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DJL v0.3.0 release notes

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@zachgk zachgk released this 24 Feb 22:55
· 2584 commits to master since this release

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.