719f18baab
Vaccine: - Replaced upload_batch_gpu (sync DtoD) with upload_batch_ptrs_6 + submit_indirect_upload_ops (async indirect kernels). Zero sync. - Deleted upload_batch_gpu entirely — no callers remain. Causal intervention: - Removed entirely from hot path. Was running 14 cuBLAS forward passes every 100 steps with no readback and no training decision based on the result. Pure GPU waste. - Kernel still exists in cubin for future offline analysis. Causal readback: - Removed stream.synchronize() + memcpy_dtoh from run_causal_intervention. Return value was already discarded by caller. Now returns 0.0 immediately. Sensitivity stays on GPU. Dead code: - Deleted upload_batch_gpu (72 lines) — replaced by indirect upload. Per-step hot path on step 2+: 9 graph replays (~45µs) 5 async HtoD (92 bytes, ~5µs) Zero sync. Zero DtoH. Zero alloc. Zero CPU compute. Zero concerns. 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;