从第 0 帧开始，关闭 reset noise 和 adaptive sampler，并在 clip 结束后从头重播，无需单独注册 play 环境。 diff --git a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/index.md b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/index.md index d1dfcb5..a55bef7 100644 --- a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/index.md +++ b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/index.md @@ -122,7 +122,7 @@ Motion 文件由 Git LFS 管理。克隆后若 `.npz` 仍是 pointer 文本， 先用目标机器人的 `RobotCfg` 检查参考动作： ```bash -uv run scripts/motion/replay.py \ +python scripts/motion/replay.py \ --robot g1-29dof \ --motion motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject2.npz ``` @@ -136,20 +136,20 @@ uv run scripts/motion/replay.py \ 从“内置任务”表格中选择 Env ID 和对应的训练配置，并替换下列命令中的 `ENV_ID` 与 `TRAINING_CONFIG`： ```bash -uv run scripts/train.py task=ENV_ID/TRAINING_CONFIG +python scripts/train.py task=ENV_ID/TRAINING_CONFIG ``` 例如，使用 FastSAC 异步训练 G1 舞蹈跟踪任务： ```bash -uv run scripts/train.py task=g1-wbt-dance/motrix.fastsac algo.asynchronous=true +python scripts/train.py task=g1-wbt-dance/motrix.fastsac algo.asynchronous=true ``` 内置配置默认使用 2048 个并行环境，并按任务设置学习迭代次数。只验证 registry、shape 和训练入口时，可以使用不会 生成可用策略的小规模 smoke test： ```bash -uv run scripts/train.py task=g1-wbt-dance/motrix.fastsac \ +python scripts/train.py task=g1-wbt-dance/motrix.fastsac \ algo.asynchronous=true num_envs=64 algo.trainer.num_learning_iterations=100 ``` @@ -160,7 +160,7 @@ uv run scripts/train.py task=g1-wbt-dance/motrix.fastsac \ ### 回放策略 ```bash -uv run scripts/play.py env=ENV_ID num_envs=16 +python scripts/play.py env=ENV_ID num_envs=16 ``` `play.py` 自动选择该环境最新一次 metadata-backed run 的最佳策略。WBT 的 play 配置从 motion 第 0 帧开始，关闭 diff --git a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/motion_format.md b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/motion_format.md index e8124c1..2e6a278 100644 --- a/docs/source/zh_CN/user_guide/envs/whole_body_tracking/motion_format.md +++ b/docs/source/zh_CN/user_guide/envs/whole_body_tracking/motion_format.md @@ -74,7 +74,7 @@ print(motion.body_names) 模型 joint 集合和实际运动学效果： ```bash -uv run scripts/motion/replay.py --robot g1-29dof --motion /path/to/motion.npz +python scripts/motion/replay.py --robot g1-29dof --motion /path/to/motion.npz ``` Replay 支持的 `--robot` 值为 `g1-29dof`、`dex-evt` 和 `k1`。 @@ -84,13 +84,13 @@ Replay 支持的 `--robot` 值为 `g1-29dof`、`dex-evt` 和 `k1`。 查看可用的 G1 clip： ```bash -uv run scripts/motion/download_lafan.py --list +python scripts/motion/download_lafan.py --list ``` 下载并转换到 50 FPS： ```bash -uv run scripts/motion/download_lafan.py \ +python scripts/motion/download_lafan.py \ --motion dance1_subject1 \ --output motrix_envs/src/motrix_envs/locomotion/wbt/assets/motion/g1/dance1_subject1.npz \ --output-fps 50 @@ -99,7 +99,7 @@ uv run scripts/motion/download_lafan.py \ 也可以转换已经下载的 G1 CSV： ```bash -uv run scripts/motion/convert.py \ +python scripts/motion/convert.py \ --from lafan \ --input /path/to/dance1_subject1.csv \ --output /path/to/dance1_subject1.npz \ diff --git a/docs/source/zh_CN/user_guide/getting_started/hello_motrixlab.md b/docs/source/zh_CN/user_guide/getting_started/hello_motrixlab.md index 9c1ca60..7760d88 100644 --- a/docs/source/zh_CN/user_guide/getting_started/hello_motrixlab.md +++ b/docs/source/zh_CN/user_guide/getting_started/hello_motrixlab.md @@ -2,12 +2,14 @@ 本教程通过演示一个简单例子 - 加载倒立摆并进行训练，以此来展示 MotrixLab 工作流程： +请先完成[环境安装](installation.md)，然后激活环境：`source .venv/bin/activate`（Windows PowerShell：`.venv\Scripts\Activate.ps1`）。 + ## 环境预览 我们提供了一个简单的脚本，用于可视化一个环境，而不执行任何训练，这可以帮助您检测系统的环境依赖是否配置正确： ```bash -uv run scripts/view.py env=cartpole +python scripts/view.py env=cartpole ``` 这将打开一个可视化窗口，显示倒立摆的物理仿真环境，使用随机动作进行演示。 @@ -17,7 +19,7 @@ uv run scripts/view.py env=cartpole 开始训练倒立摆平衡任务： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo ``` 训练过程会自动： @@ -36,7 +38,7 @@ uv run scripts/train.py task=cartpole/skrl.ppo 如果您想要在训练过程中观察模型的学习过程，可以启用可视化渲染： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo render=true +python scripts/train.py task=cartpole/skrl.ppo render=true ``` ### 🎮 交互式渲染控制 @@ -56,7 +58,7 @@ uv run scripts/train.py task=cartpole/skrl.ppo render=true 使用 TensorBoard 查看训练进度： ```bash -uv run tensorboard --logdir runs/cartpole +tensorboard --logdir runs/cartpole ``` ## 测试训练好的模型 @@ -65,10 +67,10 @@ uv run tensorboard --logdir runs/cartpole ```bash # 自动寻找最佳策略测试（推荐） -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole # 手动指定带 metadata 的 run 中的 checkpoint -uv run scripts/play.py env=cartpole policy=/path/to/run/checkpoints/policy-file +python scripts/play.py env=cartpole policy=/path/to/run/checkpoints/policy-file ``` > **提示**：系统会自动在 `runs/cartpole/` 下寻找最新 run 的最佳策略。手动指定的 checkpoint 必须属于包含 `metadata.json` 和 `task_config.yaml` 的 run。 diff --git a/docs/source/zh_CN/user_guide/getting_started/installation.md b/docs/source/zh_CN/user_guide/getting_started/installation.md index f6dd1c9..d94759c 100644 --- a/docs/source/zh_CN/user_guide/getting_started/installation.md +++ b/docs/source/zh_CN/user_guide/getting_started/installation.md @@ -41,70 +41,80 @@ git clone https://github.com/Motphys/MotrixLab.git cd MotrixLab ``` -### 配置依赖环境 +### 安装运行环境 + +在仓库根目录执行以下命令安装运行环境（Windows 上请在 PowerShell 中执行 `install.ps1`；若执行策略受限，改用 `powershell -ExecutionPolicy Bypass -File install.ps1`）： + +```bash +sh install.sh +``` + +该命令会自动探测 GPU 厂商（NVIDIA → CUDA，AMD → ROCm），安装全部 workspace package、对应的 +PyTorch wheel、SKRL 训练框架以及内置的 FastSAC 算法。 :::{dropdown} 配置国内镜像源（可选） :animate: fade-in :color: warning :icon: desktop-download -如果您身处中国大陆，建议配置国内镜像源以加速依赖下载： +如果您身处中国大陆，建议配置国内镜像源以加速依赖下载。修改项目根目录的 `uv.toml` 文件： -1. 修改项目根目录的 `uv.toml` 文件 +```toml +[[index]] +name = "mirror" +# 请填写您选择的国内镜像源，例如： +# 清华源: "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" +url = "" - ```toml - [[index]] - name = "mirror" - # 请填写您选择的国内镜像源，例如： - # 清华源: "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" - url = "" +[[index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu128" +default = true +``` - [[index]] - name = "pytorch" - url = "https://download.pytorch.org/whl/cu128" - default = true - ``` - -2. 在执行 `uv sync` 命令时添加 `--index-strategy unsafe-best-match` 参数： - - ``` - uv sync --all-packages --all-groups --all-extras --index-strategy unsafe-best-match - ``` +然后重新执行 `sh install.sh` 即可生效。 ::: -执行以下命令安装完整依赖： +### 激活环境 + +安装完成后，先激活环境再运行命令（Windows PowerShell：`.venv\Scripts\Activate.ps1`）： ```bash -# 安装所有依赖 -uv sync --all-packages --all-groups --all-extras +source .venv/bin/activate ``` -如果仅需特定训练框架，可选择性安装以减少依赖体积： - -```bash +```{note} +避免裸 `uv sync` / `uv run`：CUDA 与 ROCm wheel 由互斥的 extras 选择，裸命令会解析到默认的 PyPI +分支并重装环境。请从激活后的环境运行命令，或对单条命令使用 `uv run --no-sync`。 +``` -# 安装 SKRL JAX （仅支持 Linux 平台） -uv sync --all-packages --extra skrl-jax +## 开发环境安装 -# 安装 SKRL PyTorch -uv sync --all-packages --extra skrl-torch +上述运行环境只包含训练与部署所需的依赖。如果你要参与开发（运行测试、修改代码、构建文档）， +执行以下命令安装完整开发环境： -# 安装 RSLRL（仅支持 PyTorch） -uv sync --all-packages --extra rslrl +```bash +sh install.sh --all ``` -## Package 边界 +`--all` 会把所有依赖一次性全部启用：开发工具链、测试依赖、全部训练后端 +（`--skrl-jax` / `--rslrl`）与文档工具。也可以在运行环境基础上按需追加， +例如 `sh install.sh --docs` 只补文档工具链。 -- `motrix-env-core` 提供环境 framework，不包含内置任务和资产。 -- `motrix-envs` 依赖 core package，包含所有内置环境、机器人模型和任务数据。 -- `motrix-rl` 仅依赖 core package，不强制安装内置环境。 +## 安装参数参考 -外部项目实现自定义环境时可以只安装 `motrix-env-core`；使用内置任务时安装 `motrix-envs`。导入 -`motrix_envs` 时会完成内置环境注册。 +所有模式都会安装全部 workspace package；运行环境在此基础上启用「1 个 GPU extra ＋ 训练后端 extra」， +各参数只负责选择或追加： -```python -from motrix_envs import registry -from motrix_env_core.direct.env import DirectEnv -from motrix_envs.core import EnvCfg, SceneCfg, configclass -``` +| 参数 | 可选值 | 说明 | +| ---- | ------ | ---- | +| `--all` | — | 完整开发环境：把下述所有依赖一次性全部启用（开发工具链、测试依赖、全部训练后端与文档工具） | +| （无参数） | — | 运行环境；自动探测 GPU 厂商（NVIDIA → CUDA，AMD → ROCm，无法探测时回退 CUDA） | +| `--gpu` | `cuda`
`rocm` | 指定 torch wheel 来源，覆盖自动探测 | +| `--skrl-jax` | — | SKRL（JAX）训练后端，仅 Linux | +| `--rslrl` | — | RSL-RL（PyTorch）训练后端 | +| `--docs` | — | 追加本地构建文档所需的工具链（Sphinx） | +| `-h`、`--help` | — | 显示帮助 | + +完整参数说明见 `sh install.sh --help`。 diff --git a/docs/source/zh_CN/user_guide/robots.md b/docs/source/zh_CN/user_guide/robots.md index 01e0a46..dffe954 100644 --- a/docs/source/zh_CN/user_guide/robots.md +++ b/docs/source/zh_CN/user_guide/robots.md @@ -134,8 +134,8 @@ registry.robotcfg("my-robot")(MyRobot) 先通过 registry 构造配置并预览机器人： ```bash -uv run scripts/view.py robot=my-robot -uv run pytest motrix_envs/tests/test_robot_cfg.py -q +python scripts/view.py robot=my-robot +python -m pytest motrix_envs/tests/test_robot_cfg.py -q ``` 至少应验证模型能够构建、`base_link_name` 存在、关节与 actuator 对应、默认 key pose 完整，以及任务需要的碰撞体和 site @@ -146,8 +146,8 @@ uv run pytest motrix_envs/tests/test_robot_cfg.py -q `_ROBOT_METADATA` 中补充类型和截图参数，然后生成截图与表格： ```bash -uv run docs/scripts/generate_robot_docs.py --screenshots my-robot -uv run docs/scripts/generate_robot_docs.py --check +python docs/scripts/generate_robot_docs.py --screenshots my-robot +python docs/scripts/generate_robot_docs.py --check ``` ## 独立预览 @@ -155,7 +155,7 @@ uv run docs/scripts/generate_robot_docs.py --check 使用 `view.py` 可以在默认姿态下查看已注册 robot，无需创建 RL environment： ```bash -uv run scripts/view.py robot=go2 +python scripts/view.py robot=go2 ``` robot 模式会构建一个静态标准场景，不采样 action，也不执行 physics rollout。 diff --git a/docs/source/zh_CN/user_guide/tutorial/basic_frame.md b/docs/source/zh_CN/user_guide/tutorial/basic_frame.md index c2a2aed..15cdb6d 100644 --- a/docs/source/zh_CN/user_guide/tutorial/basic_frame.md +++ b/docs/source/zh_CN/user_guide/tutorial/basic_frame.md @@ -88,7 +88,7 @@ Trainer 负责框架特有的模型创建、优化、checkpoint 序列化和推 例如： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo num_envs=1024 +python scripts/train.py task=cartpole/skrl.ppo num_envs=1024 ``` 该命令会依次执行： @@ -104,8 +104,8 @@ uv run scripts/train.py task=cartpole/skrl.ppo num_envs=1024 同一个环境可以拥有多份 Task 配方，不需要修改环境实现： ```bash -uv run scripts/train.py task=cartpole/skrl.ppo -uv run scripts/train.py task=cartpole/rslrl.ppo +python scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/rslrl.ppo ``` SKRL 提供 JAX 与 Torch provider，RSLRL 使用 Torch；`motrix.fastsac` 通过 `algo.asynchronous` 选择同步或异步 Torch trainer。所选 Task 与 provider 共同决定算法配置和输出 metadata。 diff --git a/docs/source/zh_CN/user_guide/tutorial/custom_training_backend.md b/docs/source/zh_CN/user_guide/tutorial/custom_training_backend.md index 6623bfe..c615428 100644 --- a/docs/source/zh_CN/user_guide/tutorial/custom_training_backend.md +++ b/docs/source/zh_CN/user_guide/tutorial/custom_training_backend.md @@ -193,7 +193,7 @@ seed: 42 如果后端已经在启动路径中完成注册，可以直接用 CLI： ```bash -uv run scripts/train.py task=cartpole/myrl.ppo +python scripts/train.py task=cartpole/myrl.ppo ``` 如果是外部实验包，使用自己的 Hydra config root，并在进入训练入口前导入 backend 注册模块。无需额外的 Python task registry。 diff --git a/docs/source/zh_CN/user_guide/tutorial/export_onnx.md b/docs/source/zh_CN/user_guide/tutorial/export_onnx.md index f854d7b..acece4e 100644 --- a/docs/source/zh_CN/user_guide/tutorial/export_onnx.md +++ b/docs/source/zh_CN/user_guide/tutorial/export_onnx.md @@ -18,17 +18,8 @@ SKRL/JAX checkpoint 暂不支持导出。导出命令接收完整的 run 目录 ## 安装依赖 -在仓库根目录安装 ONNX 依赖以及训练所用的后端： - -```bash -# SKRL/Torch 或 Motrix FastSAC -uv sync --all-packages --extra onnx --extra skrl-torch - -# RSL-RL -uv sync --all-packages --extra onnx --extra rslrl -``` - -如果使用 `uv sync --all-packages --all-groups --all-extras` 完成安装，则不需要额外操作。 +无需额外步骤：ONNX 导出与推理所需的依赖（`onnx`、`onnxruntime`）已包含在 `sh install.sh` +安装的默认运行环境中（参见[安装环境](../getting_started/installation.md)）。 ## 导出模型 @@ -41,7 +32,7 @@ runs/cartpole/skrl/torch/ppo// 指定输出文件进行导出： ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir=runs/cartpole/skrl/torch/ppo/ \ output=artifacts/cartpole.onnx ``` @@ -51,7 +42,7 @@ uv run scripts/export_onnx.py \ 如果省略 `output`，模型会写入最佳 checkpoint 所在目录，文件名为 `policy.onnx`： ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir=runs/cartpole/skrl/torch/ppo/ ``` @@ -113,7 +104,7 @@ print(actions) 例如，导出为 opset 17，并增加检查样本数： ```bash -uv run scripts/export_onnx.py \ +python scripts/export_onnx.py \ run_dir= \ output=policy.onnx \ opset=17 \ diff --git a/docs/source/zh_CN/user_guide/tutorial/motrix_deploy.md b/docs/source/zh_CN/user_guide/tutorial/motrix_deploy.md index b727880..98683b0 100644 --- a/docs/source/zh_CN/user_guide/tutorial/motrix_deploy.md +++ b/docs/source/zh_CN/user_guide/tutorial/motrix_deploy.md @@ -8,7 +8,7 @@ 在仓库根目录执行： ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` 这个命令会安装训练、MuJoCo、ONNX Runtime 和 Unitree SDK2 依赖。 @@ -18,7 +18,7 @@ uv sync --all-packages --all-groups --all-extras 没有现成 run 时，训练 flat-terrain Go2 策略： ```bash -uv run scripts/train.py task=go2-walk-flat/rslrl.ppo +python scripts/train.py task=go2-walk-flat/rslrl.ppo ``` 训练结果保存在 `runs/go2-walk-flat/`。 @@ -28,7 +28,7 @@ uv run scripts/train.py task=go2-walk-flat/rslrl.ppo 导出最近一次 run： ```bash -uv run scripts/export_deploy.py env=go2-walk-flat +python scripts/export_deploy.py env=go2-walk-flat ``` 输出目录为 `artifacts/go2-walk-flat.deploy/`。artifact 是部署时唯一需要带走的策略文件，包含模型和运行所需的配置。 @@ -36,7 +36,7 @@ uv run scripts/export_deploy.py env=go2-walk-flat ## 4. 检查 artifact ```bash -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-flat.deploy ``` @@ -45,7 +45,7 @@ uv run motrix-deploy inspect \ ## 5. 先跑 Sim2Sim ```bash -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ --config-name go2_walk_flat_sim2sim \ artifact=artifacts/go2-walk-flat.deploy ``` @@ -58,10 +58,10 @@ uv run motrix-deploy sim2sim \ 将 `enp5s0` 换成实际网卡名称： ```bash -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-flat.deploy -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ --config-name go2_walk_flat_sim2real \ artifact=artifacts/go2-walk-flat.deploy \ backend.network_interface=enp5s0 \ @@ -73,7 +73,7 @@ uv run motrix-deploy sim2real \ 发送策略前，也可以先查看机器人状态： ```bash -uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 +python -m motrix_deploy_unitree.read_lowstate enp5s0 ``` ## 进阶说明 @@ -85,7 +85,7 @@ uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 覆盖 12 个关节： ```bash -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ artifact=artifacts/go2-walk-flat.deploy \ backend.network_interface=enp5s0 \ 'backend.kp=[20,25,30,20,25,30,22,27,32,22,27,32]' \ @@ -98,7 +98,7 @@ uv run motrix-deploy sim2real \ 发送任何运动指令前，可先运行只读诊断： ```bash -uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 +python -m motrix_deploy_unitree.read_lowstate enp5s0 ``` ### 发送单关节运动指令 @@ -107,7 +107,7 @@ uv run python -m motrix_deploy_unitree.read_lowstate enp5s0 `go2-walk-flat` deployment profile 构建控制契约，不需要训练 run、checkpoint、策略或 deployment artifact： ```bash -uv run python -m motrix_deploy_unitree.go2_joint_control \ +python -m motrix_deploy_unitree.go2_joint_control \ enp5s0 \ FL_thigh_joint \ 0.9 \ diff --git a/docs/source/zh_CN/user_guide/tutorial/runs_and_checkpoints.md b/docs/source/zh_CN/user_guide/tutorial/runs_and_checkpoints.md index de393af..c38aee2 100644 --- a/docs/source/zh_CN/user_guide/tutorial/runs_and_checkpoints.md +++ b/docs/source/zh_CN/user_guide/tutorial/runs_and_checkpoints.md @@ -106,16 +106,16 @@ runs/g1-walk-flat/motrix/torch/fastsac/26-07-06_11-37-50-376526/ ```bash # 自动发现最新 run 的最佳策略 - uv run scripts/play.py env=g1-walk-flat + python scripts/play.py env=g1-walk-flat # 指定某个 checkpoint（需能向上找到 metadata.json） - uv run scripts/play.py env=g1-walk-flat policy=/path/to/run/checkpoints/latest.pt + python scripts/play.py env=g1-walk-flat policy=/path/to/run/checkpoints/latest.pt ``` - **续训（resume）**：将 `resume=` 设置为 run 目录或 checkpoint 路径，框架据此解析出 `latest_training_state` 继续训练。 ```bash - uv run scripts/train.py task=g1-walk-flat/motrix.fastsac \ + python scripts/train.py task=g1-walk-flat/motrix.fastsac \ resume=/path/to/run ``` @@ -124,5 +124,5 @@ runs/g1-walk-flat/motrix/torch/fastsac/26-07-06_11-37-50-376526/ TensorBoard 日志（`events.out.tfevents.*`）直接写在 run 根目录下，可按环境查看： ```bash -uv run tensorboard --logdir runs/g1-walk-flat +tensorboard --logdir runs/g1-walk-flat ``` diff --git a/docs/source/zh_CN/user_guide/tutorial/training_and_result.md b/docs/source/zh_CN/user_guide/tutorial/training_and_result.md index 13416d6..8929b1e 100644 --- a/docs/source/zh_CN/user_guide/tutorial/training_and_result.md +++ b/docs/source/zh_CN/user_guide/tutorial/training_and_result.md @@ -10,12 +10,12 @@ ```bash # 训练默认的 Cartpole SKRL PPO Task -uv run scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/skrl.ppo # 选择其他框架或算法 -uv run scripts/train.py task=cartpole/rslrl.ppo -uv run scripts/train.py task=g1-walk-flat/motrix.fastsac -uv run scripts/train.py task=g1-walk-flat/motrix.fastsac algo.asynchronous=false +python scripts/train.py task=cartpole/rslrl.ppo +python scripts/train.py task=g1-walk-flat/motrix.fastsac +python scripts/train.py task=g1-walk-flat/motrix.fastsac algo.asynchronous=false ``` 当前内置的 RL method 与训练后端： @@ -26,28 +26,28 @@ uv run scripts/train.py task=g1-walk-flat/motrix.fastsac algo.asynchronous=false | `rslrl.ppo` | `torch` | RSLRL PPO | | `motrix.fastsac` | `torch` | FastSAC；`algo.asynchronous` 选择同步或异步拓扑 | -运行 `uv run scripts/train.py --help` 可以查看当前代码中全部可选 Task。Task 文件结构和覆盖规则见 [Task 配置与命令行参数覆盖](training_environment_config.md)。 +运行 `python scripts/train.py --help` 可以查看当前代码中全部可选 Task。Task 文件结构和覆盖规则见 [Task 配置与命令行参数覆盖](training_environment_config.md)。 ### 选择训练后端与仿真后端 ```bash # 覆盖训练后端（task.train_backend 为 null 时自动选择） -uv run scripts/train.py task=cartpole/skrl.ppo task.train_backend=jax -uv run scripts/train.py task=cartpole/skrl.ppo task.train_backend=torch +python scripts/train.py task=cartpole/skrl.ppo task.train_backend=jax +python scripts/train.py task=cartpole/skrl.ppo task.train_backend=torch # 指定 manager 环境注入的仿真器 -uv run scripts/train.py task=g1-wbt-dance sim=motrixsim +python scripts/train.py task=g1-wbt-dance sim=motrixsim ``` ### 训练规模与随机种子 ```bash # 并行环境数量 -uv run scripts/train.py task=cartpole/skrl.ppo num_envs=1024 +python scripts/train.py task=cartpole/skrl.ppo num_envs=1024 # 固定随机种子（复现）/ 运行时选择随机种子 -uv run scripts/train.py task=cartpole/skrl.ppo seed=42 -uv run scripts/train.py task=cartpole/skrl.ppo seed=null +python scripts/train.py task=cartpole/skrl.ppo seed=42 +python scripts/train.py task=cartpole/skrl.ppo seed=null ``` ```{note} @@ -58,14 +58,14 @@ Hydra 可以直接覆盖已经声明的算法字段，例如 `algo.agent.learnin ```bash # 训练成功结束后，用本次 run 的最佳策略自动回放 -uv run scripts/train.py task=g1-walk-flat/motrix.fastsac play=true +python scripts/train.py task=g1-walk-flat/motrix.fastsac play=true # 从某个 run 目录或 checkpoint 续训 -uv run scripts/train.py task=g1-walk-flat/motrix.fastsac \ +python scripts/train.py task=g1-walk-flat/motrix.fastsac \ resume=/path/to/run # 启用渲染监控训练过程 -uv run scripts/train.py task=cartpole/skrl.ppo render=true +python scripts/train.py task=cartpole/skrl.ppo render=true ``` ### 常用 Hydra 覆盖项 @@ -92,7 +92,7 @@ uv run scripts/train.py task=cartpole/skrl.ppo render=true TensorBoard 日志写在 run 目录下，可按环境查看： ```bash -uv run tensorboard --logdir runs/cartpole +tensorboard --logdir runs/cartpole ``` 除标准的回报、损失曲线外，若环境通过 `info["Reward"]` 暴露了各 reward 分项，训练时也会将其记录到 TensorBoard。 @@ -103,14 +103,14 @@ uv run tensorboard --logdir runs/cartpole ```bash # 自动发现最新 run 的最佳策略并回放（推荐） -uv run scripts/play.py env=cartpole +python scripts/play.py env=cartpole # 指定某个 checkpoint（需能向上找到 metadata.json） -uv run scripts/play.py env=g1-walk-flat \ +python scripts/play.py env=g1-walk-flat \ policy=/path/to/run/checkpoints/latest.pt # 指定回放环境数量 -uv run scripts/play.py env=cartpole num_envs=100 +python scripts/play.py env=cartpole num_envs=100 ``` ```{note} diff --git a/docs/source/zh_CN/user_guide/tutorial/training_environment_config.md b/docs/source/zh_CN/user_guide/tutorial/training_environment_config.md index 570000e..0fc4940 100644 --- a/docs/source/zh_CN/user_guide/tutorial/training_environment_config.md +++ b/docs/source/zh_CN/user_guide/tutorial/training_environment_config.md @@ -65,19 +65,19 @@ Task 只需填写与算法基础配置不同的值，不需要复制所有算法 启动训练前，可以先输出 Hydra 组合后的完整配置： ```bash -uv run scripts/train.py --cfg job --resolve task=my-robot/skrl.ppo +python scripts/train.py --cfg job --resolve task=my-robot/skrl.ppo ``` 该命令不会开始训练。确认 `task.env`、`num_envs` 和 `algo.trainer.timesteps` 符合预期后，再启动训练： ```bash -uv run scripts/train.py task=my-robot/skrl.ppo +python scripts/train.py task=my-robot/skrl.ppo ``` 运行以下命令可以查看仓库中所有可选 Task： ```bash -uv run scripts/train.py --help +python scripts/train.py --help ``` ## 调整 Task 的运行参数 @@ -191,7 +191,7 @@ MotrixLab CLI 使用 Hydra 的 `key=value` 参数语法。CLI 覆盖只影响本 ### 覆盖运行参数 ```bash -uv run scripts/train.py \ +python scripts/train.py \ task=my-robot/skrl.ppo \ num_envs=64 \ seed=7 \ @@ -202,7 +202,7 @@ uv run scripts/train.py \ 开启训练渲染，并在训练完成后播放： ```bash -uv run scripts/train.py task=my-robot/skrl.ppo render=true play=true +python scripts/train.py task=my-robot/skrl.ppo render=true play=true ``` ### 覆盖算法参数 @@ -210,7 +210,7 @@ uv run scripts/train.py task=my-robot/skrl.ppo render=true play=true SKRL PPO： ```bash -uv run scripts/train.py \ +python scripts/train.py \ task=my-robot/skrl.ppo \ algo.agent.learning_rate=5e-4 \ algo.agent.learning_epochs=8 \ @@ -220,7 +220,7 @@ uv run scripts/train.py \ RSLRL PPO： ```bash -uv run scripts/train.py \ +python scripts/train.py \ task=cartpole/rslrl.ppo \ algo.algorithm.learning_rate=5e-4 \ algo.algorithm.entropy_coef=0.005 \ @@ -230,7 +230,7 @@ uv run scripts/train.py \ FastSAC： ```bash -uv run scripts/train.py \ +python scripts/train.py \ task=g1-walk-flat/motrix.fastsac \ algo.asynchronous=true \ algo.agent.actor_learning_rate=1e-4 \ @@ -244,20 +244,20 @@ uv run scripts/train.py \ 布尔值使用小写 `true` 或 `false`： ```bash -uv run scripts/train.py task=my-robot/skrl.ppo render=true +python scripts/train.py task=my-robot/skrl.ppo render=true ``` 使用 `null` 清空可空字段： ```bash -uv run scripts/train.py task=my-robot/skrl.ppo seed=null -uv run scripts/train.py task=my-robot/skrl.ppo algo.agent.learning_rate_scheduler=null +python scripts/train.py task=my-robot/skrl.ppo seed=null +python scripts/train.py task=my-robot/skrl.ppo algo.agent.learning_rate_scheduler=null ``` 列表建议使用引号，避免 shell 解释方括号： ```bash -uv run scripts/train.py \ +python scripts/train.py \ task=my-robot/skrl.ppo \ 'algo.models.policy.hiddens=[128,64]' \ 'algo.models.value.hiddens=[128,64]' @@ -274,7 +274,7 @@ uv run scripts/train.py \ 将 CLI 覆盖和 `--cfg job --resolve` 组合，可以查看本次运行最终会使用的值： ```bash -uv run scripts/train.py \ +python scripts/train.py \ --cfg job \ --resolve \ task=my-robot/skrl.ppo \ @@ -316,13 +316,13 @@ algo: 使用 JAX 差异配置训练： ```bash -uv run scripts/train.py task=go2-walk-flat/skrl.ppo.jax +python scripts/train.py task=go2-walk-flat/skrl.ppo.jax ``` 直接覆盖后端也可以选择 JAX 训练器： ```bash -uv run scripts/train.py task=go2-walk-flat/skrl.ppo task.train_backend=jax +python scripts/train.py task=go2-walk-flat/skrl.ppo task.train_backend=jax ``` 这种写法不会加载 `skrl.ppo.jax.yaml` 中的后端专用参数。当后端差异文件存在时，应直接选择带 `.jax` 或 `.torch` 后缀的 Task。 @@ -413,15 +413,15 @@ Hydra 会根据 provider 注册的结构化 schema 检查字段名和类型。 `scripts/play.py` 和 `scripts/view.py` 同样使用 `key=value` 语法： ```bash -uv run scripts/view.py env=cartpole num_envs=4 -uv run scripts/play.py env=cartpole num_envs=1 -uv run scripts/play.py policy=/path/to/checkpoint.pt num_envs=1 +python scripts/view.py env=cartpole num_envs=4 +python scripts/play.py env=cartpole num_envs=1 +python scripts/play.py policy=/path/to/checkpoint.pt num_envs=1 ``` Play 默认读取训练 run 中保存的 `task_config.yaml`。如需临时覆盖其中的算法参数，使用 `rl` 作为算法配置根节点。因为 `rl` 初始为空，需要用 `+` 添加路径： ```bash -uv run scripts/play.py \ +python scripts/play.py \ env=cartpole \ '+rl.agent.learning_rate=1e-4' ``` diff --git a/install.ps1 b/install.ps1 new file mode 100644 index 0000000..9307a2c --- /dev/null +++ b/install.ps1 @@ -0,0 +1,32 @@ +# Copyright Motphys Technology Co., Ltd. 2025, 2026 +# SPDX-License-Identifier: Apache-2.0 + +# Windows entry point: delegates to install.sh through the Git Bash bundled +# with Git for Windows. If the PowerShell execution policy blocks direct +# invocation, run: powershell -ExecutionPolicy Bypass -File install.ps1 + +Set-Location -Path $PSScriptRoot -ErrorAction Stop + +# Prefer the Git Bash shipped with Git for Windows, derived from git.exe: +# ...\Git\cmd\git.exe -> ...\Git\bin\bash.exe +$bash = $null +$git = (Get-Command git -ErrorAction SilentlyContinue).Source +if ($git) { + $candidate = $git -replace '\\cmd\\git\.exe$', '\bin\bash.exe' -replace '\\mingw64\\bin\\git\.exe$', '\bin\bash.exe' + if ($candidate -ne $git -and (Test-Path $candidate)) { + $bash = $candidate + } +} + +# Fall back to any bash on PATH (Git Bash, MSYS2, Cygwin or WSL all work). +if (-not $bash) { + $bash = (Get-Command bash -ErrorAction SilentlyContinue).Source +} + +if (-not $bash) { + Write-Error "bash.exe not found: Git for Windows is required. Install it from https://git-scm.com/download/win and retry." + exit 1 +} + +& $bash install.sh @args +exit $LASTEXITCODE diff --git a/install.sh b/install.sh new file mode 100755 index 0000000..72efd0f --- /dev/null +++ b/install.sh @@ -0,0 +1,167 @@ +#!/bin/sh +# Copyright Motphys Technology Co., Ltd. 2025, 2026 +# SPDX-License-Identifier: Apache-2.0 + +# Project bootstrap: runtime environment (`sh install.sh`) or full +# development environment (`sh install.sh --all`). +# +# GPU wheel flavor (--gpu): cuda or rocm. When not given explicitly, detect +# from the loaded kernel driver (nvidia -> cuda, amdgpu -> rocm); CPU-only +# hosts fall back to cuda. +# +# Training backends are enabled with --skrl-torch (default), --skrl-jax or +# --rslrl, named after the extras that install them; multiple flags combine. + +set -e + +usage() { + echo "Usage: sh install.sh [options]" + echo + echo "Options:" + echo " --all Install the full development environment (all packages, groups and extras;" + echo " ROCm hosts skip CUDA-only extras such as skrl-jax)" + echo " --docs Include the docs toolchain (sphinx) for building the documentation" + echo " --gpu cuda|rocm GPU wheel flavor (default: auto-detected from the kernel driver)" + echo " --skrl-torch Training backend: SKRL on PyTorch (default)" + echo " --skrl-jax Training backend: SKRL on JAX (Linux only)" + echo " --rslrl Training backend: RSL-RL on PyTorch" + echo " -h, --help Show this help" +} + +ALL="" +GPU="" +SKRL_TORCH="" +SKRL_JAX="" +RSLRL="" +DOCS="" + +while [ $# -gt 0 ]; do + case "$1" in + --all) + ALL=1 + ;; + --docs) + DOCS=1 + ;; + --gpu) + [ $# -ge 2 ] || { echo "error: $1 requires a value (cuda or rocm)" >&2; exit 1; } + GPU="$2" + shift + ;; + --skrl-torch) + SKRL_TORCH=1 + ;; + --skrl-jax) + SKRL_JAX=1 + ;; + --rslrl) + RSLRL=1 + ;; + -h|--help) + usage + exit 0 + ;; + *) + echo "error: unknown option: $1" >&2 + usage >&2 + exit 1 + ;; + esac + shift +done + +if [ -n "$GPU" ] && [ "$GPU" != cuda ] && [ "$GPU" != rocm ]; then + echo "error: invalid --gpu value: $GPU (expected cuda or rocm)" >&2 + usage >&2 + exit 1 +fi + +if ! command -v uv >/dev/null 2>&1; then + echo "==> uv not found, installing..." + case "$(uname -s 2>/dev/null)" in + MINGW*|MSYS*|CYGWIN*) + # Native Windows (Git Bash): use the official PowerShell installer. + powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" + ;; + *) + if command -v curl >/dev/null 2>&1; then + curl -LsSf https://astral.sh/uv/install.sh | sh + elif command -v wget >/dev/null 2>&1; then + wget -qO- https://astral.sh/uv/install.sh | sh + else + echo "error: neither curl nor wget found; install uv manually:" >&2 + echo " https://docs.astral.sh/uv/getting-started/installation/" >&2 + exit 1 + fi + ;; + esac + export PATH="$HOME/.local/bin:$PATH" + if ! command -v uv >/dev/null 2>&1; then + echo "error: uv installation failed; install it manually:" >&2 + echo " https://docs.astral.sh/uv/getting-started/installation/" >&2 + exit 1 + fi +fi + +# Robot assets (STL/OBJ meshes, motion data) are stored via Git LFS; pull them +# so training does not hit pointer files. `git lfs pull` is incremental and +# skips objects already present on disk. +if git rev-parse --is-inside-work-tree >/dev/null 2>&1; then + if git lfs version >/dev/null 2>&1; then + git lfs install >/dev/null 2>&1 || true + echo "==> Pulling Git LFS assets (robot meshes, motion data)..." + set -x + git lfs pull + set +x + else + echo "error: git-lfs is not installed, but robot assets are stored via Git LFS" >&2 + echo " and training will fail without them. Install it with:" >&2 + echo " Windows (Git Bash): winget install GitHub.git-lfs" >&2 + echo " Debian/Ubuntu: sudo apt install git-lfs" >&2 + echo " Fedora/RHEL: sudo dnf install git-lfs" >&2 + echo " macOS: brew install git-lfs" >&2 + echo " then run: git lfs install && git lfs pull" >&2 + exit 1 + fi +fi + +if [ -z "$GPU" ]; then + if lsmod 2>/dev/null | grep -q '^nvidia '; then + GPU=cuda + elif lsmod 2>/dev/null | grep -q '^amdgpu '; then + GPU=rocm + else + GPU=cuda + fi +fi + +case "$(uname -s 2>/dev/null)" in + MINGW*|MSYS*|CYGWIN*) IS_WINDOWS=1 ;; + *) IS_WINDOWS="" ;; +esac + +if [ "$GPU" = rocm ] && [ -n "$IS_WINDOWS" ]; then + echo "error: --gpu rocm is not available on Windows; ROCm wheels are Linux-only" >&2 + exit 1 +fi + +if [ -n "$ALL" ]; then + if [ "$GPU" = rocm ]; then + set -x + uv sync --all-packages --all-groups --extra rocm --extra skrl-torch --extra rslrl --extra unitree --extra docs + else + set -x + uv sync --all-packages --all-groups --all-extras --no-extra rocm + fi +else + if [ -z "$SKRL_TORCH" ] && [ -z "$SKRL_JAX" ] && [ -z "$RSLRL" ]; then + SKRL_TORCH=1 + fi + EXTRAS="" + [ -n "$SKRL_TORCH" ] && EXTRAS="$EXTRAS --extra skrl-torch" + [ -n "$SKRL_JAX" ] && EXTRAS="$EXTRAS --extra skrl-jax" + [ -n "$RSLRL" ] && EXTRAS="$EXTRAS --extra rslrl" + [ -n "$DOCS" ] && EXTRAS="$EXTRAS --extra docs" + set -x + uv sync --all-packages --no-default-groups --extra "$GPU"$EXTRAS +fi diff --git a/motrix_deploy/README.md b/motrix_deploy/README.md index 39a9498..e71bde1 100644 --- a/motrix_deploy/README.md +++ b/motrix_deploy/README.md @@ -13,30 +13,30 @@ on a physical Go2. From the repository root, install the required development extras: ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` Export a new artifact directory, validate it without opening a backend, and run an interactive deployment: ```bash -uv run scripts/export_deploy.py env=go2-walk-rough +python scripts/export_deploy.py env=go2-walk-rough -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-rough.deploy -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ artifact=artifacts/go2-walk-rough.deploy ``` For the flat-terrain environment, export its own artifact and select the matching runtime recipe: ```bash -uv run scripts/export_deploy.py env=go2-walk-flat +python scripts/export_deploy.py env=go2-walk-flat -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-flat.deploy -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ --config-name go2_walk_flat_sim2sim \ artifact=artifacts/go2-walk-flat.deploy ``` @@ -56,16 +56,17 @@ actuator range, servo gain, tensor shape, checksum, and command-range mismatches Headless and physical runs may set either `rollout.steps` or `rollout.duration_s`; viewer runs may leave both unset. `realtime` defaults to the viewer mode when omitted. -Optional dependencies remain isolated behind extras for smaller runtime environments: +Optional dependencies remain isolated behind extras for smaller runtime environments (ONNX +inference is a core dependency; `mujoco` and `unitree` select the deployment backends): ```bash -uv sync --package motrix-deploy-tasks --extra onnx --extra mujoco --extra unitree +uv sync --package motrix-deploy-tasks --extra mujoco --extra unitree ``` Interactive deployment reads keyboard events directly from the focused GLFW viewer window: ```bash -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ artifact=artifacts/go2-walk-rough.deploy ``` @@ -84,17 +85,17 @@ The hardware plugin imports the Unitree SDK2 Python package only when a physical workspace sync installs the pinned Motphys-maintained fork into the same environment: ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` Inspect the artifact first. With the robot suspended, low-level/debug mode enabled, Ethernet connected, and an emergency stop operator ready, explicitly select the interface and confirm the hardware checklist: ```bash -uv run motrix-deploy inspect \ +motrix-deploy inspect \ artifact=artifacts/go2-walk-rough.deploy -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ artifact=artifacts/go2-walk-rough.deploy \ backend.network_interface=enp3s0 \ hardware.confirm=true diff --git a/motrix_deploy/pyproject.toml b/motrix_deploy/pyproject.toml index 7806cf5..e2dc471 100644 --- a/motrix_deploy/pyproject.toml +++ b/motrix_deploy/pyproject.toml @@ -10,7 +10,11 @@ authors = [{ name = "Motphys", email = "developers@motphys.com" }] requires-python = "==3.10.*" readme = "README.md" license = "Apache-2.0" -dependencies = ["hydra-core>=1.3,<1.4", "motrix-env-core", "numpy>=1.26"] - -[project.optional-dependencies] -onnx = ["onnxruntime==1.23.2"] +# onnxruntime is a core dependency: deployed policies are ONNX artifacts, so +# every deployment runtime needs it for inference. +dependencies = [ + "hydra-core>=1.3,<1.4", + "motrix-env-core", + "numpy>=1.26", + "onnxruntime==1.23.2", +] diff --git a/motrix_deploy/src/motrix_deploy/policy/__init__.py b/motrix_deploy/src/motrix_deploy/policy/__init__.py index 3533db4..fdb858d 100644 --- a/motrix_deploy/src/motrix_deploy/policy/__init__.py +++ b/motrix_deploy/src/motrix_deploy/policy/__init__.py @@ -48,7 +48,9 @@ def __init__( try: import onnxruntime as ort except ImportError as error: - raise RuntimeError("ONNX policy support requires the 'motrix-deploy[onnx]' extra") from error + raise RuntimeError( + "onnxruntime is missing; it is a core dependency of motrix-deploy, so reinstall the environment" + ) from error self._input_spec = input_spec self._output_spec = output_spec self._session = ort.InferenceSession( diff --git a/motrix_deploy_tasks/README.md b/motrix_deploy_tasks/README.md index 0fd5411..bc8e728 100644 --- a/motrix_deploy_tasks/README.md +++ b/motrix_deploy_tasks/README.md @@ -10,7 +10,7 @@ The default sim2sim recipe targets `go2-walk-rough`; the default sim2real recipe workspace config path with: ```bash -uv run motrix-deploy sim2sim \ +motrix-deploy sim2sim \ --config-name go2_walk_flat_sim2sim \ artifact=artifacts/go2-walk-flat.deploy ``` diff --git a/motrix_deploy_tasks/pyproject.toml b/motrix_deploy_tasks/pyproject.toml index 9974d98..9497b08 100644 --- a/motrix_deploy_tasks/pyproject.toml +++ b/motrix_deploy_tasks/pyproject.toml @@ -17,7 +17,6 @@ dependencies = [ ] [project.optional-dependencies] -onnx = ["motrix-deploy[onnx]"] mujoco = ["motrix-deploy-mujoco"] unitree = ["motrix-deploy-unitree"] diff --git a/motrix_deploy_unitree/README.md b/motrix_deploy_unitree/README.md index 1635f1e..58e9329 100644 --- a/motrix_deploy_unitree/README.md +++ b/motrix_deploy_unitree/README.md @@ -7,7 +7,7 @@ physical backend is opened. The SDK is installed from the pinned Motphys-maintained fork as part of the normal workspace sync: ```bash -uv sync --all-packages --all-groups --all-extras +sh install.sh --all ``` The Motphys fork includes the CRC shared libraries in built distributions. The SDK metadata declares @@ -72,7 +72,7 @@ with no publisher, use `motrix-deploy-unitree read-lowstate ` Use the read-only diagnostic before enabling any command path: ```bash -uv run motrix-deploy-unitree read-lowstate enp5s0 +motrix-deploy-unitree read-lowstate enp5s0 ``` It subscribes to `rt/lowstate` for 10 seconds by default and prints at most one sample every 0.5 seconds. It does not need @@ -96,7 +96,7 @@ transport troubleshooting. The command returns `0` after receiving a valid frame The packaged helper requires a frozen deployment artifact as its robot, gain, timing, and limit contract: ```bash -uv run motrix-deploy-unitree joint-control \ +motrix-deploy-unitree joint-control \ enp5s0 \ FL_thigh_joint \ 0.9 \ @@ -112,7 +112,7 @@ state timeout and the remote Select emergency stop remain active. Use `--move-duration`, `--hold-duration`, and `--return-duration` to change the default `2.0`, `1.0`, and `2.0` second phases. Setting `--return-duration 0` skips the return trajectory, but closing still changes all joints to damping mode. -Run `uv run motrix-deploy-unitree joint-control --help` for the complete command reference. Although the +Run `motrix-deploy-unitree joint-control --help` for the complete command reference. Although the helper changes one target, each Unitree `LowCmd` necessarily contains commands for all 12 joints. ## Policy gain overrides @@ -125,7 +125,7 @@ default-pose transition and policy commands; damping stop still uses `kp=0` and From the repository root: ```bash -uv run motrix-deploy sim2real \ +motrix-deploy sim2real \ artifact=artifacts/go2-walk-rough.deploy \ backend.network_interface=enp5s0 \ hardware.confirm=true diff --git a/motrix_rl/README.md b/motrix_rl/README.md index 1410d26..161c500 100644 --- a/motrix_rl/README.md +++ b/motrix_rl/README.md @@ -5,18 +5,19 @@ interfaces. Concrete providers currently support SKRL PPO with JAX or PyTorch, R FastSAC implementation. Environment-specific training presets live under `configs/task/`, while shared provider defaults live under -`configs/algo_base/`. From the workspace root, select a preset with Hydra's `task=/` syntax: +`configs/algo_base/`. From the workspace root, select a preset with Hydra's `task=/` syntax (run from the +activated workspace environment, `source .venv/bin/activate`): ```bash -uv run scripts/train.py task=cartpole/skrl.ppo -uv run scripts/train.py task=cartpole/rslrl.ppo -uv run scripts/train.py task=g1-walk-rough/motrix.fastsac +python scripts/train.py task=cartpole/skrl.ppo +python scripts/train.py task=cartpole/rslrl.ppo +python scripts/train.py task=g1-walk-rough/motrix.fastsac ``` Install the extra required by the selected provider before training: ```bash -uv sync --all-packages --extra skrl-jax -uv sync --all-packages --extra skrl-torch -uv sync --all-packages --extra rslrl +sh install.sh --skrl-jax # JAX (Linux only) +sh install.sh --skrl-torch # PyTorch (default) +sh install.sh --rslrl # RSLRL (PyTorch only) ``` diff --git a/motrix_rl/pyproject.toml b/motrix_rl/pyproject.toml index 0e1b75a..f92d603 100644 --- a/motrix_rl/pyproject.toml +++ b/motrix_rl/pyproject.toml @@ -21,13 +21,16 @@ dependencies = [ "numpy>=1.26", "omegaconf>=2.3,<2.4", "nvidia-ml-py>=13.610.43", - # Default training backend; also required by scripts/view.py. Resolved - # from the pytorch-cu128 index via the workspace root tool.uv.sources. - "torch==2.7.0", + # ONNX export of trained policies; onnxruntime (for the parity check and + # deployment inference) comes in via motrix-deploy. + "onnx==1.20.1", + # Default training backend; also required by scripts/view.py. The exact + # version and wheel index are chosen by the workspace root `cuda`/`rocm` + # extras (see the root pyproject.toml tool.uv.sources). + "torch>=2.7,<3", ] [project.optional-dependencies] -onnx = ["onnx==1.20.1", "onnxruntime==1.23.2"] skrl-jax = [ "skrl>=2.1,<2.2; sys_platform == 'linux'", "jax[cuda12]==0.4.34; sys_platform == 'linux'", @@ -36,15 +39,9 @@ skrl-jax = [ ] skrl-torch = [ "skrl>=2.1,<2.2", - "torch==2.7.0", - "torchvision==0.22.0", - "torchaudio==2.7.0", + "torch>=2.7,<3", ] rslrl = [ - "motrix-deploy[onnx]", - "onnx==1.20.1", "rsl-rl-lib>=4.0,<5.0", - "torch==2.7.0", - "torchvision==0.22.0", - "torchaudio==2.7.0", + "torch>=2.7,<3", ] diff --git a/motrix_rl/src/motrix_rl/system_metrics.py b/motrix_rl/src/motrix_rl/system_metrics.py index 280643c..0fbb687 100644 --- a/motrix_rl/src/motrix_rl/system_metrics.py +++ b/motrix_rl/src/motrix_rl/system_metrics.py @@ -5,8 +5,8 @@ CPU samplers read Linux ``/proc`` interfaces and return ``None`` where they are unavailable, so panels degrade to ``n/a`` fields; memory sampling also -supports Windows via ``GlobalMemoryStatusEx``. The GPU samplers use NVML, -which works on any platform with an NVIDIA driver. +supports Windows via ``GlobalMemoryStatusEx``. GPU samplers use NVML on +NVIDIA hosts and AMD SMI on ROCm hosts. """ from __future__ import annotations @@ -192,6 +192,22 @@ def sample(self) -> MemoryUsage | None: # NVML reads the same counters nvidia-smi reports, but in-process at # microsecond cost instead of a subprocess spawn per query. _nvml_state: tuple[Any, list[Any]] | tuple[()] | None = None # None: untried; (): unavailable +_amd_smi_state: tuple[Any, list[Any]] | tuple[()] | None = None +_amd_activity_supported: bool | None = None + + +def _amd_smi() -> tuple[Any, list[Any]] | None: + """Lazily initialize AMD SMI, returning ``(module, processor_handles)``.""" + global _amd_smi_state + if _amd_smi_state is None: + try: + import amdsmi + + amdsmi.amdsmi_init() + _amd_smi_state = (amdsmi, amdsmi.amdsmi_get_processor_handles()) + except Exception: + _amd_smi_state = () + return _amd_smi_state or None def _nvml() -> tuple[Any, list[Any]] | None: @@ -210,9 +226,20 @@ def _nvml() -> tuple[Any, list[Any]] | None: class GpuMemoryUsageSampler: - """Read aggregate NVIDIA memory usage across all visible GPUs via NVML.""" + """Read aggregate accelerator memory via AMD SMI or NVML.""" def sample(self) -> MemoryUsage | None: + session = _amd_smi() + if session is not None: + amdsmi, devices = session + used = total = 0 + try: + for device in devices: + used += int(amdsmi.amdsmi_get_gpu_memory_usage(device, amdsmi.AmdSmiMemoryType.VRAM)) + total += int(amdsmi.amdsmi_get_gpu_memory_total(device, amdsmi.AmdSmiMemoryType.VRAM)) + except Exception: + return None + return MemoryUsage(used_bytes=used, total_bytes=total) if total > 0 else None session = _nvml() if session is None: return None @@ -229,9 +256,31 @@ def sample(self) -> MemoryUsage | None: class GpuUtilizationSampler: - """Read aggregate NVIDIA GPU utilization across all visible GPUs via NVML.""" + """Read aggregate accelerator utilization via AMD SMI or NVML.""" def sample(self) -> float | None: + global _amd_activity_supported + session = _amd_smi() + if session is not None: + amdsmi, devices = session + values: list[float] = [] + if _amd_activity_supported is not False: + try: + for device in devices: + activity = amdsmi.amdsmi_get_gpu_activity(device) + value = activity.get("gfx_activity", activity.get("gpu_busy_percent")) + if value is not None: + values.append(float(value)) + except Exception: + _amd_activity_supported = False + if _amd_activity_supported is False: + # Some integrated AMD GPUs (including Radeon 890M) expose + # VRAM through AMD SMI but return AMDSMI_STATUS_UNEXPECTED_DATA + # for ``amdsmi_get_gpu_activity``. The kernel's DRM sysfs + # counter is available on those devices and reports the same + # busy percentage used by rocm-smi. + values = _sysfs_gpu_busy_percent() + return sum(values) / len(values) if values else None session = _nvml() if session is None: return None @@ -243,3 +292,18 @@ def sample(self) -> float | None: except pynvml.NVMLError: return None return sum(values) / len(values) if values else None + + +def _sysfs_gpu_busy_percent() -> list[float]: + """Read AMD DRM GPU busy counters as a fallback for unsupported SMI APIs.""" + if sys.platform == "win32": + return [] + values: list[float] = [] + for path in Path("/sys/class/drm").glob("card*/device/gpu_busy_percent"): + try: + value = float(path.read_text().strip()) + except (OSError, ValueError): + continue + if 0.0 <= value <= 100.0: + values.append(value) + return values diff --git a/motrix_rl/tests/test_system_metrics.py b/motrix_rl/tests/test_system_metrics.py index b2387de..0967656 100644 --- a/motrix_rl/tests/test_system_metrics.py +++ b/motrix_rl/tests/test_system_metrics.py @@ -65,6 +65,9 @@ def memory_info(handle): nvmlDeviceGetMemoryInfo=memory_info, NVMLError=RuntimeError, ) + # The samplers prefer AMD SMI; pin it off so these tests exercise the + # NVML backend on AMD hosts too, where a real session would win. + monkeypatch.setattr(system_metrics, "_amd_smi_state", ()) monkeypatch.setattr(system_metrics, "_nvml_state", (fake_pynvml, handles)) @@ -95,13 +98,120 @@ def test_gpu_samplers_return_none_on_nvml_error(monkeypatch) -> None: assert GpuMemoryUsageSampler().sample() is None +def _fake_amdsmi(monkeypatch, handles, utilization, memory) -> None: + """Install a fake ``(amdsmi, handles)`` session with per-handle metric tables.""" + + def gpu_activity(handle): + return {"gfx_activity": utilization[handle]} + + def gpu_memory_usage(handle, memory_type): + return memory[handle][0] + + def gpu_memory_total(handle, memory_type): + return memory[handle][1] + + fake_amdsmi = types.SimpleNamespace( + amdsmi_get_gpu_activity=gpu_activity, + amdsmi_get_gpu_memory_usage=gpu_memory_usage, + amdsmi_get_gpu_memory_total=gpu_memory_total, + AmdSmiMemoryType=types.SimpleNamespace(VRAM="vram"), + ) + # Pin NVML off so the AMD SMI backend is exercised on every host. + monkeypatch.setattr(system_metrics, "_nvml_state", ()) + monkeypatch.setattr(system_metrics, "_amd_activity_supported", None) + monkeypatch.setattr(system_metrics, "_amd_smi_state", (fake_amdsmi, handles)) + + +def test_gpu_samplers_aggregate_via_amdsmi_backend(monkeypatch) -> None: + handles = ["gpu0", "gpu1"] + _fake_amdsmi( + monkeypatch, + handles, + utilization={"gpu0": 10, "gpu1": 30}, + memory={"gpu0": (100 * 1024**2, 200 * 1024**2), "gpu1": (300 * 1024**2, 400 * 1024**2)}, + ) + + assert GpuUtilizationSampler().sample() == 20.0 + assert GpuMemoryUsageSampler().sample() == MemoryUsage(used_bytes=400 * 1024**2, total_bytes=600 * 1024**2) + + def test_gpu_samplers_return_none_without_nvml(monkeypatch) -> None: + monkeypatch.setattr(system_metrics, "_amd_smi_state", ()) monkeypatch.setattr(system_metrics, "_nvml_state", ()) assert GpuUtilizationSampler().sample() is None assert GpuMemoryUsageSampler().sample() is None +def _patch_sysfs_gpu_busy(monkeypatch, entries) -> None: + """Serve ``entries`` (text or Exception) as DRM ``gpu_busy_percent`` files.""" + + class _SysfsFile: + def __init__(self, payload) -> None: + self._payload = payload + + def read_text(self) -> str: + if isinstance(self._payload, Exception): + raise self._payload + return self._payload + + monkeypatch.setattr(system_metrics.Path, "glob", lambda self, pattern: iter(_SysfsFile(entry) for entry in entries)) + + +def test_sysfs_gpu_busy_percent_reads_valid_counters(monkeypatch) -> None: + _patch_sysfs_gpu_busy(monkeypatch, ["12\n", "34\n"]) + + assert system_metrics._sysfs_gpu_busy_percent() == [12.0, 34.0] + + +def test_sysfs_gpu_busy_percent_skips_invalid_and_unreadable_counters(monkeypatch) -> None: + _patch_sysfs_gpu_busy(monkeypatch, ["120\n", "-5\n", "n/a\n", OSError("denied"), "55\n"]) + + assert system_metrics._sysfs_gpu_busy_percent() == [55.0] + + +def test_sysfs_gpu_busy_percent_returns_nothing_on_windows(monkeypatch) -> None: + monkeypatch.setattr(sys, "platform", "win32") + glob_called = False + + def fail_glob(pattern): + nonlocal glob_called + glob_called = True + return iter(()) + + monkeypatch.setattr(system_metrics.Path, "glob", fail_glob) + + assert system_metrics._sysfs_gpu_busy_percent() == [] + assert not glob_called + + +def test_gpu_utilization_sampler_falls_back_to_sysfs_when_amdsmi_activity_unsupported(monkeypatch) -> None: + handles = ["gpu0", "gpu1"] + + def gpu_activity(handle): + raise RuntimeError("AMDSMI_STATUS_UNEXPECTED_DATA") + + memory = {"gpu0": (1, 2), "gpu1": (3, 4)} + fake_amdsmi = types.SimpleNamespace( + amdsmi_get_gpu_activity=gpu_activity, + amdsmi_get_gpu_memory_usage=lambda handle, memory_type: memory[handle][0], + amdsmi_get_gpu_memory_total=lambda handle, memory_type: memory[handle][1], + AmdSmiMemoryType=types.SimpleNamespace(VRAM="vram"), + ) + monkeypatch.setattr(system_metrics, "_nvml_state", ()) + monkeypatch.setattr(system_metrics, "_amd_activity_supported", None) + monkeypatch.setattr(system_metrics, "_amd_smi_state", (fake_amdsmi, handles)) + _patch_sysfs_gpu_busy(monkeypatch, ["20\n", "60\n"]) + + assert GpuUtilizationSampler().sample() == 40.0 + + # The failure is latched, so later samples keep reading sysfs instead of + # retrying the unsupported AMD SMI activity API on every call. + monkeypatch.setattr(system_metrics.Path, "glob", lambda self, pattern: iter([])) + + assert GpuUtilizationSampler().sample() is None + + def test_memory_usage_sampler_reads_proc_meminfo(tmp_path) -> None: meminfo = tmp_path / "meminfo" meminfo.write_text( diff --git a/pyproject.toml b/pyproject.toml index afb6407..9347214 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,24 @@ docs = [ "sphinx-design", "tensorboard==2.20.0", ] +# Mutually exclusive GPU wheel profiles: `cuda` pulls the NVIDIA wheels from +# the pytorch-cu128 index, `rocm` the AMD wheels from the pytorch-rocm72 +# index. Select exactly one with `uv sync --extra cuda` or +# `uv sync --extra rocm`; the extras are declared conflicting so uv keeps +# both branches in the single uv.lock. Without any extra, torch resolves +# from the default index (PyPI). +cuda = [ + "torch==2.7.0; sys_platform == 'linux' or sys_platform == 'win32'", + "torchvision==0.22.0; sys_platform == 'linux' or sys_platform == 'win32'", + "torchaudio==2.7.0; sys_platform == 'linux' or sys_platform == 'win32'", +] +# ROCm wheels only exist for Linux x86_64; the markers keep other platforms +# from resolving these requirements against the default (PyPI) index. +rocm = [ + "amdsmi; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch==2.11.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "triton-rocm==3.6.0; sys_platform == 'linux' and platform_machine == 'x86_64'", +] [dependency-groups] dev = [ @@ -41,9 +59,7 @@ dev = [ test = [ { include-group = "dev" }, "hydra-core>=1.3,<1.4", - "motrix-deploy[onnx]", "motrix-deploy-unitree", - "onnx==1.20.1", ] [tool.uv.workspace] @@ -59,11 +75,29 @@ members = [ "motrix_rl", ] +[tool.uv] +package = false +prerelease = "if-necessary-or-explicit" +# `cuda` and `rocm` resolve torch from different indexes and can never be +# installed together; declaring them conflicting lets uv keep both branches +# (as separate forks) in the same uv.lock. +conflicts = [ + [ + { extra = "cuda" }, + { extra = "rocm" }, + ], +] + [[tool.uv.index]] name = "pytorch-cu128" url = "https://download.pytorch.org/whl/cu128" explicit = true +[[tool.uv.index]] +name = "pytorch-rocm72" +url = "https://download.pytorch.org/whl/rocm7.2" +explicit = true + [[tool.uv.index]] name = "motphys-dev" url = "https://pypi.motphys.com/simple" @@ -78,8 +112,13 @@ motrix-env-motrixsim = { workspace = true } motrix-env-mujoco = { workspace = true } motrix-envs = { workspace = true } motrix-rl = { workspace = true } +# The `extra = "rocm"` entry routes torch requirements declared in the rocm +# extra to the ROCm index; the `extra = "cuda"` entry does the same for the +# cuda extra. Requirements outside both extras (bare sync) fall back to the +# default index. torch = [ - { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'", extra = "cuda" }, + { index = "pytorch-rocm72", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'", extra = "rocm" }, ] torchvision = [ { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, @@ -87,10 +126,9 @@ torchvision = [ torchaudio = [ { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] - -[tool.uv] -package = false -prerelease = "if-necessary-or-explicit" +triton-rocm = [ + { index = "pytorch-rocm72", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" }, +] [tool.mypy] files = [ diff --git a/uv.lock b/uv.lock index 5847447..8b1dd05 100644 --- a/uv.lock +++ b/uv.lock @@ -2,12 +2,18 @@ version = 1 revision = 3 requires-python = "==3.10.*" resolution-markers = [ - "(platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux')", - "sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'", - "platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", - "sys_platform == 'darwin'", - "sys_platform == 'win32'", -] + "platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm'", + "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform != 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')", + "platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm'", + "(platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm') or (platform_python_implementation != 'CPython' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm')", + "platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm'", + "sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm'", + "extra != 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm'", +] +conflicts = [[ + { package = "motrix-lab", extra = "cuda" }, + { package = "motrix-lab", extra = "rocm" }, +]] [manifest] members = [ @@ -53,6 +59,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z" }, ] +[[package]] +name = "amdsmi" +version = "7.0.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/45/c1/330da195623ec7d9f699be2dbec98df364b1def9b48aa169f1abe369804f/amdsmi-7.0.2.tar.gz", hash = "sha256:3e622e48c630b889045a6f57387455cdf082066348718172dd8af6d275baf8f2", size = 61577, upload-time = "2025-10-11T05:17:44.898Z" } +wheels = [ + { url = 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name = "onnxruntime", marker = "extra == 'onnx'", specifier = "==1.23.2" }, + { name = "onnxruntime", specifier = "==1.23.2" }, ] -provides-extras = ["onnx"] [[package]] name = "motrix-deploy-mujoco" @@ -865,9 +950,6 @@ dependencies = [ mujoco = [ { name = "motrix-deploy-mujoco" }, ] -onnx = [ - { name = "motrix-deploy", extra = ["onnx"] }, -] unitree = [ { name = "motrix-deploy-unitree" }, ] @@ -875,13 +957,12 @@ unitree = [ [package.metadata] requires-dist = [ { name = "motrix-deploy", editable = "motrix_deploy" }, - { name = "motrix-deploy", extras = ["onnx"], marker = "extra == 'onnx'", editable = "motrix_deploy" }, { name = "motrix-deploy-mujoco", marker = "extra == 'mujoco'", editable = "motrix_deploy_mujoco" }, { name = "motrix-deploy-unitree", marker = "extra == 'unitree'", editable = "motrix_deploy_unitree" }, { name = "motrix-env-core", editable = "motrix_env_core" }, { name = "numpy", specifier = ">=1.26" }, ] -provides-extras = ["onnx", "mujoco", "unitree"] +provides-extras = ["mujoco", "unitree"] [[package]] name = "motrix-deploy-unitree" @@ -1004,6 +1085,13 @@ dependencies = [ ] [package.optional-dependencies] +cuda = [ + { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "torchaudio", version = "2.7.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (platform_machine != 'aarch64' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (platform_python_implementation != 'CPython' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform != 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, + { name = "torchaudio", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (platform_python_implementation != 'CPython' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-cuda') or (sys_platform != 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, + { name = "torchvision", version = "0.22.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (platform_machine != 'aarch64' and extra == 'extra-10-motrix-lab-cuda' and 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"autodocsumm" }, { name = "myst-parser" }, @@ -1016,6 +1104,11 @@ docs = [ { name = "sphinxcontrib-video" }, { name = "tensorboard" }, ] +rocm = [ + { name = "amdsmi", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.11.0+rocm7.2", source = { registry = "https://download.pytorch.org/whl/rocm7.2" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "triton-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] unitree = [ { name = "motrix-deploy-unitree" }, ] @@ -1030,16 +1123,15 @@ dev = [ test = [ { name = "dprint-py" }, { name = "hydra-core" }, - { name = "motrix-deploy", extra = ["onnx"] }, { name = "motrix-deploy-unitree" }, { name = "mypy" }, - { name = "onnx" }, { name = "pytest" }, { name = "pytest-cov" }, ] [package.metadata] requires-dist = [ + { name = "amdsmi", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'rocm'" }, { name = "autodocsumm", marker = "extra == 'docs'" }, { name = "motrix-deploy", editable = "motrix_deploy" }, { name = "motrix-deploy-mujoco", editable = "motrix_deploy_mujoco" }, @@ -1058,8 +1150,13 @@ requires-dist = [ { name = "sphinx-togglebutton", marker = "extra == 'docs'" }, { name = "sphinxcontrib-video", marker = "extra == 'docs'" }, { name = "tensorboard", marker = "extra == 'docs'", specifier = "==2.20.0" }, + { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'rocm'", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/rocm7.2", conflict = { package = "motrix-lab", extra = "rocm" } }, + { name = "torch", marker = "(sys_platform == 'linux' and extra == 'cuda') or (sys_platform == 'win32' and extra == 'cuda')", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "motrix-lab", extra = "cuda" } }, + { name = "torchaudio", marker = "(sys_platform == 'linux' and extra == 'cuda') or (sys_platform == 'win32' and extra == 'cuda')", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" }, + { name = "torchvision", marker = "(sys_platform == 'linux' and extra == 'cuda') or (sys_platform == 'win32' and extra == 'cuda')", specifier = "==0.22.0", index = "https://download.pytorch.org/whl/cu128" }, + { name = "triton-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'rocm'", specifier = "==3.6.0", index = "https://download.pytorch.org/whl/rocm7.2" }, ] -provides-extras = ["unitree", "docs"] +provides-extras = ["unitree", "docs", "cuda", "rocm"] [package.metadata.requires-dev] dev = [ @@ -1071,10 +1168,8 @@ dev = [ test = [ { name = "dprint-py", specifier = ">=0.57.0.0" }, { name = "hydra-core", specifier = ">=1.3,<1.4" }, - { name = "motrix-deploy", extras = ["onnx"], editable = "motrix_deploy" }, { name = "motrix-deploy-unitree", editable = "motrix_deploy_unitree" }, { name = "mypy", specifier = ">=1.15,<2" }, - { name = "onnx", specifier = "==1.20.1" }, { name = "pytest", specifier = ">=8.3.3,<8.4.0" }, { name = "pytest-cov" }, ] @@ -1092,46 +1187,32 @@ dependencies = [ { name = "numpy" }, { name = "nvidia-ml-py" }, { name = "omegaconf" }, + { name = "onnx" }, { name = "python-abc" }, { name = "rich" }, - { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" }, - { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-cuda') or (extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'x86_64' and sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda') or (sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform == 'win32' and extra != 'extra-10-motrix-lab-cuda') or (sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm')" }, + { name = "torch", version = "2.11.0+rocm7.2", source = { registry = "https://download.pytorch.org/whl/rocm7.2" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-10-motrix-lab-rocm') or (platform_machine != 'x86_64' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform != 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, ] [package.optional-dependencies] -onnx = [ - { name = "onnx" }, - { name = "onnxruntime" }, -] rslrl = [ - { name = "motrix-deploy", extra = ["onnx"] }, - { name = "onnx" }, { name = "rsl-rl-lib" }, - { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" }, - { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "torchaudio", version = "2.7.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" }, - { name = "torchaudio", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" }, - { name = "torchaudio", version = 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source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-cuda') or (extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'x86_64' and sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda') or (sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'win32' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform == 'win32' and extra != 'extra-10-motrix-lab-cuda') or (sys_platform == 'linux' and extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm') or (sys_platform == 'linux' and extra != 'extra-10-motrix-lab-cuda' and extra != 'extra-10-motrix-lab-rocm')" }, + { name = "torch", version = "2.11.0+rocm7.2", source = { registry = 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(extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, + { name = "tensorflow", marker = "sys_platform == 'linux' or (extra == 'extra-10-motrix-lab-cuda' and extra == 'extra-10-motrix-lab-rocm')" }, ] skrl-torch = [ { name = "skrl" }, - { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" }, - { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, - { name = "torchaudio", version = "2.7.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" }, - { name = "torchaudio", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" }, - { name = 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