1.0.0-M2

Highlights

Adds proper support for java 9 modules: https://github.com/eclipse/deeplearning4j/pull/9631 https://github.com/eclipse/deeplearning4j/pull/9626

As part of the same work flatbuffers has been upgraded to 1.12.1. This affects the samediff file format and the user interfaces. Flatbuffers as a file format is forwards and backwards compatible but if you have any issues please do let us know. The relevant files have been updated using the flatc compiler.

Removed rl4j: in continuing to cut unmaintained modules, the 1.0 will focus the framework on a few key use cases. This invites other folks to build external modules for a tightly maintained core that focuses on deployment, framework interop and training models in java.

Added new model zoo module called omnihub for dl4j and new samediff models. These can be found here: See more in the new omnihub section.

Migrated the snapshots to sonatype's new repository https://s01.oss.sonatype.org. More context can be found here:

Consolidated tests to platform-tests to allow for easy testing of behavior against different backends.

Adds proper support for jetson nano with curated binaries and an updated cuda 10.2

Adds Spark 3 support:

Reduce binary size using selective compilation:

Remove scala 11 support. Only supporting scala 2.12:

Extensive enhancements for samediff model training:

Nd4j/Samdiff/Libnd4j

Features and Enhancements

1. Add beginnings of graph optimization framework:

2. Many onnx model import improvements (add new ops):

3. Add new op subset frameworks: allows selective inclusion of operations to enable users to reduce binary size:

Bug Fixes

1. Update samediff api to allow dimensions as variables

2. Fix cuda shuffle:

3. Fix up conditions/matching:

4. ImageResize updates to improve compatibility with onnx:

Deeplearning4j

Features and Enhancements

1. Add Spark 3 support:

2. Added Deconvolution3D for keras import

3. Add full channels last support for 3d convolutions:

Bug Fixes

1. Fix confusion matrix count increments:

2. Fix Conv3D data format serialization:

Datavec

Features and Enhancements

1. Add LabelsSource to BagOfWordsVectorizer (thanks to XAI!):

2. Performance enhancement for mnist related datasetiterators:

Bug Fixes

1. Fix memory leak in datavec-arrow:

Omnihub

Launches new Omnihub module. Allows access to models from: https://github.com/KonduitAI/omnihub-zoo

A pretrained omnihub module will provide access to pretrained samediff and dl4j modules. This will also supplant the old dl4j zoo.

Modules will be made available from a Pretrained class:

Python4j

Clean up tests/consolidate tests to platform-tests