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Deep Java Library (DJL) v0.4.0 Release Notes

Release Date: 2020-03-30 // about 4 years ago

  • 👍 DJL 0.4.0 brings PyTorch and TensorFlow 2.0 inference support. Now you can use these engines directly from DJL with minimum code changes.

    🚀 Note: TensorFlow 2.0 currently is in PoC stage, users will have to build from source to use it. We expect TF Engine finish in the future releases.

    Key Features

    • Training improvement
      • Add InputStreamTranslator
    • Model Zoo improvement
      • Add LocalZooProvider
      • Add ListModels API
    • 👍 PyTorch Engine support
      • Use the new ai.djl.pytorch:pytorch-native-auto dependency for automatic engine selection and a simpler build/installation process
      • 60+ methods supported
    • 👍 PyTorch ModelZoo support
      • Image Classification models: ResNet18 and ResNet50
      • Object Detection model: SSD_ResNet50
    • 👍 TensorFlow 2.0 Engine support
      • Support on Eager Execution for imperative mode
      • 30+ methods support
    • 👍 TensorFlow ModelZoo support
      • Image Classification models: ResNet50, MobileNetV2

    💥 Breaking Changes

    ⚡️ There are a few changes in API and ModelZoo packages to adapt to multi-engine support. Please follow our latest examples to update your code base from 0.3.0 to 0.4.0.

    Known Issues

    🚀 1. PyTorch engine doesn't fully support multithreaded inference. You may see random crashes. Single-threaded inference is not impacted. We expect to fix this issue in a future release. 💻 2. We saw random crash on mac for “transfer Learning on CIFAR-10 Dataset” example on Jupyter Notebook. Command line all works.