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.

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.

Highlights

A number of bug fixes following the M1 release, thanks to the feedback from the community, allowed us to quickly sort out a few issues. This is a minor bug fix release to address short comings found with M1. Most fixes were related to keras import, the cnn/rnn helpers, and python4j.

Snapshots will also be published every 2 days automatically now https://github.com/eclipse/deeplearning4j/pull/9355 to get around sonatype ossrh deletion of snapshots every 3 days. This should increase robustness of the snapshots.

Worked around an issue with github actions pre emptively upgrading visual studio breaking the cuda builds: https://github.com/eclipse/deeplearning4j/pull/9364

Added backwards compatibility for centos 6 via a new linux-x86_64-compat classifier enabling use of older glibcs on centos 7:

https://github.com/eclipse/deeplearning4j/pull/9368 https://github.com/eclipse/deeplearning4j/pull/9368https://github.com/eclipse/deeplearning4j/pull/9373

A number of bugs were fixed with LSTM and CUDNN: https://github.com/eclipse/deeplearning4j/pull/9372

Known issues

https://github.com/eclipse/deeplearning4j/issues/9142 - avoid shuffle operations on gpu. Pre save data on cpu in mini batches. For more help, please post on the forums at https://community.konduit.ai/

Deeplearning4j

Features and Enhancements

• Add batch normalization support for RNNs: https://github.com/eclipse/deeplearning4j/pull/9338

• Disable old helpers by default https://github.com/eclipse/deeplearning4j/pull/9343

• Minor unit test fixes: https://github.com/eclipse/deeplearning4j/pull/9346

• Add keras support for cnn 1d NWHC: https://github.com/eclipse/deeplearning4j/pull/9353

• Move the warning about version check to tracing so it stops logging this during normal usage confusing users: https://github.com/eclipse/deeplearning4j/pull/9356

• Allow 1d convolutions to accept feed forward as input type: https://github.com/eclipse/deeplearning4j/pull/9365

• Remove the old benchmark suite and migrate it to contrib: https://github.com/eclipse/deeplearning4j/pull/9374

• Remove old MKLDNNLSTM helper (it never fully functioned anyways): https://github.com/eclipse/deeplearning4j/pull/9381

Bug fixes

• Fixed an issue with helper reflection ensuring the classes would be loaded properly https://github.com/eclipse/deeplearning4j/pull/9333 https://github.com/eclipse/deeplearning4j/pull/9350

• Fix minor workspace activation bug: https://github.com/eclipse/deeplearning4j/pull/9341

• Fixed compilation error when running anything more than jdk 8 and NIO buffers: https://github.com/eclipse/deeplearning4j/pull/9351

• Move logback to be a test dependency for some modules: https://github.com/eclipse/deeplearning4j/pull/9362

• Keras model import fixes for GlobalPooling: https://github.com/eclipse/deeplearning4j/pull/9378 https://github.com/eclipse/deeplearning4j/pull/9384

Nd4j

Features and Enhancements

• Add Eigen op as public ensuring easier use when running eigenvalue decomposition https://github.com/eclipse/deeplearning4j/pull/9328

Bug fixes

• Fixes minor issue with choice(..) op https://github.com/eclipse/deeplearning4j/pull/9360 thanks to https://github.com/Romira915

• Minor applyScalar typo fix: https://github.com/eclipse/deeplearning4j/pull/9385

Datavec

Features and Enhancements

Bug fixes

Fixed serialization bug with StringToTimeTransform: https://github.com/eclipse/deeplearning4j/pull/9377 thanks to community member https://github.com/yumg

Python4j

Features and Enhancements

• Made python4j's python path setting more robust by migrating from set path calls to add path calls: https://github.com/eclipse/deeplearning4j/pull/9386

Bug fixes

• Fixes bug with numpy import array jvm crashes: https://github.com/eclipse/deeplearning4j/pull/9348

Samediff

Features and Enhancements

Bug fixes

Fixed inconsistent conventions between SameDiffVariable getArr and getArrForName().. https://github.com/eclipse/deeplearning4j/pull/9357