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jgrusewski 3d679e824f feat(ml): GPU saturation final — DQN PER deferral, PPO trajectory batching, Liquid/TGGN sync reduction, DoubleBuffer wiring

- DQN PER: defer td_errors to_vec1() after loss.to_scalar() — piggyback on
  existing pipeline flush instead of forcing premature GPU→CPU stall
- PPO trajectories: capacity-hint Vec allocations, extend_flat_states methods,
  states_flat field on TrajectoryBatch for zero-copy GPU upload
- TGGN validate(): batch N per-sample losses on GPU → single to_scalar() sync
  (was N GPU→CPU syncs)
- Liquid backward(): batch grad-norm per-param sqr().sum_all() on GPU → single
  to_scalar() sync (was N GPU→CPU syncs per optimizer step)
- Liquid validate(): same N→1 GPU sync reduction as TGGN
- DQN trainer: restore EpochPrefetcher/DoubleBufferedLoader API (wrongly deleted)
- train_baseline_rl: wire DoubleBuffer GPU pre-upload — after CPU prefetch
  completes, immediately upload next fold to GPU via DqnGpuData::upload() so
  next fold starts with data already resident on GPU

2478 tests pass, 0 clippy warnings, 0 compile errors.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-02 15:41:23 +01:00

baseline_common

fix(ml): wire spread_cost_bps into RL training — commission + spread = total tx cost

2026-03-01 22:10:43 +01:00

cuda_test.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

download_baseline.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

evaluate_baseline.rs

feat(ml): wire Prometheus eval metrics into evaluate_baseline