adcbd6f0df
gradient_dot_and_norm kernel used atomicAdd with 1264 blocks to compute dot(g_train, g_val) and |g_val|² — same H100 L2 contention pattern as the grad_norm hang. Replaced with two-phase: per-block partials + single- block finalize (gradient_dot_norm_finalize). Zero atomicAdd. Also added step-0-only per-phase GPU syncs to catch any remaining hangs immediately (forward, aux, conditional, Adam, PER — only on step 0). TODO: Causal intervention runs 14 full cuBLAS forward passes per step. Should be optimized (batched or reduced frequency). 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;