94b968d800
Experience collector: PinnedHostBuf<T> via cuMemHostAlloc(PORTABLE) for 6 DtoH buffers — ~25-30% faster transfers vs pageable memory. NVRTC caching: 4 OnceLock additions in ml-ppo cuda_nn: - softmax.rs: was re-compiling NVRTC on EVERY forward pass batch (!) - linear.rs, lstm.rs: cached for multi-layer construction Device attribute: OnceLock<u32> for max_threads in gpu_replay_buffer pfx_sum — eliminates ~5µs driver query per call. Co-Authored-By: Claude Opus 4.6 (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;