Model_Training_Tuning_Engine.md

graph LR
    Tuning_API["Tuning API"]
    Tuning_Orchestrator["Tuning Orchestrator"]
    Hypermodel_Definition["Hypermodel Definition"]
    Tuning_History_Management["Tuning History Management"]
    Model_Creation_Persistence["Model Creation & Persistence"]
    Tuning_API -- "invokes" --> Tuning_Orchestrator
    Tuning_Orchestrator -- "utilizes" --> Hypermodel_Definition
    Tuning_Orchestrator -- "writes to" --> Tuning_History_Management
    Tuning_Orchestrator -- "passes model to" --> Model_Creation_Persistence

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Details

The Model Training & Tuning Engine subsystem is responsible for the entire lifecycle of machine learning model training, hyperparameter optimization, evaluation, and persistence within the libra project. It embodies the project's architectural bias towards usability and abstraction, providing a streamlined interface for complex ML tasks.

Tuning API

Serves as the primary external interface or entry point for users or other system components to initiate a model tuning task. It abstracts the underlying complexity of the tuning engine, providing a simplified interaction point.

Related Classes/Methods:

• libra.queries.tune:670-742

Tuning Orchestrator

Manages and executes the hyperparameter tuning process for various machine learning model types (e.g., regression, classification, CNNs) and tuning algorithms (e.g., Hyperband). It encapsulates the core logic for optimizing model performance and coordinating the tuning workflow.

Related Classes/Methods:

• libra.modeling.tuner:tuneReg
• libra.modeling.tuner:tuneClass
• libra.modeling.tuner:tuneCNN
• libra.modeling.tuner:tuneHyperband

Hypermodel Definition

Defines the specific architectural structure and the search space for hyperparameters relevant to different machine learning model types, such as Convolutional Neural Networks. This component provides the blueprints for models that the Tuning Orchestrator will optimize.

Related Classes/Methods:

• libra.modeling.tuner:CNNHyperModel

Tuning History Management

Provides a centralized mechanism for recording and managing historical data, metrics, and results generated during tuning sessions. This component is crucial for tracking experiment progress, reproducibility, and analysis of tuning outcomes.

Related Classes/Methods:

• libra.modeling.tuner:tuner_hist

Model Creation & Persistence

Handles the final creation, saving, and loading of trained machine learning models, ensuring they can be persisted and reused across different sessions or deployments. This component is crucial for the "model persistence" aspect of the engine.

Related Classes/Methods:

• libra.modeling.prediction_model_creation

FAQ