d3762e7250
After Fix 1..16 migrated all 80+ production callers off `super::htod_f32` and `super::clone_htod_f32`, the helper bodies in `cuda_pipeline/mod.rs:129-145` had zero non-test consumers. Deleted both function definitions per `feedback_no_legacy_aliases.md` (no deprecated wrappers). Per `feedback_no_partial_refactor.md` (when a shared contract is deleted, every consumer migrates together — including tests), the two surviving test-block callers in `gpu_tlob.rs::tests` (lines 1017 and 1132) are migrated to `mapped_pinned::upload_f32_via_pinned` in the same commit. The other test-only callers in `signal_adapter.rs::tests`, `gpu_action_selector.rs::tests`, and `cuda_pipeline/mod.rs::tests` use bare `stream.memcpy_htod` / `stream.memcpy_stod` against the cudarc handle directly (not the deleted helpers) — no change needed. A docstring was added at the deletion site recording when and why the helpers were removed, pointing future readers at the canonical replacements `mapped_pinned::clone_to_device_f32_via_pinned` and `mapped_pinned::upload_f32_via_pinned`. Final state of the HtoD migration sequence: - production callers of `stream.memcpy_htod` / `memcpy_stod`: 0 - production callers of `htod_f32` / `clone_htod_f32`: 0 - helper definitions: removed from `mod.rs` docs/dqn-gpu-hot-path-audit.md updated with Fix 17 entry. cargo check -p ml --lib clean at 12 warnings. cargo check -p ml --tests clean at 23 warnings (12 lib duplicates + 11 test-specific, baseline unchanged). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;