d799bfafbe
Replace per-sample CUDA kernels (16,384 launches per GEMM) with batched cublasLtMatmul — ONE call per GEMM layer. Forward pass uses 3 GEMMs + SiLU + bias_add + expectile_loss. Backward pass uses 5 GEMMs + SiLU backward + bias_grad_reduce. Eliminates grads_per_sample buffer (was 27MB), tiled backward loop, and weight_grad_reduce kernel. Constructor now accepts Arc<SharedCublasHandle> instead of bare stream, sharing the cuBLAS handle with the trunk forward/backward pipeline. Eight IqlGemmDesc descriptors are cached at init with heuristic algo selection for CUDA Graph compatibility. 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;