d04b6c7023
- trade_ml.rs: Replace 3 mock data fallbacks (submit, predictions, performance) with proper error propagation. Commands now fail honestly when the API Gateway is unreachable instead of silently returning fake data. Mark 3 integration tests as #[ignore]. - monitoring_service: Add tonic-health with set_serving for MonitoringServiceServer. Enables grpc_health_probe readiness checks. - ml_training_service: Add tonic-health with set_serving for MlTrainingServiceServer. Wired into both TLS and non-TLS paths. - data_acquisition_service: Add tonic-health with set_serving for DataAcquisitionServiceServer. - ml/cuda_streams: Fix pre-existing unused variable clippy warning. All 8 services now have standard gRPC health checking enabled. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ml_training_service
Model training orchestration and lifecycle management for DQN, PPO, TFT, Mamba2, TLOB, and Liquid models with progress tracking and artifact storage.
Key Types
MlTrainingServiceImpl-- main gRPC serviceJobTracker-- training job state machineCheckpointManager-- model artifact persistence
Features
minimal(default) -- minimal ML feature set for financial modelsgpu-- SIMD GPU acceleration (requires CUDA)mock-data-- mock training data (testing, bypasses database)
Configuration
GRPC_PORT-- gRPC listen portDATABASE_URL-- PostgreSQL for job metadata and training history- Prometheus metrics on port 9094
Testing
SQLX_OFFLINE=true cargo test -p ml_training_service --lib