88cf7a321e
The algo cache serialized heuristic-selected algo bytes to disk with GPU name + CUDA version validation. This created a divergent code path between dev (RTX 3050) and prod (H100) — different machines would get different cached algos, producing different but "frozen" results. Determinism should come from the algorithm itself being deterministic, not from caching one machine's non-deterministic output. The proper fix is routing evaluation through the trainer's CUDA-Graphed forward pass (which IS deterministic by design). Kept: COMPUTE_32F_PEDANTIC on all cublasLt matmul descriptors (disables TF32, universal across GPUs). Kept: IQN + attention backward determinism, single-stream eval, evaluator reuse. 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;