Add a sampler that never materializes frame indices: it stores only
per-episode boundaries (numpy, a few bytes per episode) and maps logical
positions to frame indices on the fly with searchsorted. Shuffling uses a
seeded Feistel permutation over [0, num_frames) (cycle-walking to the
exact domain), so the data order is a pure function of (seed, epoch):
- no RNG state to synchronize across distributed ranks,
- constant memory and zero epoch-boundary cost at any dataset size,
- O(1) seek to any position, enabling sample-exact resume.
Opt in with --deterministic_sampler=true. On resume, lerobot-train maps
the checkpointed step back to (epoch, start_index) via
compute_sampler_state and continues at the exact sample where the run
left off (up to accelerate's even_batches padding at epoch boundaries).
The shuffle is pseudo-random rather than a true uniform permutation, the
standard trade-off in large-scale training loaders.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
In distributed training, accelerate can only synchronize the shuffle
permutation across ranks when the sampler exposes a generator attribute.
EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch
shards relied on every rank's global CPU RNG staying in lockstep forever;
any rank-asymmetric RNG consumption (e.g. eval rollouts on the main
process only) silently desynced the permutations and ranks trained on
overlapping/missing samples.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Pepijn