d06c99b0e1
- Fix format_push_string: write!() instead of push_str(&format!()) (25 sites) - Fix str_to_string: .to_owned() instead of .to_string() on &str (6 sites) - Fix unseparated_literal_suffix: add _ separator (6 sites) - Fix multiple_inherent_impl: merge split impl blocks in TGGN, TFT, OFI (3) - Fix else_if_without_else: add exhaustive else clauses (3 sites) - Fix if_then_some_else_none: use .then().transpose() (1 site) - Fix unwrap_in_result: replace expect() with match + ? (2 sites) - Fix wildcard_enum_match_arm: enumerate Storage variants explicitly (2) - Fix decimal_literal_representation: use hex for power-of-2 constants (5) - Fix rc_buffer: Arc<Vec<T>> → Arc<[T]> for OFI features - Fix needless_range_loop: convert to iterator patterns (17 sites) - Fix used_underscore_binding: remove prefix on used vars (6 sites) - Fix doc list item indentation (7 sites) - Allow too_many_arguments on ML training functions (4) - Allow multiple_unsafe_ops_per_block on CUDA FFI functions (3) - Allow upper_case_acronyms on SLSTM/MLSTM model names (2) - Add ML-crate pedantic allows: shadow, similar_names, type_complexity, indexing_slicing, partial_pub_fields, non_ascii_literal, same_name_method (following existing ml-labeling/ml-universe pattern) Result: cargo clippy --workspace -- -D warnings passes with zero warnings. All 2758+ lib tests pass (2 pre-existing backtesting failures unchanged). 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