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jgrusewski 95fc94ae9c fix: precommit review — ensemble reuse, keep gpu_data across folds, eliminate Vec<f64>

Fix 1 (HIGH): Ensemble trainers were created + data uploaded inside the
fold loop, causing redundant GPU uploads per fold per ensemble member.
Moved creation + init_from_fxcache before the fold loop; inside the loop
we now reuse trainers via set_training_range + reset_for_fold.

Fix 2 (MEDIUM): reset_for_fold cleared gpu_data = None, forcing re-upload
on every fold even though the full dataset was already GPU-resident via
init_from_fxcache. Removed the clearing — data stays on GPU across folds.

Fix 3 (LOW): train_fold_from_slices allocated a Vec<f64> per bar via
t.to_vec(). Added train_with_data_full_loop_slices that accepts
&[([f64; 42], [f64; 4])] — both are Copy stack types, zero heap alloc
per element. Added matching collect_gpu_experiences_slices and
run_training_steps_slices helpers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 23:21:45 +02:00

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

fix: wire real GPU backprop + SPSA gradients, fix checkpoint loading, eliminate candle from examples

2026-03-20 15:41:42 +01:00

download_baseline.rs

fix: add retry with backoff to Databento download_quarter

2026-03-31 22:57:43 +02:00

evaluate_baseline.rs

feat: DQN gems — spectral decoupling, manifold mixup, regime replay, family hyperopt