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.