{
  "id": "diffusion-policy-train-py",
  "name": "Diffusion Policy: Configured policy training launcher",
  "summary": "Pinned upstream Hydra training entrypoint that resolves a selected workspace configuration and invokes its operational training run. Statically reviewed source only; not run and no Hub preparation adapter.",
  "framework": "diffusion-policy",
  "kind": "official_example",
  "source_registry": "official-robot-workflows-v1",
  "capabilities": [
    "policy-training",
    "robot-workflow"
  ],
  "robot_types": [
    "source-defined"
  ],
  "robot_scope": "Source-configured workflow. The selected dataset, checkpoint, simulator/controller and observation/action schema determine applicability; no specific robot names, compatibility combinations or Hub support are inferred.",
  "source": {
    "repository": "https://github.com/real-stanford/diffusion_policy",
    "commit": "5ba07ac6661db573af695b419a7947ecb704690f",
    "tag": "main",
    "path": "train.py",
    "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/train.py",
    "raw_url": "https://raw.githubusercontent.com/real-stanford/diffusion_policy/5ba07ac6661db573af695b419a7947ecb704690f/train.py",
    "sha256": "4dc8d6e876db25a62bf6c647444d7ee88f6754cda992c75728419d47f26374e5",
    "bytes": 965,
    "authors": [
      "Cheng Chi",
      "Siyuan Feng",
      "Yilun Du",
      "Zhenjia Xu",
      "Eric Cousineau",
      "Benjamin Burchfiel",
      "Shuran Song"
    ]
  },
  "license": {
    "spdx": "MIT",
    "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/LICENSE",
    "sha256": "304e99a48271e10820f71a1b86ef395c06c100d7f68dd1d308790d1064fc4d41",
    "scope": "Selected first-party Python workflow source only. Preserve upstream copyright and per-file notices. Model weights, datasets, third-party code, robot drivers/firmware and services retain separate terms; no rights for those assets are inferred.",
    "bytes": 1106
  },
  "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/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/README.md",
    "setup_evidence": [
      {
        "path": "README.md",
        "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/README.md",
        "sha256": "51b6717d694a4ea4b476cf07c64651dc58643402beb0a27cda38a87cc58334b0",
        "bytes": 25219,
        "purpose": "Upstream authors, simulation/real-robot setup, W&B login, train/evaluation usage and code license"
      },
      {
        "path": "conda_environment.yaml",
        "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/conda_environment.yaml",
        "sha256": "c945bcac09284cf897d3e1837139ea83f991e4b2298dbd3a8c4a0718a34994ea",
        "bytes": 1626,
        "purpose": "Simulation environment Python and CUDA/PyTorch declarations"
      },
      {
        "path": "conda_environment_real.yaml",
        "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/conda_environment_real.yaml",
        "sha256": "6e2c5d828e5277edb99551f6e935ac36addad462b6a71ef5b0eefe60920a67a9",
        "bytes": 2017,
        "purpose": "Real-robot environment Python, CUDA and hardware dependency declarations"
      },
      {
        "path": "conda_environment_macos.yaml",
        "url": "https://github.com/real-stanford/diffusion_policy/blob/5ba07ac6661db573af695b419a7947ecb704690f/conda_environment_macos.yaml",
        "sha256": "80c41cb42c7702f6959a5fadc177db339b0877b1a17a807660722d9db4995f0e",
        "bytes": 1183,
        "purpose": "Separate development-only environment; not benchmark parity"
      }
    ]
  },
  "requirements": {
    "python": "Python 3.9 in upstream simulation/real conda declarations.",
    "packages": [
      "Pinned diffusion_policy checkout",
      "Workflow-specific conda environment with PyTorch1.12.1 and CUDA toolkit11.6",
      "Configured simulator or real-robot dependencies"
    ],
    "display": "Hardware: README benchmark-reproduction setup specifies Linux with an NVIDIA GPU. Exact device and memory depend on chosen workspace/data; macOS is documented for development only. Weights: Checkpoint initialization/resume behavior depends on the explicitly chosen workspace configuration; no blanket external pretrained-weight requirement is declared by this dispatcher. Datasets: Requires a selected demonstration dataset/task configuration; README's Push-T example downloads a separate training dataset. Services/auth: README's documented training path explicitly logs in to W&B. The dispatcher itself delegates logging to its chosen workspace; no particular API-key variable is named.",
    "execution_domain": "computation",
    "source_imports": [
      "diffusion_policy.workspace.base_workspace",
      "hydra",
      "omegaconf",
      "pathlib",
      "sys"
    ],
    "notes": "Included because README names it as the operational training entrypoint and it calls workspace.run, not as a config-only expansion. Source registers an eval resolver, so only reviewed upstream configurations could be considered for later manual execution. Code licensing does not establish model, checkpoint, dataset or service permission.",
    "declared_requirements": {
      "python": "Python 3.9 in upstream simulation/real conda declarations.",
      "weights": "Checkpoint initialization/resume behavior depends on the explicitly chosen workspace configuration; no blanket external pretrained-weight requirement is declared by this dispatcher.",
      "datasets": "Requires a selected demonstration dataset/task configuration; README's Push-T example downloads a separate training dataset.",
      "hardware": "README benchmark-reproduction setup specifies Linux with an NVIDIA GPU. Exact device and memory depend on chosen workspace/data; macOS is documented for development only.",
      "external_services_and_auth": "README's documented training path explicitly logs in to W&B. The dispatcher itself delegates logging to its chosen workspace; no particular API-key variable is named.",
      "evidence": [
        {
          "anchor": "OmegaConf.register_new_resolver(\"eval\", eval",
          "line": 18
        },
        {
          "anchor": "workspace.run()",
          "line": 32
        }
      ],
      "license_scopes": {
        "code": "MIT for selected first-party workflow source.",
        "weights": "README links separately downloadable trained checkpoints but the reviewed repository text does not separately establish their license. Do not extend first-party-code MIT to weights.",
        "datasets": "README links separate task demonstration datasets and dependencies such as robomimic. Individual dataset licenses were not reviewed; code MIT does not grant dataset rights.",
        "third_party_code": "External dependencies retain their own licenses."
      },
      "interpretation": "Pinned upstream declarations only; no dependency resolution, model/data access, compatibility inference or runtime validation was performed."
    }
  },
  "invocation": {
    "entrypoint": "python train.py --config-name=<reviewed-training-workspace>",
    "notes": "Included because README names it as the operational training entrypoint and it calls workspace.run, not as a config-only expansion. Source registers an eval resolver, so only reviewed upstream configurations could be considered for later manual execution."
  },
  "popularity": {
    "metric": "github_stars",
    "repository_stars": 4651,
    "checked_at": "2026-10-17",
    "meaning": "Dated repository interest, not robot-skill usage, compatibility or runtime success."
  },
  "evidence": {
    "level": "source_reviewed",
    "runtime_verification": "not_run",
    "checked_at": "2026-10-17",
    "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"
  }
}