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Author	SHA1	Message	Date
jgrusewski	450c23a6d0	refactor(cuda): eliminate all CPU fallbacks — CUDA mandatory across ML stack - Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences) - Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250) - Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.) - Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum) - Move compile_ptx_for_device() to ml-core for shared access - Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs, training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics - Replace unwrap_or(Device::Cpu) with hard errors everywhere - Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers - Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline) - Port IQL value network to GPU kernel (5 CUDA entry points) - Port HER goal relabeling to GPU kernel (warp-per-sample) - Wire DSR GPU-to-CPU sync in training loop - cfg!(feature = "cuda") → true in inference_validator Zero warnings, zero errors across entire workspace. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-16 21:01:28 +01:00
jgrusewski	d313486dc2	refactor(ml): split monolith into 9 sub-crates + delete dead code Extract 9 new sub-crates from the ml monolith to enable parallel compilation across the workspace: New crates (this commit): - ml-features (282 tests): feature engineering, 21 modules - ml-labeling (45 tests): triple barrier, meta-labeling, fractional diff - ml-ensemble (116 tests): ensemble coordination, voting, confidence - ml-hyperopt (47 tests): core PSO/TPE optimizer, parameter space - ml-checkpoint (41 tests): checkpoint persistence, compression, signing - ml-regime (68 tests): CUSUM, Bayesian changepoint, regime classification - ml-data-validation (67 tests): FDR correction, CPCV, data quality - ml-risk (33 tests): neural VaR, Kelly criterion, circuit breakers - ml-validation (43 tests): statistical validation, walk-forward, DSR Extended existing crates: - ml-dqn: added evaluation/ (backtesting engine, metrics, reports) and checkpoint implementation - ml-supervised: added checkpoint implementations - ml-core: added shared types needed by new sub-crates Pattern: each module in ml/ becomes a thin facade (pub use subcrate::*) with bridge modules staying in ml for cross-model adapter code. Dead code deleted (~7K lines): - 13 undeclared files in microstructure/ (never compiled) - 7 undeclared files + tests/ in risk/ (never compiled) - parquet_io, cache_service, cache_storage, minio_integration (unused) - extraction_wave_d_impl.rs (bare fn outside impl block) All 2,746 sub-crate tests + 951 ml tests pass. Full workspace builds clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-08 15:17:22 +01:00

Author

SHA1

Message

Date

jgrusewski

450c23a6d0

refactor(cuda): eliminate all CPU fallbacks — CUDA mandatory across ML stack

- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
  training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator

Zero warnings, zero errors across entire workspace.

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

2026-03-16 21:01:28 +01:00

jgrusewski

d313486dc2

refactor(ml): split monolith into 9 sub-crates + delete dead code

Extract 9 new sub-crates from the ml monolith to enable parallel
compilation across the workspace:

New crates (this commit):
- ml-features (282 tests): feature engineering, 21 modules
- ml-labeling (45 tests): triple barrier, meta-labeling, fractional diff
- ml-ensemble (116 tests): ensemble coordination, voting, confidence
- ml-hyperopt (47 tests): core PSO/TPE optimizer, parameter space
- ml-checkpoint (41 tests): checkpoint persistence, compression, signing
- ml-regime (68 tests): CUSUM, Bayesian changepoint, regime classification
- ml-data-validation (67 tests): FDR correction, CPCV, data quality
- ml-risk (33 tests): neural VaR, Kelly criterion, circuit breakers
- ml-validation (43 tests): statistical validation, walk-forward, DSR

Extended existing crates:
- ml-dqn: added evaluation/ (backtesting engine, metrics, reports)
  and checkpoint implementation
- ml-supervised: added checkpoint implementations
- ml-core: added shared types needed by new sub-crates

Pattern: each module in ml/ becomes a thin facade (pub use subcrate::*)
with bridge modules staying in ml for cross-model adapter code.

Dead code deleted (~7K lines):
- 13 undeclared files in microstructure/ (never compiled)
- 7 undeclared files + tests/ in risk/ (never compiled)
- parquet_io, cache_service, cache_storage, minio_integration (unused)
- extraction_wave_d_impl.rs (bare fn outside impl block)

All 2,746 sub-crate tests + 951 ml tests pass.
Full workspace builds clean.

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

2026-03-08 15:17:22 +01:00

2 Commits