cfc44be13c
Two root causes of cross-process non-determinism fixed: 1. MSE→C51 graph switch: graph_forward_mse and graph_forward were captured in different sessions with different cublasLtMatmul configs. Fix: always use blended graph_forward (c51_alpha controls blend). Deleted LossMode enum, graph_forward_mse, and all switching code. 2. Multiple capture sessions at steps 0-2: graph_forward, graph_ddqn, and graph_adam were captured individually before graph_mega at step 2. Each capture contaminated cuBLAS internal state. Fix: capture graph_mega at step 0 (single capture session). Result: runs with same initial cublasLtMatmul capture are fully bit-identical across all 30 epochs. Remaining cross-process variation is from the single initial cublasLtMatmul call during graph capture. 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;