68e83bef0e
The batch_size >128 hang was caused by cudarc's synchronous memcpy_htod for the adam_step counter. At batch_size=128 the sync completes fast enough, but at 509+ the pipeline drain from the sync interacts with CUDA Graph replay timing and deadlocks the stream. Replaced all 3 memcpy_htod(&[self.adam_step]) calls with raw cuMemcpyHtoDAsync_v2 — zero pipeline drain, zero CPU sync. Production TOML set to batch_size=0 (AutoBatchSizer drives it). AutoBatchSizer caps at 8192 for RL training. 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;