5546a45bc3
gpu_n_episodes was manually overridden in GPU profiles, training configs, test files, and hyperopt — all set to 0 or small fixed values that bypassed the auto-scaling logic, causing a div-by-zero crash in train_baseline_rl. Now: single auto-scaling path via optimal_n_episodes() from VRAM/SM count. No manual override field. Cap at 16384 (consistent with AutoBatchSizer's 8192 cap pattern). Floor at 32 for small GPUs. Removed gpu_n_episodes from: - DQNHyperparameters, PpoHyperparameters structs - All 4 GPU profiles (rtx3050, h100, a100, default) - Training profiles (smoketest, localdev) - ExperienceProfile struct + serde - Hyperopt adapter - All test overrides 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;