87ea9fa416
Per `feedback_no_htod_htoh_only_mapped_pinned.md`, mapped pinned
(cuMemHostAlloc DEVICEMAP) is the only allowed CPU↔GPU path.
Adds module-level `upload_via_mapped_{f32,i32,u32,u64}` helpers and an
in-place `update_via_mapped_f32`. Each stages CPU data through a
transient mapped pinned buffer and DtoD-copies into the destination
`CudaSlice<T>`, then stream-syncs so the staging buffer is safe to
drop. Destination buffer types remain `CudaSlice<T>` so every existing
consumer (raw_ptr / kernel arg) is untouched, satisfying
`feedback_no_partial_refactor.md` for these one-shot init paths.
Migrated COLD sites in `GpuDqnTrainer::new`:
- weight_decay_mask (TOTAL_PARAMS f32)
- branch_slice_starts_dev, branch_slice_lens_dev ([i32; 4])
- branch_grad_scales_dev ([f32; 4])
- per_branch_gamma_base/max_dev ([f32; 4] each)
- q_quantile_branch_offsets/sizes_dev ([i32; 4] each)
- spectral_norm_descriptors_dev ([u64; 78])
- stochastic_depth_scale_buf ([f32; 3])
- stochastic_depth_rng_state ([u32; 1])
- vsn_group_begins_buf, vsn_group_ends_buf ([i32; num_groups] each)
- mamba2_params Xavier init (mamba2_param_count f32)
11 COLD HtoD sites eliminated. cargo check clean (15 warnings; +2 over
baseline 13 are the new MappedU32/U64 struct visibility warnings,
matching the existing MappedF32/I32 pattern).
Co-Authored-By: Claude Opus 4.7 (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;