075ed8ba4f
step_denoise_adam uses adam_update_kernel + grad_norm_finalize_kernel UNGRAPHED after child graph replays. Same CUfunctions are captured in adam_child and forward_child. On Hopper, launching a captured CUfunction ungraphed corrupts graph kernel state → 3100ms adam_child replay on next step. Fix: skip selectivity+denoise ungraphed training. These are auxiliary optimizers with non-fatal error handling. To re-enable: load separate CUfunction instances for ungraphed paths or move into child graphs. Expected: adam drops from 3100ms to ~30ms. Epoch from ~690s to ~60s. 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;