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osobhandClaude Fable 5 19b6581f9c feat(jepa): extended GPU training, data pipeline, integration, and cargo config
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- jepa_gpu: remove the bring-up 2-block cap; full-depth GPU-resident
  ViT verified against a full-depth CPU reference on the RTX 5060 Ti
  (depth-12 ViT-Tiny max_rel_err <= 6.6e-5). CpuViTEncoder's own hidden
  min(depth,2) cap removed too — CPU-path callers now get the model
  they configured.
- jepa_distributed: real TCP parameter-server AllReduce backend
  (rendezvous handshake with world-size/rank validation, length-
  prefixed f32 payloads, connect/read/accept timeouts, connect retry
  until deadline so early peers survive rank 0 still computing);
  jepa_runner wires it for world_size > 1 and fails hard on collective
  errors. Two-rank loopback training run covered by test.
- rtx-jepa-cli (new crate): rtx-jepa binary with train/bench/plan/
  validate subcommands driving JepaRunConfig, run_jepa_training,
  run_jepa_benchmark, and ClusterTrainingPlan (plan --emit-config
  round-trips through a config serializer). GPU bench on this node:
  86k patches/sec vs 1.3k CPU (~64x).
- ViTSizeStr::Micro (d=32, depth=2) added as an explicit test/smoke
  size now that no hidden caps keep full-size configs cheap; heavy
  tests moved onto it (rtx-transformers suite: 367s -> 5s, and the
  runner subset had ballooned to 35min at full depth before this).

966 lib tests pass; 35/35 jepa_gpu with cuda; 8/8 CLI tests.

Co-Authored-By: Claude Fable 5 <[email protected]>
2026-07-10 00:04:21 -07:00