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on_boarding.md

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graph LR
    User_Application_Layer["User Application Layer"]
    ML4A_Model_Abstraction_Layer["ML4A Model Abstraction Layer"]
    Media_Creative_Processing["Media & Creative Processing"]
    Data_Utility_Services["Data & Utility Services"]
    External_Model_Submodules["External Model Submodules"]
    User_Application_Layer -- "Invokes ML functions" --> ML4A_Model_Abstraction_Layer
    ML4A_Model_Abstraction_Layer -- "Exchanges processed media with" --> Media_Creative_Processing
    Media_Creative_Processing -- "Requests ML processing from" --> ML4A_Model_Abstraction_Layer
    ML4A_Model_Abstraction_Layer -- "Loads model implementations" --> External_Model_Submodules
    External_Model_Submodules -- "Supplies loaded models" --> ML4A_Model_Abstraction_Layer
    ML4A_Model_Abstraction_Layer -- "Utilizes core utilities" --> Data_Utility_Services
    Data_Utility_Services -- "Provides data/utilities" --> ML4A_Model_Abstraction_Layer
    User_Application_Layer -- "Directly manipulates media/canvas" --> Media_Creative_Processing
    User_Application_Layer -- "Directly uses utilities" --> Data_Utility_Services
    Media_Creative_Processing -- "Relies on file/data utilities" --> Data_Utility_Services
    click ML4A_Model_Abstraction_Layer href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml4a/ML4A_Model_Abstraction_Layer.md" "Details"
    click Media_Creative_Processing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml4a/Media_Creative_Processing.md" "Details"
    click External_Model_Submodules href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ml4a/External_Model_Submodules.md" "Details"
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Details

The ml4a project is structured around a core ML4A Model Abstraction Layer that provides simplified access to various deep learning models. The User Application Layer serves as the primary entry point for users, enabling them to invoke ML functionalities and directly interact with Media & Creative Processing components for handling audio and image data. The Media & Creative Processing layer not only manages media but also requests ML processing from the ML4A Model Abstraction Layer for creative effects. Both the ML4A Model Abstraction Layer and Media & Creative Processing rely on Data & Utility Services for foundational operations like file handling and data manipulation. The ML4A Model Abstraction Layer dynamically integrates External Model Submodules to load and utilize actual deep learning model implementations, forming a cohesive system for creative ML applications.

User Application Layer

The primary interface for users, typically Jupyter Notebooks or custom Python scripts, to interact with the ml4a library. This layer represents the user's own code that imports and utilizes the functionalities exposed by the ml4a library.

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ML4A Model Abstraction Layer [Expand]

Encapsulates various deep learning models, providing a simplified and consistent API for setup, inference, and specific model operations. This is the core ML functionality exposed to users.

Related Classes/Methods:

Media & Creative Processing [Expand]

Handles loading, saving, displaying, and basic manipulation of audio and image data, along with functionalities for creating, modifying, and visualizing dynamic canvases and masks for interactive creative applications.

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Data & Utility Services

Provides foundational helper functions for file operations, downloads, console output, and general data manipulation, alongside tools for generating, processing, and augmenting datasets.

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External Model Submodules [Expand]

Manages the dynamic loading and integration of external ML model implementations, which are often included as git submodules, providing the actual deep learning code.

Related Classes/Methods:

FAQ