ff0549972d
Root cause: graph capture disables event tracking, but cudarc's record_err() stores errors from cuStreamWaitEvent on disabled events. bind_to_thread() calls check_err() and returns the stored error, blocking ALL subsequent cudarc API calls (launch, sync, memcpy). Fix: - check_err() before EMA kernel launch to consume stale errors - Raw cuStreamSynchronize + cuMemcpyDtoH for post-graph readback (bypasses bind_to_thread entirely) - Raw device pointers for EMA kernel args (bypasses device_ptr event wait) - GPU-native cat bounds check (dst_start + n <= output.len()) - CUDA Graph always enabled (removed should_use_cuda_graph function) 4/5 DQN pipeline tests pass with CUDA Graph on RTX 3050. 5th (epsilon_greedy) passes individually, flaky in sequence. 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;