333ea71844
Catalogue every numeric field in the HEALTH_DIAG family of log lines
(7 emit sites across training_loop.rs + metrics.rs) and classify each
source as GPU-already / CPU-bound / Mixed / Host-state / ISV-already.
Identifies the dominant cost contributors driving the observed
~70 s/epoch HEALTH_DIAG overhead on L40S after the eval async-split
(commit f815f7239) — they are all CPU-side reductions over multi-million
element per-sample buffers fetched via memcpy_dtoh:
- update_q_mag_means_cached (~786 KB DtoH + B×total_actions loop)
- var_scale_epoch_mean (N-element DtoH + conditional avg)
- trail_fire_and_hold_per_mag (4× N-element DtoH + per-mag loop)
- per_magnitude_winrate_and_variance (4× DtoH + sumsq loop)
- reward_contrib_fractions (6× large-buffer DtoH + 6 passes)
- read_eval_action_distribution_* (4 sites; CPU iter on pinned)
- branch_noisy_sigma_mean (NoisyNet param DtoH per branch)
Aggregate per-emit DtoH ≈ 50-150 MB depending on alloc_episodes ×
alloc_timesteps; fully consistent with the 70 s/epoch observation.
All inputs are already device-resident, so the migration is "stop
reducing on the host" plus a small kernel family writing into a single
mapped-pinned HealthDiagSnapshot struct.
Phase 0 deliverable per the dispatching prompt; subsequent commits
(struct + mapped wrapper, kernel family, CPU emit rewrite, audit-doc
updates) follow the phased plan documented at the bottom of the file
and gate-review on this inventory before touching code.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.