a29f109b67
Update all callers to match the new pure-cudarc APIs introduced by the hive agent CudaSlice migration. Key changes: - GpuTrainingGuard::new() now takes Arc<CudaStream>; callers use from_device() - check_and_accumulate/qvalue_stats/qvalue_divergence take &CudaSlice<f32> instead of &Tensor; callers convert via tensor_to_cuda_slice_f32() - accumulate_q_value takes f32 scalar, returns () (no Result) - GpuReplayBuffer::insert_batch gains batch_size arg, takes CudaSlice params - signal_adapter functions take &Arc<CudaStream> (cudarc 0.17 Arc requirement) - Add tensor_to_cuda_slice_u32() and cuda_f32_to_tensor() utility functions - Replace CudaView usage with owned CudaSlice via tensor_to_cuda_slice_f32() - Fix CudaStorage.device field access (was method call in older API) - Fix borrow-after-move in copy_actions_out via scoped DtoD copy Zero errors, zero warnings across lib + tests + examples + full workspace. 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;