graph LR
Model_Inference_Core["Model Inference Core"]
Model_Optimization_Quantization_["Model Optimization (Quantization)"]
Model_Export_Utility["Model Export Utility"]
Model_Optimization_Quantization_ -- "provides optimized outputs to" --> Model_Inference_Core
Model_Inference_Core -- "provides input to" --> Model_Export_Utility
The model2vec subsystem is designed for streamlined model deployment and inference. It comprises three key components: Model Optimization (Quantization), which prepares models for efficient execution; Model Inference Core, which manages and performs the actual predictions using these optimized models; and Model Export Utility, which facilitates the conversion of models from the inference core into various deployable formats. This architecture ensures that models are optimized for performance, efficiently executed, and readily adaptable for diverse deployment environments.
This component serves as the central hub for managing and executing model inference. It handles loading pre-trained models, performing predictions, and evaluating model performance. It provides the primary API for users to interact with trained models for real-world applications.
Related Classes/Methods:
This component focuses on enhancing model efficiency and reducing resource consumption. It specifically implements quantization techniques to optimize models for faster inference and smaller memory footprint, preparing them for efficient deployment.
Related Classes/Methods:
This component provides a dedicated utility for converting trained models into platform-agnostic, optimized formats like ONNX. This enables seamless deployment across various environments and frameworks, ensuring interoperability and efficiency.
Related Classes/Methods: