DJL v0.6.0 release notes
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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
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