c8df643e67
Capture the experience collector's bottleneck + Q-network forward pass into a CUDA Graph on first timestep. Prevents ungraphed cublasLtMatmul calls from contaminating cuBLAS internal state between epochs. Root cause identified: multiple cuBLAS handles on the same CUDA stream. NVIDIA docs: "maintain a separate cuBLAS handle for each stream" — implying one handle per stream. We have 3 handles (experience collector, training, DDQN) on one stream. Proper fix (next session): refactor CublasForward to share a single cuBLAS handle across all components. Separate the handle (shared) from per-component cached descriptors. 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;