7208836d1f
gpu_tlob.rs:232 was loading symbol `attn_adam_update` from the
attention_backward_kernel cubin, but the kernel is defined as
`attn_adam_kernel` (attention_backward_kernel.cu:70); gpu_attention.rs:281
already loads the same symbol with the correct name. Mismatch caused
GpuTlob::new to fail with "named symbol not found" on every workflow
init, suppressed by the surrounding "training continues without TLOB"
warning. Per feedback_no_hiding.md, graceful-degraded modules ARE the
ghost-feature pattern.
With the symbol now loading, a separate cuBLAS-Lt heuristic failure
surfaces ("tlob heuristic (fwd_q): no algo found") — the TLOB GEMM
shape M=TLOB_OUT=16, K=TLOB_IN=32, N=batch is too narrow for cuBLAS-Lt
heuristics to find an algo. That's a real follow-up (pad dims, classic
sgemm fallback, or custom small-M kernel) but is independent of and
strictly upstream of this commit's symbol-name fix. The previous symbol
error was hiding the heuristic error.
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;