djl and JDLL
DJL is a comprehensive deep learning framework that provides the foundational infrastructure and model execution engines, while JDLL is a specialized client library built on top of similar principles specifically designed for running bioimage models, making them complementary tools for different use cases rather than direct competitors.
About djl
deepjavalibrary/djl
An Engine-Agnostic Deep Learning Framework in Java
Supports pluggable deep learning backends (PyTorch, TensorFlow, MXNet) with automatic CPU/GPU selection, enabling seamless engine switching without code changes. Provides a high-level NDArray API and composable neural network blocks for both inference and training, with built-in model zoo integration for pre-trained models. Includes ergonomic dataset handling, training configuration, and optimizer management through a fluent Java API that integrates natively with existing JVM ecosystems.
About JDLL
bioimage-io/JDLL
The Java library to run Deep Learning models
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