1.0.0-M1
Highlights
In light of the coming 1.0, the project has decided to cut a number of modules before the final release. These modules have not had many users in the past and have created confusion for many users just trying to use a few simple apis. Many of these modules have not been maintained.
There will likely be 1 or 2 more milestone releases before the final 1.0. These should be considered checkpoints.
These modules include:
Arbiter
Jumpy
Datavec modules for video, audio, audio, sound. The computer vision datavec module
will continue to be available.
Tokenizers: The tokenizers for chinese, japanese, korean were imported from other frameworks
and not really updated.
Scalnet, Nd4s: We removed the scala modules due to the small user base. We welcome 3rd party enhancements
to the framework for syntatic sugar such as kotlin and scala. The framework's focus will be on providing
the underlying technology rather than the defacto interfaces. If there is interest in something higher level, please discuss it on community forums
ARM support: We have included armcompute modules for core convolution routines. These routines can be found here
TVM: We now support running TVM modules. Docs coming soon.
We've updated our shaded modules to newer versions to mitigate security risks. These modules include: 1. jackson 2. guava
Cuda 11: We've upgraded dl4j and associated modules to support cuda 11 and 11.2.
A more modular model import framework supporting tensorflow and onnx: 1. Model mapping procedures loadable as protobuf 2. Defining custom rules for import to work around unsupported or custom layers/operations 3. Op descriptor for all operations in nd4j
This will enable users to override model import behavior to run their own custom models. This means, in most circumstances, there will be no need to modify model import core code anymore. Instead, users will be able to provide definitions and custom rules for their graphs.
Users will be expected to convert their models in an external process. This means running standalone conversions for their models. This extends to keras import as well. Sometimes users convert their models in production directly from keras.
The workflow going forward is to ensure that your model is converted ahead of time to avoid performance issues with converting large models.
Removed ppc from nd4j-native-platform and nd4j-cuda-platform. If you need this architecture, please contact us or build from source.
Added more support for avx/mkldnn/cudnn linked acceleration in our c++ library. We now have the ability to distribute more combinations of pre compiled math kernels via different combinations of classifiers. See the ADR here for more details.
The class loader is now overridable. This is useful for OSGI and application server environments.
We've upgraded arrow to 4.0.0 enabling the associated nd4j-arrow and datavec-arrow modules to be used without netty clashes.
Deeplearning4j
Bug fixes
Improved keras model import support for NWHC as well as NCHW input formats for both rnn and cnn
Nd4j
Features and Enhancements
CTC Loss: We now have basic support for CTC loss in nd4j. This will enable the import of CTC loss based models for speech recognition as well as OCR.
Bug fixes
Python4j
Features and Enhancements
Rewritten and more stable python execution. This allows better support for multi threaded environments.
Bug fixes
Contributors: https://github.com/eclipse/deeplearning4j/issues?q=is%3Apr+author%3Amjlorenzo305