Deeplearning4j Suite Overview

Introduction to core Deeplearning4j concepts.

Eclipse DeepLearning4J

Eclipse Deeplearning4j is a suite of tools for running deep learning on the JVM. It's the only framework that allows you to train models from java while interoperating with the python ecosystem through a mix of python execution via our cpython bindings, model import support, and interop of other runtimes such as tensorflow-java and onnxruntime.

Consider going to our Quickstart for an overview of where to get started. If you have dependency issues please use our Required Dependencies guide.

The use cases include importing and retraining models (Pytorch, Tensorflow, Keras) models and deploying in JVM Micro service environments, mobile devices, IoT, and Apache Spark. It is a great compliment to your python environment for running models built in python, deployed to or packaged for other environments.

Deeplearning4j has several submodules including:

1. Samediff: a tensorflow/pytorch like framework for execution of complex graphs. This framework is lower level, but very flexible. It's also the base api for running onnx and tensorflow graphs.

2. Nd4j: numpy ++ for java. Contains a mix of numpy operations and tensorflow/pytorch operations.

3. Libnd4j: A lightweight, standalone c++ library enable math code to run on different devices. Optimizable for running on a wide variety of devices.

4. Python4j: A python script execution framework easing deployment of python scripts in to production.

5. Apache Spark Integration: An integration with the Apache Spark framework enabling execution of deep learning pipelines on spark

6. Datavec: A data transformation library converting raw input data to tensors suitable for running neural networks on.

How to use this website

This website follows the divio framework layout. This website has several sections of documentation following this layout. Below is an overview of the sections of the site:

1. Multi project contains all cross project documentation such as end to end training and other whole project related documentation. This should be the default entry point for those getting started.

2. Deeplearning4j contains all of the documentation related to the core deeplearning4j apis such as the multi layer network and the computation graph. Consider this the high level framework for building neural networks. If you would like something lower level like tensorflow or pytorch, consider using samediff

3. Samediff contains all the documentation related to the samediff submodule of ND4j. Samediff is a lower level api for building neural networks similar to pytorch or tensorflow with built in automatic differentiation.

4. Datavec contains all the documentation related to our data transformation library datavec.

5. Python4j contains all the documentation related to our cpython execution framework python4j.

6. Libnd4j contains all the documentation related to our underlying C++ framework libnd4j.

7. Apache Spark contains all of the documentation related to our Apache Spark integration.

8. Concepts/Theory contains all of the documentation related to general mathematical or computer science theory needed to understand various aspects of the framework.

Open Source

The libraries are completely open-source, Apache 2.0 under open governance at the Eclipse foundation. The Eclipse Deeplearning4j project welcomes all contributions. See our community and our Contribution guide to get involved.

JVM/Python/C++

Deeplearning4j can either be a compliment to your existing workflows in python and c++ or a standalone library for you to build and deploy models. Use what components you find useful.