{
  "id": "openpi-scripts-train-py",
  "name": "OpenPI: JAX policy training",
  "summary": "Train or fine-tune an OpenPI policy with the JAX training pipeline and an explicit upstream training/data configuration. Inspect the pinned source and setup before use.",
  "framework": "openpi",
  "kind": "official_example",
  "source_registry": "official-robot-workflows-v1",
  "capabilities": [
    "policy-training",
    "robot-workflow"
  ],
  "robot_types": [
    "source-defined"
  ],
  "robot_scope": "Generic workflow entrypoint. The explicitly chosen dataset, policy, environment or device configuration determines robot compatibility; no supported-robot list or runtime result is inferred.",
  "source": {
    "repository": "https://github.com/Physical-Intelligence/openpi",
    "commit": "215abfb217dbac7d5f1273282331b9b1866c0479",
    "tag": "main",
    "path": "scripts/train.py",
    "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/scripts/train.py",
    "raw_url": "https://raw.githubusercontent.com/Physical-Intelligence/openpi/215abfb217dbac7d5f1273282331b9b1866c0479/scripts/train.py",
    "sha256": "22f98a06aaa6e68e0f725d727ab95398238d1cae8a46bb94e829f330758a9baf",
    "bytes": 10488,
    "authors": [
      "Physical Intelligence team"
    ]
  },
  "license": {
    "spdx": "Apache-2.0",
    "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/LICENSE",
    "sha256": "43070e2d4e532684de521b885f385d0841030efa2b1a20bafb76133a5e1379c1",
    "scope": "Selected first-party workflow Python source only; preserve all upstream copyright, author and per-file notices. This does not relicense model weights, datasets, Gemma or other third-party model code, robot firmware, simulator binaries, meshes, dependencies or external services; their separate terms and access requirements remain applicable.",
    "bytes": 11356
  },
  "access": {
    "mode": "source_reference",
    "hub_prepare": false,
    "instructions": "Fetch the pinned repository, verify this source checksum, and use upstream installation and invocation. This record does not install or execute code.",
    "setup_url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/README.md",
    "setup_evidence": [
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        "path": "README.md",
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        "bytes": 23254,
        "purpose": "upstream author and setup"
      },
      {
        "path": "pyproject.toml",
        "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/pyproject.toml",
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        "bytes": 3110,
        "purpose": "Python/package declaration"
      },
      {
        "path": "examples/aloha_real/README.md",
        "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/examples/aloha_real/README.md",
        "sha256": "cd56d8224ee9500837fd83a00ffe7cc1c96008558a4a81fabaf96cdccf9a9a24",
        "bytes": 6440,
        "purpose": "ALOHA hardware setup"
      },
      {
        "path": "examples/aloha_sim/README.md",
        "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/examples/aloha_sim/README.md",
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      },
      {
        "path": "examples/droid/README.md",
        "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/examples/droid/README.md",
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      },
      {
        "path": "examples/libero/README.md",
        "url": "https://github.com/Physical-Intelligence/openpi/blob/215abfb217dbac7d5f1273282331b9b1866c0479/examples/libero/README.md",
        "sha256": "a2caaf5459c4b01837b73aa93a6f69e8cdf10385b39c34eba36afa85f83fafe9",
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        "purpose": "LIBERO evaluation setup"
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    ]
  },
  "requirements": {
    "python": "Python >=3.11 as declared by the pinned pyproject.toml",
    "packages": [
      "Pinned OpenPI checkout and openpi-client",
      "Upstream uv environment; JAX/CUDA or PyTorch backend as selected",
      "Environment-specific ALOHA, DROID or LIBERO dependencies when selected"
    ],
    "display": "Upstream supports Ubuntu 22.04. Policy inference/training requires suitable NVIDIA GPU memory and separately obtained checkpoints/data; real robot clients additionally require the exact upstream robot setup and a configured policy server.",
    "execution_domain": "computation",
    "source_imports": [
      "dataclasses",
      "etils.epath",
      "flax.nnx",
      "flax.training",
      "flax.traverse_util",
      "functools",
      "jax",
      "jax.experimental",
      "jax.numpy",
      "logging",
      "numpy",
      "openpi.models.model",
      "openpi.shared.array_typing",
      "openpi.shared.nnx_utils",
      "openpi.training.checkpoints",
      "openpi.training.config",
      "openpi.training.data_loader",
      "openpi.training.optimizer",
      "openpi.training.sharding",
      "openpi.training.utils",
      "openpi.training.weight_loaders",
      "optax",
      "platform",
      "tqdm_loggable.auto",
      "typing",
      "wandb"
    ]
  },
  "invocation": {
    "entrypoint": "uv run scripts/train.py",
    "notes": "Use the pinned upstream environment and CLI configuration, dataset/checkpoint paths and platform-specific README. Some upstream workflows can download assets or publish datasets; inspect flags before use. No checkpoint licensing, robot compatibility or runtime success is established by this source reference."
  },
  "popularity": {
    "metric": "github_stars",
    "repository_stars": 14274,
    "checked_at": "2026-10-18",
    "meaning": "Dated repository interest, not robot-skill usage, compatibility or runtime success."
  },
  "evidence": {
    "level": "source_reviewed",
    "runtime_verification": "not_run",
    "checked_at": "2026-10-18",
    "inspection": "Verified exact source, applicable license and author/setup document bytes at a full Git commit; parsed the Python AST and reviewed this guarded operational entrypoint and its explicit license notices. No upstream code, installation, robot command, dataset, mesh or checkpoint was executed or loaded.",
    "entrypoint_kind": "python_main",
    "inspection_method": "automated_static"
  },
  "artifact": {
    "kind": "workflow",
    "format": "python",
    "workflow_role": "policy-training"
  }
}