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.