e9d9afbd61
L40S smoke train-jfbzr (commit 23e9a1f78) segfaults at exit 139 in the
first run_full_step invocation, after "GPU training guard initialized
(epoch loop)" and before any HEALTH_DIAG output. Static analysis
debugger couldn't reproduce locally (test_data/futures-baseline/ lacks
.fxcache).
This commit adds eprintln! checkpoints at each phase boundary in the
ungraphed step-0 path of FusedTrainer::run_full_step (PER sample,
PopArt, counters, spectral norm, TLOB forward, forward_main, DDQN,
aux_ops, post_aux, NaN checks).
stderr is line-flushed (vs stdout buffered through tracing JSON
formatter), so the last printed checkpoint identifies the SEGV site.
Each fires once per fold's first step (graph capture absorbs subsequent
steps), so log overhead is minimal.
Diagnostic-only commit — no behavior change. Will be reverted after the
next L40S smoke localizes the SEGV and the root-cause fix lands.
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;