# 1.0.0-M2 ## Highlights Adds proper support for java 9 modules: \ \ 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 . 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:\ \ 4. Update onednn to 2.2: 5. Add updated jetson nano support: 6. Enhance codegen exposing more functions for samediff: \ \ 7. Add new samediff eager mode (mainly used for model import use cases):\ \ [**https://github.com/eclipse/deeplearning4j/pull/9535**](https://github.com/eclipse/deeplearning4j/pull/9535)\ [**https://github.com/eclipse/deeplearning4j/pull/9533**](https://github.com/eclipse/deeplearning4j/pull/9533) 8. Add dimensions as input variables:\ ### 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: 5. Rewrite compat sparse to dense op: 6. Fix creation of string scalar ndarrays: 7. Fix serialization with conv/pooling3d: ## 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: 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://deeplearning4j.konduit.ai/release-notes/1.0.0-m2.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.