8b8bb1af70
SP-chain training has been standardising on L40S since 2026-05-09, but
every invocation required an explicit `--gpu-pool ci-training-l40s`
override. The 2026-05-04 train-mnpf7 incident (sm_90 cubins deployed
to an L40S device, then resubmitted with the explicit override) was
the last incident in a long line of "forgot the pool flag" friction.
`feedback_default_to_l40s_pool.md` codified the user preference; this
commit lands the default in the actual invocation paths.
Changes:
- infra/k8s/argo/train-template.yaml: gpu-pool default H100 → L40S
- infra/k8s/argo/train-multi-seed-template.yaml: same + cuda-compute
-cap default 90 → 89
- scripts/argo-train.sh: docstring / --help / compute-cap fallback
case all flip to L40S as the bare default; H100 becomes opt-in via
`--gpu-pool ci-training-h100` for 80 GB / sm_90 workloads
- scripts/argo-test.sh: --help text aligned
Other architectural defaults (data-source=mbp10 per
feedback_mbp10_mandatory; imbalance-bar-threshold=20.0 per the 2026-
05-10 OOM-prevention fix) are already correct in the template.
Verified via `argo-train.sh dqn --branch sp20-aux-h-fixed --sha HEAD
--baseline --dry-run` — rendered workflow shows cuda-compute-cap=89,
no explicit gpu-pool override (template default L40S in effect).
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.