7f3066e695
training_dtype() now returns BF16 on all CUDA devices, enabling full tensor-core utilization. F32 is used only at boundaries (scalar extraction, loss computation, softmax). VRAM estimator updated to account for BF16 byte sizes, C51/QR atoms, dueling streams, NoisyNet param doubling, and GPU PER buffer pre-allocation. Changes: - mixed_precision.rs: training_dtype() returns BF16 on CUDA - curiosity.rs: F32 cast before scalar extraction - network.rs: F32 output at NetworkLayers forward boundary - traits.rs: estimate_trial_vram_mb_full() with BF16-aware sizing - dqn.rs adapter: uses full estimator with worst-case architecture 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;