c65d6228f4
Three fixes: 1. cuCtxSetLimit(STACK_SIZE, 4096) in GpuCuriosityTrainer::new() — the fused kernel needs ~3KB/thread (6 arrays of 42-128 floats), default 1024B causes stack overflow → async crash → stream deadlock 2. ml crate default features restored to ["minimal-inference", "cuda"] — was ["minimal-inference"] only, causing #[cfg(not(feature="cuda"))] gates to fire and block GPU code paths 3. Removed candle-core, candle-nn, candle-optimisers from ml/Cargo.toml dependencies — Candle was eliminated from source but deps remained. NOTE: 322 candle references remain in ml/src/ — next commit migrates them. 4. Re-enabled curiosity in smoke test (curiosity_weight back to default) 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;