a334ca288f
Move // gpu-ok: annotations to same line as violation patterns so the guard script's grep -v filter actually suppresses them. Fixes 6 false positives in ensemble adapters (dqn, ppo, liquid, kan, tggn, diffusion). Replace hardcoded 48KB shmem limit in compile_forward_kernel() with GPU-aware query (max_shared_memory_kb) — matches gpu_experience_collector pattern. H100 now gets 128-row tiles (was 64), eliminating tile loops for ≤128-dim layers in the backtest forward kernel. Co-Authored-By: Claude Opus 4.6 <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;