facbf76eb5
Pearl 5's online_taus/target_taus/cos_features were declared as CudaSlice<f32> (device-only), populated via upload_f32_via_pinned which does a DtoD copy from a separate mapped-pinned staging buffer. The DtoD inside CUDA Graph capture triggers CUDA_ERROR_STREAM_CAPTURE_INVALIDATED and the 'continuing ungraphed' fallback observed in smoke-test-hhr5q. This violates feedback_no_htod_htoh_only_mapped_pinned: the rule is mapped-pinned (cuMemHostAlloc DEVICEMAP) for ALL CPU↔GPU paths. No DtoD copies, no HtoD copies, no exceptions. Fix: convert all 3 buffers (online_taus, target_taus, cos_features) to MappedF32Buffer per-branch [MappedF32Buffer; 4] arrays. Host writes go directly to host_ptr; IQN kernel reads dev_ptr of the same memory — no copy step at all. The mem::swap pattern is replaced with pure selection: activate_branch_taus sets active_branch_idx; kernel launch sites index online_taus_per_branch[active_branch_idx].dev_ptr. Eliminates upload_f32_via_pinned calls for these buffers entirely. Refresh becomes a host write to mapped-pinned host_ptr at fold boundary; subsequent kernel launches see the write through the mapped-pinned coherence guarantee after stream sync. cargo check + cargo build --release + cargo test --lib (sp4 sp5 state_reset_registry: 13/13) all clean. Sanity grep for upload_f32_via_pinned in gpu_iqn_head.rs returns zero. Co-Authored-By: Claude Sonnet 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;