a13490dd97
Root cause: GpuTensor::cat() called to_host() on each input tensor, concatenated on CPU, then from_host() back to GPU. This caused: 1. Race conditions when async GPU work hadn't completed before to_host 2. CUDA_ERROR_ILLEGAL_ADDRESS when event tracking was disabled 3. Massive performance hit (2 DMA transfers per tensor per cat call) Fix: replace with DtoD memcpy via CudaSlice::slice() views. Both dim=0 (contiguous blocks) and dim>0 (interleaved rows) use GPU-to-GPU copies. Zero CPU involvement, zero event dependency, zero race conditions. Also: revert global disable_event_tracking() — was a workaround for the to_host race, no longer needed with GPU-native cat/stack. 5/5 DQN pipeline tests pass with CUDA Graph enabled on RTX 3050. 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;