$ account deepjavalibrary
Deep Java Library (DJL) is an engine-agnostic Java framework for deep learning that provides a unified interface for building, training, and deploying models across multiple backends (PyTorch, TensorFlow, MXNet, ONNX Runtime). The framework features a modular architecture with core APIs for model management, tensor operations, and training, complemented by engine-specific implementations and extensions for various modalities (CV, NLP, audio, time series). DJL enables Java developers to work with deep learning without needing to learn the intricacies of underlying frameworks, offering translator patterns for data preprocessing/postprocessing, a model zoo system for accessing pre-trained models, and flexible training configurations. The library is actively maintained with recent commits, extensive testing, and continuous integration workflows.