472dffd0f3
Root cause 1 — CUDA_ERROR_ILLEGAL_ADDRESS: shmem_max_in_dim only included trunk dims (state_dim, shared_h1, shared_h2) but not head dims (value_h, adv_h). BF16 weight tile for branch output overflowed shared memory on RTX 3050 (48KB). Root cause 2 — CUDA_ERROR_INVALID_VALUE on EMA kernel: cudarc 0.17's automatic event tracking records read/write events on CudaSlice buffers. During CUDA Graph capture (events disabled) then replay (events re-enabled), stale write events from CudaSlice Drops poison the context error_state. Next bind_to_thread() propagates it. Fix: disable_event_tracking() at GpuDqnTrainer construction — single-owner forked stream, all sync points are explicit. Also: - Remove all #[ignore] from smoke tests, use real ES.FUT .dbn data - Validate DBN schema at file level (skip non-OHLCV) - Organize test_data/ into per-symbol subdirectories - Fix pre-existing gpu_kernel_parity_test + evaluate_baseline errors 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;