06f9819f05
cudarc's stream.alloc_zeros uses cuMemAllocAsync (stream-ordered). These allocations are only accessible from the allocating stream. When cublasLtMatmul runs on a forked BRANCH stream (multi-stream branch dispatch), the stream-ordered workspace is inaccessible, causing CUBLAS_STATUS_NOT_SUPPORTED on H100 at batch=4096. Fix: allocate workspace via cuMemAlloc_v2 (synchronous, globally accessible from all streams). This matches the C++ debug test which uses cudaMalloc and works on H100 for all dimensions. Also: add d_layout destroy (leaked handle cleanup). 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;