Deep Java Library is an open-source, high-level, engine-agnostic Java framework for deep learning. Designed to provide a native Java development experience, DJL enables developers to build, train, and deploy deep learning models using familiar Java tools and IDEs. Its intuitive API abstracts the complexities of deep learning, allowing seamless integration into Java applications without requiring extensive machine learning expertise. DJL supports multiple deep learning engines, including Apache MXNet, PyTorch, and TensorFlow, offering flexibility and adaptability to various project requirements. Key Features and Functionality: - Engine Agnostic: Developers can write code once and run it on different deep learning engines without modification, facilitating flexibility and future-proofing applications. - Native Java API: DJL offers intuitive APIs that align with native Java concepts, simplifying the development process for Java programmers. - Model Zoo: Access a repository of pre-trained models, enabling quick integration of state-of-the-art AI capabilities into Java applications. - Ease of Deployment: DJL simplifies the deployment of deep learning models, allowing developers to bring in their own models or use existing ones from the Model Zoo, facilitating rapid deployment in production environments. - Hardware Optimization: The library automatically selects between CPU and GPU based on available hardware, ensuring optimal performance without manual configuration. Primary Value and Problem Solved: DJL addresses the gap in deep learning tools for Java developers by providing a comprehensive, easy-to-use framework that integrates seamlessly with existing Java applications. It eliminates the need for developers to switch to other programming languages to implement deep learning solutions, thereby reducing development time and complexity. By supporting multiple deep learning engines and offering a rich set of pre-trained models, DJL empowers Java developers to incorporate advanced AI capabilities into their applications efficiently.