a3c2fb4dd1
1. cuCtxSetLimit(STACK_SIZE, 8192) in curiosity trainer — prevents stack overflow in fused kernel (6 arrays of 42-128 floats/thread) 2. Removed in-kernel gradient zeroing (Phase 1) — had inter-block race where fast blocks atomicAdd while slow blocks still zero. Now uses host-side memset_zeros (GPU cuMemsetD8Async, stream-ordered) 3. cuda_slice_to_tensor_f32 now uses safe stream.memcpy_dtod() instead of raw device_ptr + memcpy_dtod_async (cudarc event tracking fix) 4. Debug traces in smoke test and training loop for deadlock diagnosis Investigation ongoing: deadlock in init_gpu_experience_collector — the collector constructor hangs during initialization, not during experience collection or training. 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;