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14.7 kB
| """Studio episode/media stages on the LeRobot #4892 execution contracts. | |
| The CPU controller keeps its shared billing guard and provider semaphores in | |
| process. Only opaque in-process handles enter operational plans; tokens and | |
| provider objects are never serialized. Scientific identities exclude handles. | |
| """ | |
| import hashlib | |
| import json | |
| import shutil | |
| import tarfile | |
| import uuid | |
| from dataclasses import asdict | |
| from pathlib import Path | |
| from lerobot.annotations.runtime.configs import RuntimeConfig, StageConfig | |
| from lerobot.annotations.runtime.pipeline import run_pipeline | |
| from lerobot.annotations.runtime.types import ( | |
| Artifact, | |
| DatasetRef, | |
| InputItem, | |
| ItemResult, | |
| ModuleSpec, | |
| Outcome, | |
| fingerprint, | |
| ) | |
| from lerobot.annotations.runtime.worker import accepted_items | |
| CONTEXTS = {} | |
| def unpack(bundle, directory): | |
| with tarfile.open(bundle) as archive: | |
| for member in archive.getmembers(): | |
| path = Path(member.name) | |
| if not member.isfile() or path.is_absolute() or ".." in path.parts: | |
| raise ValueError("Invalid Studio checkpoint bundle") | |
| target = directory / path | |
| target.parent.mkdir(parents=True, exist_ok=True) | |
| target.write_bytes(archive.extractfile(member).read()) | |
| class StudioStage: | |
| def __init__(self, handle, phase, scientific): | |
| self.handle, self.phase, self.scientific = handle, phase, scientific | |
| self.spec = ModuleSpec("studio_" + phase, "1", "episode", {"bundle": None}) | |
| def scientific_config(self): | |
| return {"phase": self.phase, **self.scientific} | |
| def setup(self, context): | |
| self.values = CONTEXTS[self.handle] | |
| def teardown(self): | |
| pass | |
| def process_batch(self, items, context): | |
| import worker | |
| from lerobot.annotations.steerable_pipeline.staging import EpisodeStaging | |
| record, row, info, root, manifest, provider, media = self.values | |
| results = [] | |
| for item in items: | |
| directory = context.scratch / item.item_id | |
| directory.mkdir(parents=True, exist_ok=True) | |
| staging_dir = directory / "staging" | |
| if self.phase == "language": | |
| result = worker._annotate_episode( | |
| record, row, info, root, manifest, provider, staging_dir, None | |
| ) | |
| else: | |
| artifact = Artifact(**item.payload["upstream"]) | |
| if not context.store.verify(artifact): | |
| raise ValueError("Upstream checkpoint checksum mismatch") | |
| bundle = context.store.download(artifact, directory / "language.tar.gz") | |
| unpack(bundle, directory) | |
| bundle.unlink() | |
| result = json.loads((directory / "result.json").read_text()) | |
| if self.phase == "hands": | |
| import math | |
| import time | |
| import av | |
| import pyarrow.parquet as pq | |
| from subset import trim_video | |
| from lerobot.annotations.hands.writer import attach_actions | |
| if worker.HAND_ANNOTATOR is None: | |
| raise RuntimeError("The HF Jobs hand worker was not initialized") | |
| native = directory / "hand-native" | |
| native.mkdir(parents=True, exist_ok=True) | |
| video = native / "episode.mp4" | |
| started = time.perf_counter() | |
| print( | |
| "HAND_RUNTIME " | |
| + json.dumps( | |
| {"event": "episode_video_prepare_start", "source_episode": record.episode_index} | |
| ), | |
| flush=True, | |
| ) | |
| source_video = root / worker.video_path(row, info, manifest["camera"]) | |
| offset = float(row[f"videos/{manifest['camera']}/from_timestamp"]) | |
| # Standalone raw episode videos need no RGB re-encode. The | |
| # native writer independently verifies decoded frame count/FPS. | |
| with av.open(str(source_video)) as probe: | |
| stream = probe.streams.video[0] | |
| complete_episode = ( | |
| len(probe.streams.video) == 1 | |
| and offset == 0 | |
| and stream.start_time == 0 | |
| and stream.frames == int(row["length"]) | |
| and math.isclose(float(stream.average_rate or 0), float(info["fps"]), rel_tol=1e-6) | |
| ) | |
| if complete_episode: | |
| video = source_video | |
| else: | |
| trim_video( | |
| source_video, video, offset, int(row["length"]), float(info["fps"]), lossless=True | |
| ) | |
| prepared = time.perf_counter() | |
| print( | |
| "HAND_RUNTIME " | |
| + json.dumps( | |
| { | |
| "event": "native_annotation_start", | |
| "source_episode": record.episode_index, | |
| "video_prepare_seconds": prepared - started, | |
| "input_mode": "whole-episode-video" if complete_episode else "lossless-trim", | |
| } | |
| ), | |
| flush=True, | |
| ) | |
| try: | |
| native_report = worker.HAND_ANNOTATOR.annotate( | |
| video, | |
| manifest["hand_actions"]["calibration"], | |
| native / "work", | |
| orientation=manifest["hand_actions"].get("orientation", "sensor"), | |
| ) | |
| except Exception: | |
| # Native inference is offline and has no Hub credentials. | |
| import logging | |
| logging.exception( | |
| "Native hand annotation failed for source episode %s", record.episode_index | |
| ) | |
| raise | |
| inferred = time.perf_counter() | |
| table = pq.read_table( | |
| record.data_path, filters=[("episode_index", "=", record.episode_index)] | |
| ) | |
| table, summary = attach_actions(table, native_report, directory=native, fps=info["fps"]) | |
| summary["timing_seconds"] = { | |
| "source_video_prepare": prepared - started, | |
| "native_annotation": inferred - prepared, | |
| "native_scope": "intrinsics, rectification, camera, hands and native export; includes coordinator wait and first-use model initialization; excludes source preparation and final dataset video copying", | |
| } | |
| summary["native_components"] = { | |
| key: native_report[key] | |
| for key in ("camera_statistics", "hand_components") | |
| if key in native_report | |
| } | |
| print( | |
| "HAND_RUNTIME " | |
| + json.dumps( | |
| { | |
| "event": "native_annotation_finished", | |
| "source_episode": record.episode_index, | |
| "native_seconds": inferred - prepared, | |
| "valid_frames": summary["valid_frames"], | |
| } | |
| ), | |
| flush=True, | |
| ) | |
| pq.write_table(table, directory / "hand_actions.parquet") | |
| result["hand_actions"] = summary | |
| # Model assets and RGB/prediction caches never enter dataset uploads or checkpoints. | |
| shutil.rmtree(native) | |
| elif self.phase == "human_video": | |
| staging = EpisodeStaging(staging_dir, record.episode_index) | |
| frames = worker.SourceFrames(row, info, root, manifest["camera"]) | |
| full = worker.SourceFrames(row, info, root, manifest["camera"], max_size=None) | |
| try: | |
| entries = worker.generate_human_videos( | |
| record, | |
| staging, | |
| provider, | |
| frames, | |
| full, | |
| media, | |
| directory / "human_videos" / f"episode_{record.episode_index:06d}", | |
| manifest["human_video"]["segments_per_episode"], | |
| ) | |
| except worker.BudgetExceededError as exc: | |
| entries = [ | |
| { | |
| "source_episode_index": record.episode_index, | |
| "status": "time_stopped" | |
| if isinstance(exc, worker.DeadlineExceededError) | |
| else "budget_stopped", | |
| "files": {}, | |
| } | |
| ] | |
| for entry in entries: | |
| entry["files"] = { | |
| kind: str(Path(path).relative_to(directory)) | |
| for kind, path in entry.get("files", {}).items() | |
| } | |
| result["human_videos"] = worker.human_video_summary(entries) | |
| result["human_video_entries"] = entries | |
| (directory / "result.json").write_text(json.dumps(result)) | |
| bundle = context.scratch / (item.item_id + ".tar.gz") | |
| with tarfile.open(bundle, "w:gz") as archive: | |
| for path in sorted(directory.rglob("*")): | |
| if path.is_file(): | |
| archive.add(path, arcname=str(path.relative_to(directory))) | |
| artifact = context.write_asset(item, "bundle", bundle) | |
| results.append(ItemResult(item.item_id, Outcome.COMPLETED, (artifact,))) | |
| return results | |
| class EpisodeSource: | |
| def __init__(self, record, row, info, manifest): | |
| self.record, self.row = record, row | |
| self.identity = { | |
| "row": row, | |
| "fps": info["fps"], | |
| "task": record.episode_task, | |
| "source": manifest.get("dataset"), | |
| "revision": manifest.get("dataset_revision"), | |
| } | |
| revision = manifest.get("dataset_revision") or fingerprint(self.identity) | |
| self.dataset_ref = DatasetRef(manifest.get("dataset", "studio-local"), revision) | |
| def discover(self, stage, store, upstream): | |
| payload = {} | |
| identity = dict(self.identity) | |
| if upstream: | |
| plan, _ = next(iter(upstream.values())) | |
| _, result = next(accepted_items(store, plan)) | |
| artifact = result.artifacts[0] | |
| payload["upstream"] = asdict(artifact) | |
| identity["upstream"] = {key: value for key, value in asdict(artifact).items() if key != "path"} | |
| yield InputItem(str(self.record.episode_index), payload, content_digest=fingerprint(identity)) | |
| def run_episode(record, row, info, root, manifest, provider, staging_dir, media=None): | |
| handle = uuid.uuid4().hex | |
| CONTEXTS[handle] = (record, row, info, root, manifest, provider, media) | |
| folder = Path(__file__).parent | |
| scientific = { | |
| "options": { | |
| key: manifest.get(key) | |
| for key in ( | |
| "features", | |
| "camera", | |
| "vqa_hz", | |
| "prompts", | |
| "model", | |
| "generation_options", | |
| "human_video", | |
| "captioner", | |
| "captioner_profile", | |
| "flora_execution", | |
| "hand_actions", | |
| ) | |
| }, | |
| "code": { | |
| path.name: hashlib.sha256(path.read_bytes()).hexdigest() | |
| for pattern in ("*.py", "*.txt", "pipeline-version.json") | |
| for path in sorted(folder.glob(pattern)) | |
| if not path.name.startswith("test_") | |
| }, | |
| } | |
| stages = [ | |
| StageConfig( | |
| "language", | |
| "studio_runtime:StudioStage", | |
| {"handle": handle, "phase": "language", "scientific": scientific}, | |
| ) | |
| ] | |
| if media is not None: | |
| stages.append( | |
| StageConfig( | |
| "human_video", | |
| "studio_runtime:StudioStage", | |
| {"handle": handle, "phase": "human_video", "scientific": scientific}, | |
| ("language",), | |
| ) | |
| ) | |
| if manifest.get("hand_actions"): | |
| stages.append( | |
| StageConfig( | |
| "hands", | |
| "studio_runtime:StudioStage", | |
| {"handle": handle, "phase": "hands", "scientific": scientific}, | |
| (stages[-1].id,), | |
| ) | |
| ) | |
| # Existing Studio threads own concurrency; one in-process runtime worker | |
| # shares the same request budget. Accepted media is never automatically rebilled. | |
| runtime = RuntimeConfig( | |
| run_uri=str(staging_dir.parent / "runtime" / f"episode_{record.episode_index:06d}"), | |
| workers=1, | |
| batch_size=1, | |
| shard_size=1, | |
| max_retries=0, | |
| ) | |
| try: | |
| store, completed = run_pipeline(EpisodeSource(record, row, info, manifest), stages, runtime) | |
| plan, _ = completed[stages[-1].id] | |
| _, result = next(accepted_items(store, plan)) | |
| directory = staging_dir.parent / "restored" / handle | |
| directory.mkdir(parents=True, exist_ok=True) | |
| bundle = store.download(result.artifacts[0], directory / "result.tar.gz") | |
| unpack(bundle, directory) | |
| result = json.loads((directory / "result.json").read_text()) | |
| if (directory / "hand_actions.parquet").exists(): | |
| target = staging_dir.parent / "hand_actions" / f"episode_{record.episode_index:06d}.parquet" | |
| target.parent.mkdir(parents=True, exist_ok=True) | |
| shutil.copyfile(directory / "hand_actions.parquet", target) | |
| if (directory / "staging").exists(): | |
| shutil.rmtree(staging_dir / f"episode_{record.episode_index:06d}", ignore_errors=True) | |
| shutil.copytree(directory / "staging", staging_dir, dirs_exist_ok=True) | |
| for entry in result.get("human_video_entries", []): | |
| for kind, relative in entry.get("files", {}).items(): | |
| source = directory / relative | |
| target = staging_dir.parent / relative | |
| target.parent.mkdir(parents=True, exist_ok=True) | |
| shutil.copyfile(source, target) | |
| entry["files"][kind] = str(target) | |
| result["runtime"] = {name: summary.plan_id for name, (_, summary) in completed.items()} | |
| shutil.rmtree(directory) | |
| return result | |
| finally: | |
| CONTEXTS.pop(handle, None) | |