Harden production dataset streaming pipeline

docs/source/lerobot-dataset-v3.mdx

@@ -178,7 +178,9 @@ To capture comparable data-pipeline results on a training host:
 uv run python scripts/bench_streaming_dataset.py \
   --repo-id=yaak-ai/L2D-v3 \
   --batch-size=16 \
-  --num-workers=4 \
+  --num-workers=1 \
+  --fetch-workers=4 \
+  --decode-threads=2 \
   --summary-json=streaming-benchmark.json
 ```
 

docs/source/training_dataset_streaming.mdx

@@ -59,10 +59,15 @@ Subset sidecars cannot be published.
 The production defaults are:
 
 | Option                              | Default | Meaning                                             |
-| ----------------------------- | ------: | --------------------------------------------------- |
+| ----------------------------------- | ------: | --------------------------------------------------- |
 | `streaming_episode_pool_size`       |      32 | Maximum complete episodes mixed by each rank        |
 | `streaming_prefetch_episodes`       |       8 | Episodes fetched ahead of the active pool           |
 | `streaming_byte_budget_gb`          |       8 | Maximum synthesized MP4 bytes per rank              |
+| `streaming_decode_threads`          |       2 | Parallel sample assembly and video decode workers   |
+| `streaming_decoded_queue_size`      |       8 | Decoded samples buffered ahead, in planner order    |
+| `streaming_max_open_decoders`       |      64 | Independent open-decoder LRU cap per rank           |
+| `streaming_native_http_connections` |   unset | Native HTTP connection cap per rank                 |
+| `streaming_native_http_subranges`   |       1 | Concurrent subranges per native HTTP range read     |
 | `streaming_data_root`               |   unset | Optional local, Hub, bucket, or fsspec payload root |
 
 The active episode set is capped by both episode count and the exact synthesized mini-MP4 sizes
@@ -77,6 +82,9 @@ lerobot-train \
   --dataset.streaming_episode_pool_size=16 \
   --dataset.streaming_prefetch_episodes=4 \
   --dataset.streaming_byte_budget_gb=4 \
+  --dataset.streaming_decode_threads=2 \
+  --dataset.streaming_decoded_queue_size=8 \
+  --dataset.streaming_max_open_decoders=64 \
   --num_workers=4 \
   --policy.type=act \
   --output_dir=outputs/train/act_streaming
@@ -109,6 +117,8 @@ uv run python scripts/bench_streaming_dataset.py \
   --batch-size=16 \
   --num-workers=1 \
   --fetch-workers=4 \
+  --decode-threads=2 \
+  --decoded-queue-size=8 \
   --summary-json=streaming-benchmark.json
 ```
 

scripts/bench_episode_byte_cache.py

@@ -70,7 +70,7 @@ def parse_args() -> argparse.Namespace:
         type=int,
         default=8,
         help="Concurrent camera-fetch jobs. Total connections ~= workers x range-subranges; "
-        "the HF bucket path saturates around 64 connections per host, so keep the product near 64.",
+        "measure the host-specific saturation point before increasing this value.",
     )
     parser.add_argument(
         "--range-subranges",
@@ -443,6 +443,7 @@ def run_exact_coverage_stream(
             decode_pool.shutdown(wait=True)
 
     elapsed = time.perf_counter() - start
+    replacements = max(0, pool.admitted_count - min(pool_size, len(order)))
     result = {
         "target_samples_s": target_samples_s,
         "actual_samples_s": samples_done / elapsed if elapsed > 0 else float("inf"),
@@ -453,6 +454,10 @@ def run_exact_coverage_stream(
         "shard_total_frames": float(total_frames),
         "epoch_complete": 1.0 if epoch_complete else 0.0,
         "prefetch_ahead": float(prefetch_ahead),
+        "prefetch_episodes": float(prefetch_ahead),
+        "replacements": float(replacements),
+        "replacement_episodes_s": replacements / elapsed if elapsed > 0 else float("inf"),
+        "samples_per_episode": samples_done / replacements if replacements else float(samples_done),
         "batch_size": float(batch_size),
         "decode_workers": float(decode_workers),
         "kept_up": 1.0

scripts/bench_streaming_dataset.py

@@ -50,6 +50,11 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--episode-pool-size", type=int, default=32)
     parser.add_argument("--prefetch-episodes", type=int, default=8)
     parser.add_argument("--byte-budget-gb", type=float, default=8.0)
+    parser.add_argument("--decode-threads", type=int, default=2)
+    parser.add_argument("--decoded-queue-size", type=int, default=8)
+    parser.add_argument("--max-open-decoders", type=int, default=64)
+    parser.add_argument("--native-http-connections", type=int, default=None)
+    parser.add_argument("--native-http-subranges", type=int, default=1)
     parser.add_argument("--warmup-batches", type=int, default=8)
     parser.add_argument("--measure-batches", type=int, default=128)
     parser.add_argument("--summary-json", type=Path, default=None)
@@ -105,6 +110,11 @@ def main() -> None:
         episode_pool_size=args.episode_pool_size,
         prefetch_episodes=args.prefetch_episodes,
         byte_budget_gb=args.byte_budget_gb,
+        decode_threads=args.decode_threads,
+        decoded_queue_size=args.decoded_queue_size,
+        max_open_decoders=args.max_open_decoders,
+        native_http_connections=args.native_http_connections,
+        native_http_subranges=args.native_http_subranges,
         max_num_shards=max(1, args.fetch_workers),
         return_uint8=True,
     )
@@ -184,6 +194,11 @@ def main() -> None:
             "episode_pool_size": args.episode_pool_size,
             "prefetch_episodes": args.prefetch_episodes,
             "byte_budget_gb": args.byte_budget_gb,
+            "decode_threads": args.decode_threads,
+            "decoded_queue_size": args.decoded_queue_size,
+            "max_open_decoders": args.max_open_decoders,
+            "native_http_connections": args.native_http_connections,
+            "native_http_subranges": args.native_http_subranges,
             "warmup_batches": args.warmup_batches,
             "measure_batches": args.measure_batches,
         },

src/lerobot/configs/default.py

@@ -52,6 +52,14 @@ class DatasetConfig:
     streaming_prefetch_episodes: int = 8
     # Hard per-rank cap for synthesized episode-video bytes.
     streaming_byte_budget_gb: float = 8.0
+    # Parallel sample assembly/decode workers and their bounded in-order result queue.
+    streaming_decode_threads: int = 2
+    streaming_decoded_queue_size: int = 8
+    # Independent decoder-state cap.
+    streaming_max_open_decoders: int = 64
+    # Per-rank native HTTP limits. None preserves the fetcher's worker-derived default.
+    streaming_native_http_connections: int | None = None
+    streaming_native_http_subranges: int = 1
     # Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
     eval_split: float = 0.0
 
@@ -68,6 +76,16 @@ class DatasetConfig:
             raise ValueError("streaming_prefetch_episodes must be non-negative")
         if self.streaming_byte_budget_gb <= 0:
             raise ValueError("streaming_byte_budget_gb must be positive")
+        if self.streaming_decode_threads <= 0:
+            raise ValueError("streaming_decode_threads must be positive")
+        if self.streaming_decoded_queue_size <= 0:
+            raise ValueError("streaming_decoded_queue_size must be positive")
+        if self.streaming_max_open_decoders <= 0:
+            raise ValueError("streaming_max_open_decoders must be positive")
+        if self.streaming_native_http_connections is not None and self.streaming_native_http_connections <= 0:
+            raise ValueError("streaming_native_http_connections must be positive")
+        if self.streaming_native_http_subranges <= 0:
+            raise ValueError("streaming_native_http_subranges must be positive")
         if self.episodes is not None:
             if any(ep < 0 for ep in self.episodes):
                 raise ValueError(

src/lerobot/datasets/factory.py

@@ -119,6 +119,11 @@ def make_dataset(
                 episode_pool_size=cfg.dataset.streaming_episode_pool_size,
                 prefetch_episodes=cfg.dataset.streaming_prefetch_episodes,
                 byte_budget_gb=cfg.dataset.streaming_byte_budget_gb,
+                decode_threads=cfg.dataset.streaming_decode_threads,
+                decoded_queue_size=cfg.dataset.streaming_decoded_queue_size,
+                max_open_decoders=cfg.dataset.streaming_max_open_decoders,
+                native_http_connections=cfg.dataset.streaming_native_http_connections,
+                native_http_subranges=cfg.dataset.streaming_native_http_subranges,
                 repeat=True,
             )
     else:
@@ -210,6 +215,11 @@ def make_train_eval_datasets(
             episode_pool_size=cfg.dataset.streaming_episode_pool_size,
             prefetch_episodes=cfg.dataset.streaming_prefetch_episodes,
             byte_budget_gb=cfg.dataset.streaming_byte_budget_gb,
+            decode_threads=cfg.dataset.streaming_decode_threads,
+            decoded_queue_size=cfg.dataset.streaming_decoded_queue_size,
+            max_open_decoders=cfg.dataset.streaming_max_open_decoders,
+            native_http_connections=cfg.dataset.streaming_native_http_connections,
+            native_http_subranges=cfg.dataset.streaming_native_http_subranges,
             repeat=True,
         )
     else:

src/lerobot/datasets/streaming_dataset.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 import io
 import os
+from collections import deque
 from collections.abc import Callable, Iterator, Mapping
 from concurrent.futures import Future, ThreadPoolExecutor
 from pathlib import Path
@@ -122,6 +123,11 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         repeat: bool = False,
         *,
         token: str | bool | None = None,
+        decode_threads: int = 2,
+        decoded_queue_size: int = 8,
+        max_open_decoders: int = 64,
+        native_http_connections: int | None = None,
+        native_http_subranges: int = 1,
     ):
         """Initialize a StreamingLeRobotDataset.
 
@@ -152,6 +158,13 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             episode_pool_size (int | None, optional): Number of complete episodes in the sampling pool.
             prefetch_episodes (int, optional): Episodes prefetched beyond the active pool.
             byte_budget_gb (float, optional): Per-rank upper bound for synthesized episode video bytes.
+            decode_threads (int, optional): Parallel sample assembly and video decode workers.
+            decoded_queue_size (int, optional): Maximum number of decoded samples produced ahead
+                of the consumer. Results are yielded in exact planner order.
+            max_open_decoders (int, optional): Maximum number of open video decoders per rank.
+            native_http_connections (int | None, optional): Native HTTP connection limit per rank.
+                ``None`` preserves the fetcher's worker-derived default.
+            native_http_subranges (int, optional): Concurrent HTTP subranges per video range read.
             repeat (bool, optional): Repeat rank-local exact-coverage epochs without yielding a
                 short final training batch. The training factory enables this; direct iteration is
                 finite by default.
@@ -196,9 +209,24 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             raise ValueError("prefetch_episodes must be non-negative")
         if byte_budget_gb <= 0:
             raise ValueError("byte_budget_gb must be positive")
+        if decode_threads <= 0:
+            raise ValueError("decode_threads must be positive")
+        if decoded_queue_size <= 0:
+            raise ValueError("decoded_queue_size must be positive")
+        if max_open_decoders <= 0:
+            raise ValueError("max_open_decoders must be positive")
+        if native_http_connections is not None and native_http_connections <= 0:
+            raise ValueError("native_http_connections must be positive")
+        if native_http_subranges <= 0:
+            raise ValueError("native_http_subranges must be positive")
         self.episode_pool_size = episode_pool_size or min(buffer_size, 32)
         self.prefetch_episodes = prefetch_episodes
         self.byte_budget = int(byte_budget_gb * 1024**3)
+        self.decode_threads = decode_threads
+        self.decoded_queue_size = decoded_queue_size
+        self.max_open_decoders = max_open_decoders
+        self.native_http_connections = native_http_connections
+        self.native_http_subranges = native_http_subranges
         self.repeat = repeat
         self._next_epoch = 0
         self._active_epoch = 0
@@ -356,8 +384,13 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             columns=self._projected_columns,
             token=self._streaming_io_token,
         )
-        executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="lerobot-parquet")
+        parquet_executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="lerobot-parquet")
+        decode_executor = ThreadPoolExecutor(
+            max_workers=self.decode_threads,
+            thread_name_prefix="lerobot-decode",
+        )
         episode_futures: dict[int, Future[datasets.Dataset]] = {}
+        decoded_futures: deque[Future[dict]] = deque()
         scheduled_episodes: set[int] = set()
         retained_video_episodes: set[int] = set()
         if video_cache is not None:
@@ -368,7 +401,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         def submit(episode_index: int) -> Future[datasets.Dataset]:
             future = episode_futures.get(episode_index)
             if future is None:
-                future = executor.submit(self._load_episode_dataset, parquet_reader, episode_index)
+                future = parquet_executor.submit(self._load_episode_dataset, parquet_reader, episode_index)
                 episode_futures[episode_index] = future
             return future
 
@@ -382,24 +415,20 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
                     video_cache.submit_prefetch(episode_index)
                 scheduled_episodes.add(episode_index)
 
-        schedule_frontier()
-        try:
-            while True:
-                try:
-                    episode_index, frame_index = next(planner)
-                except StopIteration:
-                    self._active_epoch = epoch + 1 if self.shuffle else 0
-                    self._state_offset = 0
-                    break
-
-                episode_dataset = submit(episode_index).result()
-                item = self._make_episode_item(
-                    episode_dataset,
+        def decode_item(
+            episode_future: Future[datasets.Dataset],
+            episode_index: int,
+            frame_index: int,
+        ) -> dict:
+            return self._make_episode_item(
+                episode_future.result(),
                 episode_index,
                 frame_index,
                 video_cache=video_cache,
+                apply_image_transforms=False,
             )
 
+        def update_frontier() -> None:
             for evicted_episode in planner.evicted:
                 episode_futures.pop(evicted_episode, None)
                 if video_cache is not None and evicted_episode in retained_video_episodes:
@@ -414,12 +443,55 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             planner.newly_admitted.clear()
             schedule_frontier()
 
+        try:
+            schedule_frontier()
+            planner_exhausted = False
+            while decoded_futures or not planner_exhausted:
+                while not planner_exhausted and len(decoded_futures) < self.decoded_queue_size:
+                    try:
+                        episode_index, frame_index = next(planner)
+                    except StopIteration:
+                        planner_exhausted = True
+                        break
+
+                    episode_future = submit(episode_index)
+                    if video_cache is not None:
+                        video_cache.retain_episode(episode_index)
+                    try:
+                        decoded_future = decode_executor.submit(
+                            decode_item,
+                            episode_future,
+                            episode_index,
+                            frame_index,
+                        )
+                    except Exception:
+                        if video_cache is not None:
+                            video_cache.release_episode(episode_index)
+                        raise
+                    if video_cache is not None:
+                        decoded_future.add_done_callback(
+                            lambda _future, retained_episode=episode_index, retained_cache=video_cache: (
+                                retained_cache.release_episode(retained_episode)
+                            )
+                        )
+                    decoded_futures.append(decoded_future)
+                    update_frontier()
+
+                if not decoded_futures:
+                    continue
+                item = decoded_futures.popleft().result()
+                self._apply_image_transforms(item)
                 self._state_offset += 1
                 yield item
+            self._active_epoch = epoch + 1 if self.shuffle else 0
+            self._state_offset = 0
         finally:
+            for future in decoded_futures:
+                future.cancel()
+            decode_executor.shutdown(wait=True, cancel_futures=True)
             for future in episode_futures.values():
                 future.cancel()
-            executor.shutdown(wait=True, cancel_futures=True)
+            parquet_executor.shutdown(wait=True, cancel_futures=True)
             if video_cache is not None:
                 video_cache.close()
 
@@ -483,14 +555,15 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             sidecar_path=self._sidecar_path,
             token=self._streaming_io_token,
         )
-        decoder_limit = max(1, min(64, self.episode_pool_size * max(1, len(self.meta.video_keys))))
         return EpisodeByteCache(
             manifest,
             self._data_root,
             byte_budget=self.byte_budget,
             workers=workers,
             range_backend=range_backend,
-            max_open_decoders=decoder_limit,
+            native_http_connections=self.native_http_connections,
+            native_http_subranges=self.native_http_subranges,
+            max_open_decoders=self.max_open_decoders,
             video_backend=self._video_backend,
             tolerance_s=self.tolerance_s,
             token=self._streaming_io_token,
@@ -503,6 +576,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         frame_index: int,
         *,
         video_cache: EpisodeByteCache | None,
+        apply_image_transforms: bool = True,
     ) -> dict:
         item = episode_dataset[frame_index]
         episode = self.meta.episodes[episode_index]
@@ -563,11 +637,8 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
                         frames = frames.to(torch.float32) / 255.0
                 item[video_key] = frames.squeeze(0)
 
-        if self.image_transforms is not None:
-            for camera_key in self.meta.camera_keys:
-                if camera_key in self.meta.depth_keys:
-                    continue
-                item[camera_key] = self.image_transforms(item[camera_key])
+        if apply_image_transforms:
+            self._apply_image_transforms(item)
 
         for key, stored_unit in self._image_depth_units.items():
             if key in item and stored_unit is not None and stored_unit != self._depth_output_unit:
@@ -585,6 +656,14 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             )
         return item
 
+    def _apply_image_transforms(self, item: dict) -> None:
+        if self.image_transforms is None:
+            return
+        for camera_key in self.meta.camera_keys:
+            if camera_key in self.meta.depth_keys:
+                continue
+            item[camera_key] = self.image_transforms(item[camera_key])
+
     def state_dict(self) -> dict[str, int]:
         return {
             "epoch": self._active_epoch,

src/lerobot/streaming/episode_cache.py

@@ -75,6 +75,7 @@ class EpisodeByteCache:
         self._pool = ThreadPoolExecutor(max_workers=workers)
         self._cache: OrderedDict[tuple[int, str], dict[str, Any]] = OrderedDict()
         self._decoders: OrderedDict[tuple[int, str], Any] = OrderedDict()
+        self._decoder_locks: dict[tuple[int, str], threading.Lock] = {}
         self._futures: dict[tuple[int, str], Future[dict[str, Any]]] = {}
         self._retained_episodes: dict[int, int] = {}
         self._decoder_fallback_count = 0
@@ -96,6 +97,7 @@ class EpisodeByteCache:
             decoders = list(self._decoders.values())
             self._cache.clear()
             self._decoders.clear()
+            self._decoder_locks.clear()
             self._futures.clear()
             self._retained_episodes.clear()
             self._fallback_decoders.clear()
@@ -186,6 +188,7 @@ class EpisodeByteCache:
             self._decoders[key] = decoder
             while len(self._decoders) > self.max_open_decoders:
                 evicted_key, evicted_decoder = self._decoders.popitem(last=False)
+                self._decoder_locks.pop(evicted_key, None)
                 self._fallback_decoders.discard(evicted_key)
                 _close_decoder(evicted_decoder)
         return decoder
@@ -227,7 +230,9 @@ class EpisodeByteCache:
         decoder, release = self._decoder_for_frames(episode_index, camera_key)
         with self._lock:
             uses_pyav_timestamps = self.video_backend == "pyav" and key not in self._fallback_decoders
+            decode_lock = self._decoder_locks.setdefault(key, threading.Lock())
         try:
+            with decode_lock:
                 if uses_pyav_timestamps:
                     return decoder.get_frames_played_at(local_ts, tolerance_s=self.tolerance_s).data
                 metadata = decoder.metadata
@@ -235,7 +240,13 @@ class EpisodeByteCache:
                 if fps is None:
                     duration = max(getattr(metadata, "end_stream_seconds", 0.0), 1e-9)
                     fps = metadata.num_frames / duration
-            return decoder.get_frames_at(indices=[round(ts * fps) for ts in local_ts]).data
+                num_frames = getattr(metadata, "num_frames", None)
+                if num_frames is None:
+                    duration = max(getattr(metadata, "end_stream_seconds", 0.0), 1e-9)
+                    num_frames = round(duration * fps)
+                last_index = max(0, int(num_frames) - 1)
+                indices = [min(max(round(ts * fps), 0), last_index) for ts in local_ts]
+                return decoder.get_frames_at(indices=indices).data
         finally:
             if release is not None:
                 release()
@@ -257,6 +268,7 @@ class EpisodeByteCache:
                 with self._lock:
                     if self._decoders.get(key) is decoder:
                         self._decoders.pop(key)
+                        self._decoder_locks.pop(key, None)
                         self._fallback_decoders.discard(key)
                 continue
             return decoder, decoder.release
@@ -330,6 +342,7 @@ class EpisodeByteCache:
             entry = self._cache.pop(key)
             self._bytes -= len(entry["bytes"])
             decoder = self._decoders.pop(key, None)
+            self._decoder_locks.pop(key, None)
             self._fallback_decoders.discard(key)
             if decoder is not None:
                 _close_decoder(decoder)

src/lerobot/streaming/episode_parquet.py

@@ -11,8 +11,11 @@
 from __future__ import annotations
 
 import posixpath
+import threading
+import time
 from collections.abc import Sequence
 from pathlib import Path
+from typing import Any
 
 import fsspec
 import pyarrow as pa
@@ -29,6 +32,8 @@ class EpisodeParquetReader:
         *,
         columns: Sequence[str],
         token: str | bool | None = None,
+        max_retries: int = 4,
+        retry_backoff_s: float = 0.05,
     ):
         if not columns:
             raise ValueError("EpisodeParquetReader requires at least one projected column")
@@ -37,6 +42,13 @@ class EpisodeParquetReader:
             self.columns if "episode_index" in self.columns else (*self.columns, "episode_index")
         )
         data_root_str = str(data_root)
+        if max_retries < 0:
+            raise ValueError("max_retries must be non-negative")
+        if retry_backoff_s < 0:
+            raise ValueError("retry_backoff_s must be non-negative")
+        self._max_retries = max_retries
+        self._retry_backoff_s = retry_backoff_s
+        self._open_lock = threading.Lock() if data_root_str.startswith("hf://") else None
         storage_options = {"token": token} if token is not None and data_root_str.startswith("hf://") else {}
         self._filesystem, self._root_path = fsspec.core.url_to_fs(data_root_str, **storage_options)
 
@@ -51,7 +63,7 @@ class EpisodeParquetReader:
             raise ValueError(f"Episode {episode_index} must contain at least one row")
 
         path = posixpath.join(self._root_path.rstrip("/"), str(relative_path).lstrip("/"))
-        with self._filesystem.open(path, "rb") as source:
+        with self._open_with_retry(path) as source:
             parquet = pq.ParquetFile(source)
             available = set(parquet.schema_arrow.names)
             missing = sorted(set(self._read_columns) - available)
@@ -71,6 +83,20 @@ class EpisodeParquetReader:
             table = table.drop_columns(["episode_index"])
         return table
 
+    def _open_with_retry(self, path: str) -> Any:
+        for attempt in range(self._max_retries + 1):
+            try:
+                if self._open_lock is None:
+                    return self._filesystem.open(path, "rb")
+                with self._open_lock:
+                    return self._filesystem.open(path, "rb")
+            except FileNotFoundError:
+                if attempt == self._max_retries:
+                    raise
+                self._filesystem.invalidate_cache(posixpath.dirname(path))
+                time.sleep(self._retry_backoff_s * 2**attempt)
+        raise RuntimeError("unreachable")
+
     @staticmethod
     def _matching_row_group(parquet: pq.ParquetFile, episode_index: int) -> int | None:
         episode_column = next(

tests/configs/test_default.py

@@ -44,6 +44,11 @@ def test_dataset_config_empty_episodes_ok():
         ("streaming_episode_pool_size", 0, "episode_pool_size"),
         ("streaming_prefetch_episodes", -1, "prefetch_episodes"),
         ("streaming_byte_budget_gb", 0, "byte_budget_gb"),
+        ("streaming_decode_threads", 0, "decode_threads"),
+        ("streaming_decoded_queue_size", 0, "decoded_queue_size"),
+        ("streaming_max_open_decoders", 0, "max_open_decoders"),
+        ("streaming_native_http_connections", 0, "native_http_connections"),
+        ("streaming_native_http_subranges", 0, "native_http_subranges"),
     ],
 )
 def test_dataset_config_rejects_invalid_streaming_resource_limits(field, value, message):

tests/datasets/test_episode_parquet_reader.py

@@ -122,3 +122,33 @@ def test_reader_forwards_explicit_token_to_hf_filesystem(monkeypatch) -> None:
     )
 
     url_to_fs.assert_called_once_with("hf://datasets/private@revision", token="hf_test_token")
+
+
+def test_reader_retries_transient_remote_file_not_found(tmp_path: Path, monkeypatch) -> None:
+    path = tmp_path / "episode.parquet"
+    pq.write_table(_table([0, 0]), path)
+    filesystem = Mock()
+    attempts = 0
+
+    def open_remote(*_args, **_kwargs):
+        nonlocal attempts
+        attempts += 1
+        if attempts == 1:
+            raise FileNotFoundError("partial HfFileSystem dircache")
+        return path.open("rb")
+
+    filesystem.open.side_effect = open_remote
+    filesystem.invalidate_cache = Mock()
+    monkeypatch.setattr(fsspec.core, "url_to_fs", Mock(return_value=(filesystem, "root")))
+    reader = EpisodeParquetReader(
+        "hf://datasets/private@revision",
+        columns=("episode_index", "frame_index"),
+        max_retries=1,
+        retry_backoff_s=0,
+    )
+
+    table = reader.read_episode("data/file.parquet", episode_index=0, expected_rows=2)
+
+    assert len(table) == 2
+    assert attempts == 2
+    filesystem.invalidate_cache.assert_called_once()

tests/datasets/test_episode_video_streaming.py

@@ -11,6 +11,7 @@
 import json
 import struct
 import threading
+import time
 from concurrent.futures import ThreadPoolExecutor
 
 import numpy as np
@@ -217,6 +218,66 @@ def test_decoder_falls_back_to_pyav_when_torchcodec_rejects_mini_mp4(monkeypatch
         assert cache.decoder_fallback_count == 1
 
 
+def test_torchcodec_frame_indices_are_clamped_to_decoder_bounds(monkeypatch, tmp_path):
+    requested_indices = []
+
+    class FakeDecoder:
+        metadata = type("Metadata", (), {"average_fps": 30.0, "num_frames": 10})()
+
+        def get_frames_at(self, *, indices):
+            requested_indices.extend(indices)
+            return type("Frames", (), {"data": indices})()
+
+    with _fake_cache(monkeypatch, tmp_path) as cache:
+        monkeypatch.setattr(
+            cache.manifest,
+            "lookup",
+            lambda *_args: type("Span", (), {"source_start_pts": 0.0})(),
+        )
+        monkeypatch.setattr(cache, "_decoder_for_frames", lambda *_args: (FakeDecoder(), None))
+
+        cache.get_frames(0, "camera", [-0.1, 1.0])
+
+    assert requested_indices == [0, 9]
+
+
+def test_frame_reads_serialize_access_to_each_decoder(monkeypatch, tmp_path):
+    state_lock = threading.Lock()
+    active = 0
+    max_active = 0
+
+    class FakeDecoder:
+        metadata = type("Metadata", (), {"average_fps": 30.0, "num_frames": 10})()
+
+        def get_frames_at(self, *, indices):
+            nonlocal active, max_active
+            with state_lock:
+                active += 1
+                max_active = max(max_active, active)
+            try:
+                time.sleep(0.01)
+                return type("Frames", (), {"data": indices})()
+            finally:
+                with state_lock:
+                    active -= 1
+
+    with _fake_cache(monkeypatch, tmp_path) as cache:
+        monkeypatch.setattr(
+            cache.manifest,
+            "lookup",
+            lambda *_args: type("Span", (), {"source_start_pts": 0.0})(),
+        )
+        decoder = FakeDecoder()
+        monkeypatch.setattr(cache, "_decoder_for_frames", lambda *_args: (decoder, None))
+
+        with ThreadPoolExecutor(max_workers=2) as executor:
+            futures = [executor.submit(cache.get_frames, 0, "camera", [0.1]) for _ in range(2)]
+            for future in futures:
+                future.result()
+
+    assert max_active == 1
+
+
 def test_releasing_episode_allows_immediate_eviction(monkeypatch, tmp_path):
     with _fake_cache(monkeypatch, tmp_path, byte_budget=5) as cache:
         cache.retain_episode(0)

tests/datasets/test_streaming_factory.py

@@ -38,6 +38,11 @@ def test_factory_wires_production_streaming_settings(monkeypatch):
         streaming_episode_pool_size=7,
         streaming_prefetch_episodes=3,
         streaming_byte_budget_gb=2.5,
+        streaming_decode_threads=2,
+        streaming_decoded_queue_size=5,
+        streaming_max_open_decoders=17,
+        streaming_native_http_connections=9,
+        streaming_native_http_subranges=3,
     )
     cfg = SimpleNamespace(
         dataset=dataset_config,
@@ -54,6 +59,11 @@ def test_factory_wires_production_streaming_settings(monkeypatch):
     assert captured["kwargs"]["episode_pool_size"] == 7
     assert captured["kwargs"]["prefetch_episodes"] == 3
     assert captured["kwargs"]["byte_budget_gb"] == 2.5
+    assert captured["kwargs"]["decode_threads"] == 2
+    assert captured["kwargs"]["decoded_queue_size"] == 5
+    assert captured["kwargs"]["max_open_decoders"] == 17
+    assert captured["kwargs"]["native_http_connections"] == 9
+    assert captured["kwargs"]["native_http_subranges"] == 3
     assert captured["kwargs"]["max_num_shards"] == 1
     assert captured["kwargs"]["video_backend"] == "pyav"
     assert captured["kwargs"]["return_uint8"] is True

tests/datasets/test_streaming_production.py

@@ -10,6 +10,8 @@
 
 from __future__ import annotations
 
+import threading
+import time
 from itertools import islice
 from pathlib import Path
 
@@ -61,6 +63,61 @@ def test_streaming_matches_map_style_with_exact_coverage(tmp_path: Path, lerobot
         _assert_item_equal(sample, map_dataset[int(sample["index"])])
 
 
+def test_parallel_decode_queue_preserves_planner_order(
+    tmp_path: Path,
+    lerobot_dataset_factory,
+    monkeypatch,
+) -> None:
+    root = tmp_path / "dataset"
+    lerobot_dataset_factory(
+        root=root,
+        repo_id=DUMMY_REPO_ID,
+        total_episodes=4,
+        total_frames=40,
+        use_videos=False,
+    )
+    expected = _indices(
+        StreamingLeRobotDataset(
+            DUMMY_REPO_ID,
+            root=root,
+            seed=17,
+            buffer_size=3,
+            decode_threads=1,
+            decoded_queue_size=1,
+        )
+    )
+    parallel = StreamingLeRobotDataset(
+        DUMMY_REPO_ID,
+        root=root,
+        seed=17,
+        buffer_size=3,
+        decode_threads=3,
+        decoded_queue_size=5,
+    )
+    original_make_item = parallel._make_episode_item
+    state_lock = threading.Lock()
+    active = 0
+    max_active = 0
+
+    def delayed_make_item(*args, **kwargs):
+        nonlocal active, max_active
+        with state_lock:
+            active += 1
+            max_active = max(max_active, active)
+        try:
+            frame_index = int(args[2])
+            time.sleep(0.005 if frame_index % 3 == 0 else 0.001)
+            return original_make_item(*args, **kwargs)
+        finally:
+            with state_lock:
+                active -= 1
+
+    monkeypatch.setattr(parallel, "_make_episode_item", delayed_make_item)
+
+    assert _indices(parallel) == expected
+    assert 1 < max_active <= parallel.decode_threads
+
+
 @pytest.mark.parametrize("video_backend", ["torchcodec", "pyav"])
 def test_streaming_rgb_video_matches_map_style(
     tmp_path: Path,