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- 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>