deeplearning4j and djl
These are competitors offering overlapping deep learning capabilities for the JVM, though DL4J is more established with broader model import support while DJL emphasizes engine-agnostic flexibility across multiple backends.
About deeplearning4j
deeplearning4j/deeplearning4j
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
SameDiff provides graph-based automatic differentiation with TensorFlow protobuf import, while ND4J's 500+ operations leverage LibND4J's optimized C++ backend with multi-platform support (AVX2/512 CPU, CUDA GPU, ARM). DataVec handles ETL across diverse formats (HDFS, Spark, images, video, audio, CSV), and the stack integrates Python4J for cpython execution—enabling end-to-end ML pipelines entirely on the JVM without leaving Java.
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.
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