801781a472
Replace expensive CPU↔GPU data transfers with GPU-native operations: - check_gradients_finite: sum_all() scalar readback (4B per tensor) replaces flatten_all()+to_vec1() that copied entire gradients to CPU (up to 200MB per step across 9 call sites in DQN+PPO) - Huber loss: affine() replaces 3× Tensor::from_vec(vec![const; N]) CPU Vec allocations in the inner loss computation loop - update_priorities_gpu: per-index slice_scatter (~1KB DMA) replaces full-buffer to_vec1()+from_vec() roundtrip (1.2MB DMA per step) - PER sampling: single from_vec + GPU to_dtype replaces duplicate from_vec calls and CPU type conversion roundtrips - RegimeConditional: log warning on silent GPU batch drops 822 tests pass (350 ml-dqn, 274 ml-core, 198 ml-ppo), 0 clippy. Co-Authored-By: Claude Opus 4.6 <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;