edd7ceb0f2
Wave 0 (NVTX Instrumentation): - Add ml-core::nvtx module with NvtxRange RAII guard (runtime dlopen, zero overhead when absent) - Instrument 10 CUDA pipeline hot paths: experience collector, backtest evaluator, PPO collector, statistics, training guard, monitoring, replay buffer Wave 1 (Low-Effort H100 Optimizations): - L2 cache persistence: pin DQN weights (~23MB BF16) in H100's 50MB L2 via cudaCtxSetLimit(CU_LIMIT_PERSISTING_L2_CACHE_SIZE) — 3.5x effective bandwidth - Dynamic shared memory: GPU-aware tile sizing (228KB H100, 164KB A100, 100KB RTX) via CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES opt-in - Async double buffer: sync_staging() with CUDA stream synchronization before swap Validation: 0 clippy errors, 1629 tests passed (308+410+911), 0 gpu-hotpath violations 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;