954bca690d
Root cause identified: bf16_weight_ptrs called device_ptr() inside CUDA graph capture, which records cudarc events — not allowed during capture. Created bf16_weight_ptrs_from_base() that takes pre-resolved u64 base pointer from CachedPtrs (no device_ptr calls, graph-safe). Replaced ALL 12 bf16_weight_ptrs calls in gpu_dqn_trainer with bf16_weight_ptrs_from_base using self.ptrs.params_buf/target_params_buf. Debug forward (no graph) confirms cuBLAS GemmEx produces valid logits. Q-value divergence persists in graph mode — Adam or readback issue TBD. 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;