465abc7e9b
Initial 8x head + 2x trunk-H2 (commit 787ee7b86) OOM'd all 3 folds on
L40S with alloc next_states f32[1048576000] failure. Root cause: aux
backward stores [B, H, SH2] partial grad buffers per sample. At
H=256, SH2=256, B=16384: 4GB per head x 2 heads + 4GB trunk = +12GB
on top of existing DQN buffers, exceeding L40S 48GB budget.
Dial back:
- AUX_HIDDEN_DIM: 256 -> 128 (still 4x prior 32-unit bottleneck)
- AUX_TRUNK_H2: 256 -> 128 (back to original mild bottleneck)
At H=128: partial buffer = 2GB per head = 4GB total. Fits comfortably
alongside next_states ~4GB and other DQN buffers.
Cargo check clean. Smoke re-dispatch next.
Co-Authored-By: Claude Opus 4.7 (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;