1d93b6e44d
Candle's Tensor::cumsum(0) internally allocates an [n,n] upper-triangular matrix (9.3 GB for n=50K) causing OOM on GPUs ≤48 GB. Replace with a block-parallel Hillis-Steele scan kernel that is O(n) in time and memory. Additional fixes in this commit: - Break autograd chain leak in loss/grad accumulation via .detach() (was leaking ~32 MB/step across entire training run) - Release features_raw_cuda/targets_raw_cuda after GPU experience collection (~164 MB VRAM reclaimed) - Use softmax eval (temp=0.3) in walk-forward backtest to prevent action collapse causing trades=0 on early-stage models Validated: 1286 tests pass (408 ml-dqn + 878 ml), 0 failures. VRAM stable at 754 MB across 45K+ training steps on RTX 3050 Ti. 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;