Files

jgrusewski cadea0733f feat(ml_training_service): add ApproveModel/RejectModel handlers + proxy

- ml_training_service: handlers delegate to promotion_manager
- api_gateway: proxy pass-through for both new RPCs
- Proto messages added to service-side ml_training.proto

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-03 02:02:20 +01:00

ml/config

🚀 Wave 9: TFT INT8 Quantization Complete (20 Agents, TDD)

2025-10-15 21:38:04 +02:00

proto

feat(ml_training_service): add ApproveModel/RejectModel handlers + proxy

2026-03-03 02:02:20 +01:00

src

feat(ml_training_service): add ApproveModel/RejectModel handlers + proxy

2026-03-03 02:02:20 +01:00

tests

cleanup: delete 92 scattered .md files and .serena artifacts

2026-03-01 22:42:36 +01:00

build.rs

refactor: restructure repo — crates/, bin/, testing/ layout

2026-02-25 11:56:00 +01:00

Cargo.toml

feat: on-demand training dispatch via K8s Jobs with sidecar uploader

2026-02-26 12:43:17 +01:00

Dockerfile

fix: comprehensive audit — real brokers, deployment fixes, production safety

2026-02-25 00:32:10 +01:00

README.md

docs: create/update README.md for all services, CLI, and testing crates

2026-03-01 22:50:38 +01:00

requirements-tuner.txt

🎯 Wave 153: ML Hyperparameter Tuning - Production Ready & Validated

2025-10-13 16:10:55 +02:00

tuning_config_optimized.yaml

🚀 Wave 160 Phase 2: ML Training Infrastructure + TLOB Investigation

2025-10-14 10:42:56 +02:00

tuning_config.yaml

🚀 Wave 160 Phase 5: Complete ML Ensemble + Production Deployment (27 Agents)

2025-10-14 18:41:48 +02:00

README.md

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 service
JobTracker -- training job state machine
CheckpointManager -- model artifact persistence

Features

minimal (default) -- minimal ML feature set for financial models
gpu -- SIMD GPU acceleration (requires CUDA)
mock-data -- mock training data (testing, bypasses database)

Configuration

GRPC_PORT -- gRPC listen port
DATABASE_URL -- PostgreSQL for job metadata and training history
Prometheus metrics on port 9094

Testing

SQLX_OFFLINE=true cargo test -p ml_training_service --lib