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feat(dataset): integrate episode streaming into training
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Pepijn
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#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Benchmark the production StreamingLeRobotDataset path used by lerobot-train."""
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from __future__ import annotations
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import argparse
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import json
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import platform
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import resource
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import shutil
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import socket
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import statistics
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import subprocess
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import sys
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import time
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from pathlib import Path
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import torch
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from lerobot.datasets import StreamingLeRobotDataset
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--repo-id", required=True)
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parser.add_argument("--revision", default=None)
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parser.add_argument("--root", default=None)
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parser.add_argument("--data-root", default=None)
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parser.add_argument("--episodes", type=int, default=None, help="Use the first N episodes.")
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parser.add_argument("--batch-size", type=int, default=16)
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parser.add_argument("--num-workers", type=int, default=4)
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parser.add_argument("--prefetch-factor", type=int, default=2)
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parser.add_argument("--episode-pool-size", type=int, default=32)
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parser.add_argument("--prefetch-episodes", type=int, default=8)
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parser.add_argument("--byte-budget-gb", type=float, default=8.0)
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parser.add_argument("--warmup-batches", type=int, default=8)
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parser.add_argument("--measure-batches", type=int, default=128)
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parser.add_argument("--summary-json", type=Path, default=None)
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return parser.parse_args()
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def percentile(values: list[float], quantile: float) -> float:
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if not values:
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return 0.0
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ordered = sorted(values)
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index = round((len(ordered) - 1) * quantile)
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return ordered[index]
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def git_commit() -> str | None:
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git = shutil.which("git")
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if git is None:
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return None
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try:
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return subprocess.run(
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[git, "rev-parse", "HEAD"],
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check=True,
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capture_output=True,
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text=True,
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).stdout.strip()
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except (OSError, subprocess.CalledProcessError):
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return None
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def main_process_max_rss_mb() -> float:
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rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
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return rss / 1024**2 if sys.platform == "darwin" else rss / 1024
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def child_process_max_rss_mb() -> float:
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rss = resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss
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return rss / 1024**2 if sys.platform == "darwin" else rss / 1024
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def main() -> None:
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args = parse_args()
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episodes = list(range(args.episodes)) if args.episodes is not None else None
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init_start = time.perf_counter()
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dataset = StreamingLeRobotDataset(
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args.repo_id,
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root=args.root,
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episodes=episodes,
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revision=args.revision,
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data_root=args.data_root,
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episode_pool_size=args.episode_pool_size,
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prefetch_episodes=args.prefetch_episodes,
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byte_budget_gb=args.byte_budget_gb,
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max_num_shards=max(1, args.num_workers),
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return_uint8=True,
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)
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dataset_init_s = time.perf_counter() - init_start
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loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=args.batch_size,
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num_workers=args.num_workers,
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pin_memory=torch.cuda.is_available(),
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prefetch_factor=args.prefetch_factor if args.num_workers else None,
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persistent_workers=args.num_workers > 0,
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)
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iterator = iter(loader)
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waits: list[float] = []
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measured_samples = 0
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measured_indices: list[int] = []
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first_batch_s = 0.0
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exhausted = False
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try:
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for batch_index in range(args.warmup_batches + args.measure_batches):
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wait_start = time.perf_counter()
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try:
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batch = next(iterator)
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except StopIteration:
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exhausted = True
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break
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wait_s = time.perf_counter() - wait_start
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if batch_index == 0:
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first_batch_s = wait_s
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if batch_index >= args.warmup_batches:
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waits.append(wait_s)
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indices = batch["index"].reshape(-1).tolist()
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measured_indices.extend(int(index) for index in indices)
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measured_samples += len(indices)
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finally:
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shutdown = getattr(iterator, "_shutdown_workers", None)
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if shutdown is not None:
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shutdown()
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measured_wall_s = sum(waits)
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summary = {
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"repo_id": args.repo_id,
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"revision": str(dataset.revision),
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"git_commit": git_commit(),
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"host": socket.gethostname(),
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"platform": platform.platform(),
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"torch_version": torch.__version__,
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"dataset_init_s": dataset_init_s,
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"first_batch_s": first_batch_s,
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"measured_batches": len(waits),
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"measured_samples": measured_samples,
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"measured_wall_s": measured_wall_s,
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"samples_s": measured_samples / measured_wall_s if measured_wall_s else 0.0,
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"batch_wait_mean_ms": statistics.fmean(waits) * 1000 if waits else 0.0,
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"batch_wait_p50_ms": percentile(waits, 0.50) * 1000,
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"batch_wait_p95_ms": percentile(waits, 0.95) * 1000,
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"batch_wait_p99_ms": percentile(waits, 0.99) * 1000,
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"duplicate_indices": measured_samples - len(set(measured_indices)),
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"epoch_exhausted": exhausted,
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"main_process_max_rss_mb": main_process_max_rss_mb(),
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"worker_process_max_rss_mb": child_process_max_rss_mb(),
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"config": {
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"batch_size": args.batch_size,
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"num_workers": args.num_workers,
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"prefetch_factor": args.prefetch_factor,
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"episode_pool_size": args.episode_pool_size,
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"prefetch_episodes": args.prefetch_episodes,
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"byte_budget_gb": args.byte_budget_gb,
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"warmup_batches": args.warmup_batches,
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"measure_batches": args.measure_batches,
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},
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}
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print(json.dumps(summary, indent=2, sort_keys=True))
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if args.summary_json is not None:
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args.summary_json.parent.mkdir(parents=True, exist_ok=True)
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args.summary_json.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n")
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if __name__ == "__main__":
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main()
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