djl: an engine-agnostic Java framework for building, training, and deploying deep learning models

What it solves

Deep Java Library (DJL) provides a native Java development experience for deep learning, allowing Java developers to build, train, and deploy models without needing to be experts in machine learning or rely on Python-centric tools. It removes the friction of integrating deep learning models into Java applications.

How it works

DJL is an engine-agnostic framework. This means it provides a high-level API that allows developers to switch between different deep learning engines (such as PyTorch, TensorFlow, or MXNet) without changing their Java code. It also automatically handles hardware acceleration, choosing between CPU and GPU based on the system configuration.

Who it’s for

Java developers who want to integrate deep learning capabilities into their applications, as well as those who want to use their existing Java IDEs and expertise to learn and train neural networks.

Highlights

• Engine Agnostic: Switch between different deep learning backends without rewriting code.
• Native Java Experience: Functions as a regular Java library, fitting into standard Java workflows and IDEs.
• CUPU/GPU Auto-selection: Automatically optimizes performance based on available hardware.
• Model Zoo Integration: Simplifies loading pre-trained models for tasks like object detection.
• Comprehensive Training API: Includes built-in blocks for constructing neural networks and utilities for training and saving models.