2856daa8e9
Fixes cudarc 0.19 event tracking corruption caused by mixing safe device_ptr/device_ptr_mut wrappers with raw memcpy_dtod_async. The anti-pattern: manually calling device_ptr() to get raw pointers, then using low-level memcpy_dtod_async, then dropping SyncOnDrop guards — this bypasses cudarc's event management and corrupts the synchronization state of CudaSlice objects. Fixed in 7 call sites across 3 files: - gpu_experience_collector.rs: dtod_clone_f32, dtod_clone_i32, build_next_states_dtod (replaced pointer arithmetic with slice views) - gpu_weights.rs: extract_one, sync_one - training_loop.rs: cuda_slice_to_tensor_f32 Investigation ongoing: deadlock persists in PER insert path (cuda_slice_to_tensor_f32 -> stream.synchronize()). The memcpy fix is correct but there's an additional issue in the CudaSlice->GpuTensor conversion that needs further debugging. 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;