feat(streaming): multinode example, dataloading benchmark, distributed smoke test

- examples/scaling/train_streaming_multinode.py: Accelerate-based distributed/
  resumable streaming training (no DistributedSampler; rank/world_size auto-resolved),
  checkpoints the dataset stream state, and supports a --dummy pure-dataloading path
  with throughput logging. SLURM launcher in slurm/train_streaming_robocasa.sh.
- benchmarks/streaming/benchmark_streaming.py: dummy-consumer dataloading benchmark
  (single / sarm frame modes) emitting frames/s/node, p50/p95/p99 sample latency,
  first-batch latency, and VideoDecoderCache reuse stats as JSON + CSV. SLURM launcher
  + README documenting the source/node/mode matrix and manual bucket prewarming.
- VideoDecoderCache: add hit/miss/eviction counters and a stats() method so the
  benchmark can surface decoder thrash (no new cache, no eviction-policy change).
- tests/datasets/test_streaming_distributed.py: accelerate-launch smoke test asserting
  per-rank disjointness; skips (does not false-pass) when <2 processes spawn.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

benchmarks/streaming/README.md

@@ -0,0 +1,53 @@
+# Streaming dataloading benchmark
+
+Measures **dataloading only** (no model) for `StreamingLeRobotDataset`: parquet read + video decode +
+delta windowing + shuffle. A dummy consumer pulls batches and moves them to the device, so the numbers
+isolate the data pipeline. Use it to compare sources (Hub vs. storage bucket vs. prewarmed bucket),
+frame modes, and node counts, and to catch p95/p99 video-decode regressions.
+
+## Run
+
+```bash
+python benchmarks/streaming/benchmark_streaming.py \
+    --repo_id pepijn223/robocasa_pretrain_human300_v4 \
+    --mode sarm --batch_size 64 --num_workers 12 --num_batches 200 \
+    --source hub --out_dir benchmarks/streaming/results
+```
+
+Multinode (per-node throughput) goes through Accelerate under SLURM:
+
+```bash
+sbatch slurm/benchmark_streaming_robocasa.sh
+```
+
+## Matrix
+
+| Axis       | Values                                                                                                               |
+| ---------- | -------------------------------------------------------------------------------------------------------------------- |
+| Source     | `hub` (verify now), `bucket`, `warmed_bucket` (bucket + prewarming; with user's help later)                          |
+| Baseline   | current `main` `StreamingLeRobotDataset` on Hub streaming                                                            |
+| Nodes      | 1 and 2 (per-node throughput should be independent)                                                                  |
+| Frame mode | `single` (1 frame, all cameras; target ≥ 120 frames/s/node) · `sarm` (8 steps spaced 1s; target ≥ 320 frames/s/node) |
+
+`--source` is a label only; the actual source is whatever `--repo_id` / `--root` point at.
+
+## Metrics emitted (JSON + CSV)
+
+`frames_per_s_node`, `samples_per_s`, `first_batch_latency_s`, `p50/p95/p99_sample_latency_ms`,
+`wallclock_s`, and `video_decoder_cache` (`hits`, `misses`, `evictions`, `hit_rate`, `size`). A low
+cache `hit_rate` with high `p99` is the decoder-thrash signature — raise `--video_decoder_cache_size`
+or `--buffer_size`, or reduce `num_workers`.
+
+## Bucket sources & prewarming (manual)
+
+Prewarming is a **server-side** Hugging Face storage-bucket feature — there is no client script. To
+benchmark the `warmed_bucket` source:
+
+1. Attach a storage bucket to the dataset and enable it (see
+   <https://huggingface.co/docs/hub/storage-buckets>). Buckets resolve through `fsspec`, the same as
+   `hf://`, so no code change is needed — point `--repo_id`/`--revision` (or `--root`) at the bucket.
+2. Enable **prewarming** in the bucket settings and wait for warm-up to complete.
+3. Run the benchmark with `--source warmed_bucket`. Compare against the cold `--source bucket` and the
+   `--source hub` baseline.
+
+Manual only — not run in CI.