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