9502c1e22c
Root cause: SHMEM_MAX_IN_DIM was hardcoded to 256, but on H100 with hidden_dim_base=2048, SHARED_H1=SHARED_H2=512. The cooperative tile loader wrote 64×512=32768 floats into shmem allocated for 64×256=16384, causing CUDA_ERROR_INVALID_VALUE on kernel launch. Fixes: - Make SHMEM_MAX_IN_DIM injectable via NVRTC #define (was hardcoded) - Compute max(state_dim, shared_h1, shared_h2) and inject at compile time - Use dynamic value for host-side shmem_bytes allocation - Shrink eps_out[NOISY_MAX_DIM] → eps_out[SHMEM_TILE_ROWS] (saves 768B/thread) - Force smoke tests to Device::Cpu (CUDA driver sensitivity to binary layout) 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;