{ "id": "openvla-vla-scripts-deploy-py", "name": "OpenVLA: REST policy inference server", "summary": "Pinned server entrypoint that loads an OpenVLA policy and serves action predictions over its upstream FastAPI REST interface. Statically reviewed source only; not run and no Hub preparation adapter.", "framework": "openvla", "kind": "official_example", "source_registry": "official-robot-workflows-v1", "capabilities": [ "policy-serving", "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/openvla/openvla", "commit": "c8f03f48af692657d3060c19588038c7220e9af9", "tag": "main", "path": "vla-scripts/deploy.py", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/vla-scripts/deploy.py", "raw_url": "https://raw.githubusercontent.com/openvla/openvla/c8f03f48af692657d3060c19588038c7220e9af9/vla-scripts/deploy.py", "sha256": "14d74c56a437058eb473f41f93a0a82ec892e16e55ed91236c5d30f4fd8adc65", "bytes": 5554, "authors": [ "Moo Jin Kim", "Karl Pertsch", "Siddharth Karamcheti" ] }, "license": { "spdx": "MIT", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/LICENSE", "sha256": "c4ee2ba5958af03d74b1d3dfa174e3749171d689fb4c23cc170749c05ae3eeb5", "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": 1105 }, "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/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/README.md", "setup_evidence": [ { "path": "README.md", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/README.md", "sha256": "ba8e9a2884e38a8000dafc98af4116512e37b0e143dad2f940100482bfd6b75c", "bytes": 38458, "purpose": "Author/setup, separate model-license declaration, training and robot evaluation instructions" }, { "path": "pyproject.toml", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/pyproject.toml", "sha256": "1ea10f21c13dd82cb9255f6edbd5ac477143937ff51d22740830e4e7d08d3b45", "bytes": 2553, "purpose": "First-party code authors, Python minimum and pinned packages" }, { "path": "requirements-min.txt", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/requirements-min.txt", "sha256": "bc8177f94a408f1c034f5efb4d34dafb29beb6d1ff9cfba6cf1f73835f7af98d", "bytes": 86, "purpose": "Minimal inference dependency pins" }, { "path": "experiments/robot/libero/libero_requirements.txt", "url": "https://github.com/openvla/openvla/blob/c8f03f48af692657d3060c19588038c7220e9af9/experiments/robot/libero/libero_requirements.txt", "sha256": "ab74171b8df14e391b7759f01c4c4a2334c9a362e91abeb67337f3a0d9447369", "bytes": 63, "purpose": "LIBERO evaluation-specific package declarations" } ] }, "requirements": { "python": ">=3.8 in project metadata; upstream setup uses Python 3.10.", "packages": [ "Pinned OpenVLA checkout", "Upstream PyTorch 2.2.0, transformers 4.40.1, timm 0.9.10 and tokenizers 0.19.1 pins", "Workflow-specific dependencies in pinned README and source" ], "display": "Hardware: Docstring describes a GPU server. Source selects cuda:0 if available and otherwise CPU, while requesting flash_attention_2 and bfloat16. CPU success and minimum VRAM are not established. Weights: Loads AutoProcessor and AutoModelForVision2Seq from the configured model path; default is openvla/openvla-7b. from_pretrained is called with trust_remote_code=True. No weights or remote model code were fetched. Datasets: Accepts caller-provided image/instruction observations; no training dataset is required by this server entrypoint. Services/auth: Starts a local HTTP API, default host 0.0.0.0/port 8000. No provider API key or authentication requirement is declared by the selected entrypoint; remote model retrieval may occur when a model ID is used.", "execution_domain": "computation", "source_imports": [ "PIL", "dataclasses", "draccus", "fastapi", "fastapi.responses", "json", "json_numpy", "logging", "os.path", "pathlib", "torch", "traceback", "transformers", "typing", "uvicorn" ], "notes": "The source starts a network listener and permits model-provided remote code when actually run. This record is inert provenance, not permission to execute it. Code licensing does not establish model, checkpoint, dataset or service permission.", "declared_requirements": { "python": ">=3.8 in project metadata; upstream setup uses Python 3.10.", "weights": "Loads AutoProcessor and AutoModelForVision2Seq from the configured model path; default is openvla/openvla-7b. from_pretrained is called with trust_remote_code=True. No weights or remote model code were fetched.", "datasets": "Accepts caller-provided image/instruction observations; no training dataset is required by this server entrypoint.", "hardware": "Docstring describes a GPU server. Source selects cuda:0 if available and otherwise CPU, while requesting flash_attention_2 and bfloat16. CPU success and minimum VRAM are not established.", "external_services_and_auth": "Starts a local HTTP API, default host 0.0.0.0/port 8000. No provider API key or authentication requirement is declared by the selected entrypoint; remote model retrieval may occur when a model ID is used.", "evidence": [ { "anchor": "self.device = torch.device", "line": 74 }, { "anchor": "trust_remote_code=True", "line": 77 }, { "anchor": "port: int = 8000", "line": 120 } ], "license_scopes": { "code": "MIT for selected first-party workflow source.", "weights": "README explicitly says the released OpenVLA models derive from Llama-2 and are subject to the Llama Community License. No checkpoint/model card/base-model license was fetched or evaluated here; MIT is asserted only for the selected first-party Python source.", "datasets": "README names BridgeData V2, Open X-Embodiment and LIBERO datasets. Individual dataset terms were not reviewed, and repository MIT is not applied to them.", "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 vla-scripts/deploy.py --openvla_path ", "notes": "The source starts a network listener and permits model-provided remote code when actually run. This record is inert provenance, not permission to execute it." }, "popularity": { "metric": "github_stars", "repository_stars": 7166, "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-serving" } }