26edbb6d8f
shrink_and_perturb() is called at epoch boundaries after child graphs are captured. shrink_perturb_kernel lives in the same CUmodule as scale_f32, saxpy_f32, spectral_norm, adam_update, and other graphed CUfunctions. On Hopper (sm_90), launching any CUfunction from a graphed CUmodule ungraphed corrupts the child graph kernel state → 3100ms replay. scale_adam_momentum() is called at cosine LR warm restarts (also after graph capture). scale_f32_kernel is captured in forward_child — same CUmodule violation. Fix: add shrink_perturb_ungraphed and scale_f32_ungraphed loaded from the existing ungraphed_module (separate CUmodule instance of the same DQN_UTILITY_CUBIN). Both ungraphed callers now use isolated handles. CUmodule count: 5 → 5 (ungraphed_module already existed, reused). Co-Authored-By: Claude Sonnet 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;