DJL v0.8.0 release note

DJL 0.8.0 is a release closely following 0.7.0 to fix a few key bugs along with some new features.

Key Features

• Search model zoo with criteria
• Standard BERT transformer and WordpieceTokenizer for more BERT tasks
• Simplify MRL and Remove Anchor
• Simplify and Standardize CV Model
• Improve Model describe input and output
• String NDArray support (only for TensorFlow Engine)
• Add erfinv operator support
• MXNet 1.6.0 backward compatibility, now you can switch MXNet versions (1.6 and 1.7) using DJL 0.8.0
• Combined pytorch-engine-precxx-11 and pytorch-engine package
• Upgrade onnx runtime from 1.3.1 to 1.4.0

Documentation and examples

• Object Detection with TensorFlow saved model example
• Text Classification with TensorFlow BERT model example
• Added more documentation on TensorFlow engine.

Bug Fixes

• Fixed MXNet multithreading bug and updated multi-threading documentation
• Fixed TensorFlow 2.3 native binaries for Windows platform

Known issues

• You need to add your own Translator when loading image classification models(ResNet, MobileNet) from TensorFlow model Zoo, refer to the example here.

Contributors

Thank you to the following community members for contributing to this release:

Dennis Kieselhorst, Frank Liu, Jake Cheng-Che Lee, Lai Wei, Qing Lan, Zach Kimberg, uniquetrij

DJL v0.6.0 release notes

DJL 0.6.0 brings stable Android support, ONNX Runtime experimental inference support, experimental training support for PyTorch.

Key Features

• Stable Android inference support for PyTorch models
  • Provide abstraction for Image processing using ImageFactory
• Experimental support for inference on ONNX models
• Initial experimental training and imperative inference support for PyTorch engine
• Experimental support for using multi-engine
• Improved usage for NDIndex - support for ellipsis notation, arguments
• Improvements to AbstractBlock to simplify custom block creation
• Added new datasets
  • FashionMNIST dataset
  • StanfordMovieReview dataset

Documentation and examples

• Added Sentiment Analysis training example
• Updates the DJL webpage with new demos

Breaking changes

• ModelZoo Configuration changes
• ImageFactory changes
• Please refer to javadocs for minor API changes

Known issues

• Issue with training with MXNet in multi-gpu instances

Contributors

Thank you to the following community members for contributing to this release:

Christoph Henkelmann, Frank Liu, Jake Lee, JonTanS, Keerthan Vasist, Lai Wei, Qing, Qing Lan, Victor Zhu, Zach Kimberg, ai4java, aksrajvanshi

DJL v0.5.0 release notes

DJL 0.5.0 release brings TensorFlow engine inference, initial NLP support and experimental Android inference with PyTorch engine.

Key Features

• TensorFlow engine support with TensorFlow 2.1.0
  • Support NDArray operations, TensorFlow model zoo, multi-threaded inference
• PyTorch engine improvement with PyTorch 1.5.0
• Experimental Android Support with PyTorch engine
• MXNet engine improvement with MXNet 1.7.0
• Initial NLP support with MXNet engine
  • Training LSTM models
  • Support various text/word embedding, Seq2Seq use cases
  • Added NLP datasets
• New AWS-AI toolkit to integrate with AWS technologies
  • Load model from s3 buckets directly
• Improved model-zoo with more models
  • Check out new models in Basic Model Zoo, MXNet Model Zoo, PyTorch Model Zoo, TensorFlow Model Zoo

Documentation and examples

• Checkout our new java doc site with version support
• New tutorials and examples
  • Train LSTM models
  • Train Seq2Seq models
• New demos in our djl-demo repository
  • Covid-19 detection with DJL
  • Serverless model serving with DJL
• Checkout our latest blog posts:
  • Implement Object Detection with Pytorch in Java in 5minutes
  • Adopting machine learning in your microservices with DJL (Deep Java Library) and Spring Boot
  • AI Toolbox for Java developers (Chinese)

Breaking changes

• We moved our repository module under api module. There will be no 0.5.0 version for ai.djl.repository, use ai.djl.api instead.
• Please refer to DJL Java Doc for some minor API changes.

Know issues:

• Issue when using multiple Engines at the same time: #57
• Issue using DJL with Quarkus: #67
• We saw random crash on mac for transfer Learning on CIFAR-10 Dataset example on Jupyter Notebook. Command line all works.

DJL v0.4.1 release notes

DJL 0.4.1 release includes an important performance Improvement on MXNet engine:

Performance Improvement:

• Cached MXNet features. This will avoid MxNDManager.newSubManager() to repeatedly calling getFeature() which will make JNA calls to native code.

Known Issues:

Same as v0.4.0 release:

• PyTorch engine doesn't fully support multithreaded inference. You may see random crashes. Single-threaded inference is not impacted. We expect to fix this issue in a future release.
• We saw random crash on mac for “transfer Learning on CIFAR-10 Dataset” example on Jupyter Notebook. Command line all works.

DJL v0.4.0 release notes

DJL 0.4.0 brings PyTorch and TensorFlow 2.0 inference support. Now you can use these engines directly from DJL with minimum code changes.

Note: TensorFlow 2.0 currently is in PoC stage, users will have to build from source to use it. We expect TF Engine finish in the future releases.

Key Features

• Training improvement
  • Add InputStreamTranslator
• Model Zoo improvement
  • Add LocalZooProvider
  • Add ListModels API
• PyTorch Engine support
  • Use the new ai.djl.pytorch:pytorch-native-auto dependency for automatic engine selection and a simpler build/installation process
  • 60+ methods supported
• PyTorch ModelZoo support
  • Image Classification models: ResNet18 and ResNet50
  • Object Detection model: SSD_ResNet50
• TensorFlow 2.0 Engine support
  • Support on Eager Execution for imperative mode
  • 30+ methods support
• TensorFlow ModelZoo support
  • Image Classification models: ResNet50, MobileNetV2

Breaking Changes

There are a few changes in API and ModelZoo packages to adapt to multi-engine support. Please follow our latest examples to update your code base from 0.3.0 to 0.4.0.

Known Issues

1. PyTorch engine doesn't fully support multithreaded inference. You may see random crashes. Single-threaded inference is not impacted. We expect to fix this issue in a future release.
2. We saw random crash on mac for “transfer Learning on CIFAR-10 Dataset” example on Jupyter Notebook. Command line all works.

DJL v0.3.0 release notes

This is the v0.3.0 release of DJL

Key Features

• Use the new ai.djl.mxnet:mxnet-native-auto dependency for automatic engine selection and a simpler build/installation process
• New Jupyter Notebook based tutorial for DJL
• New Engine Support for:
  • FastText Engine
  • Started implementation on a PyTorch Engine
• Simplified training experience featuring:
  • TrainingListeners to easily provide full featured training
  • DefaultTrainingConfig now contains a default optimizer and initializer
  • Easier to transfer from examples to your own code
• Specify the random seed for reproducible training
• Run with multiple engines and specify the default using the "DJL_DEFAULT_ENGINE" environment variable or "ai.djl.default_engine" system property
• Updated ModelZoo design to support unified loading with Criteria
• Simple random Hyperparameter optimization

Breaking Changes

DJL is working to further improve the ease of use and correctness of our API. To that end, we have made a number of breaking changes for this release. Here are a few of the areas that had breaking changes:

• Renamed TrainingMetrics to Evaluator
• CompositeLoss replaced with AbstractCompositeLoss and SimpleCompositeLoss
• Modified MLP class
• Remove Matrix class
• Updates to NDArray class

Known Issues

1. RNN operators do not working with GPU on Windows.
2. Only CUDA_ARCH 37, 70 are supported for Windows GPU machine.

DJL v0.2.1 release notes

This is the v0.2.1 release of DJL

Key Features

1. Added support for Windows 10.
2. CUDA 9.2 support for all supported Operating systems (Linux, Windows)

Known Issues

1. RNN operators do not working with GPU on Windows.
2. Only CUDA_ARCH 37, 70 are supported for Windows GPU machine.

DJL v0.2.0 release notes

This is the v0.2.0 release for DJL
Key Features

1. Deep learning engine agnostic High-level API for Training and Prediction.
2. Dataset API, to create objects based out of different dataset formats, that work with training batchifier.
3. MXNet-Model-Zoo with pre-trained models for Image Classification, Object Detection, Segmentation and more.
4. Jupyter Notebooks and Examples to help get started with Training and predicting models with DJL

Engines currently supported

1.Apache MXNet

Javadocs

The javadocs are available here