Files

jgrusewski dd62f3fcfd refactor: eliminate candle from entire workspace — tests, examples, Cargo.toml

Final cleanup:
- 61 test files + 5 example files: candle imports replaced
- 8 testing/integration files: migrated to cudarc/ml-core types
- 3 services/trading_service test files: migrated
- Root Cargo.toml: candle-core, candle-nn removed from [workspace.dependencies]
- crates/ml/Cargo.toml: candle-nn dependency removed
- testing/e2e/Cargo.toml: candle-core dependency removed

Zero active candle_core/candle_nn/candle_optimisers code references remain.
Zero candle dependency declarations in any Cargo.toml.
Remaining "candle" strings are exclusively in doc comments.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-18 00:53:47 +01:00

ml/config

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

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

src

refactor(ml): delete mixed_precision module — BF16 unconditional on CUDA

2026-03-16 16:11:48 +01:00

tests

refactor(ml): delete mixed_precision module — BF16 unconditional on CUDA

2026-03-16 16:11:48 +01:00

build.rs

refactor: rewire all service build.rs to proto/ root directory

2026-03-03 20:34:09 +01:00

Cargo.toml

refactor: eliminate candle from entire workspace — tests, examples, Cargo.toml

2026-03-18 00:53:47 +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