haipipe-nn-2-tuner

Standardize the interface for machine learning model tuning by wrapping diverse algorithms with a uniform contract. This ensures consistent data handling, training, inference, and model serialization across different libraries.

Integrate a new time-series forecasting algorithm into an existing pipeline by creating a Tuner that adheres to the algorithm wrapper contract. This prevents modifications to the pipeline's orchestration logic.

Build a custom algorithm wrapper by inheriting from ModelTuner and implementing get_tfm_data, fit, infer, save_model, and load_model methods, alongside a standalone transform_fn for data conversion.

The model tuning wrapper handles data transformation by converting input data into algorithm-specific formats. It also manages model serialization for saving and loading models from external libraries.

A standardized contract for managing machine learning model lifecycles ensures consistent model fitting, inference, and serialization. It solves data handling discrepancies when using diverse external libraries.

The strict contract for wrapping machine learning algorithms requires adherence to abstract methods and a standalone transform_fn. This demands precise implementation to ensure algorithm agnosticism functions correctly.