DJL v0.10.0 Release

DJL v0.10.0 brings the new engines PaddlePaddle 2.0 and TFLite 2.4.1, updates PyTorch to 1.7.1, and introduces several new features:

Key Features

Supports PaddlePaddle 2.0 engine inference: now you can run prediction using models trained in PaddlePaddle.
Introduces the PaddlePaddle Model Zoo with new models. Please see examples for how to run them.
Upgrades TFLite engine to v2.4.1. You can convert TensorFlow SavedModel to TFLite using this converter.
Introduces DJL Central to easily browse and view models available in DJL’s ModelZoo.
Introduces generic Bert Model in DJL (#105)
Upgrades PyTorch to 1.7.1

Enables listing input and output classes in ModelZoo lookup (#624)
Improves PyTorch performance by using PyTorch index over engine agnostic solution (#638)
Introduces various fixes and improvements for MultiThreadedBenchmark (#617)
Makes the default engine deterministic when multiple engines are in dependencies(#603)
Adds norm operator (similar to numpy.linalg.norm.html) (#579)
Refactors the DJL Trackers to use builder patterns (#562)
Adds the NDArray stopGradient and scaleGradient functions (#548)
Model Serving now supports scaling up and down (#510)

Renames DJL-Easy to DJL-Zero (#519)
Makes RNN operators generic across engines (#554)
Renames CheckpointsTrainingListener to SaveModelTrainingListener (#573)
Makes Initialization optional in training (#533)
Makes SoftmaxCrossEntropyLoss's fromLogit flag mean inputs are un-normalized (#639)
Refactors the Vocabulary builder API
Refactors the SymbolBlock with AbstractSymbolBlock (#491)

The training tests fail on GPU and Windows CPU if 3 engines(MXNet, PyTorch, TensorFlow) are loaded and run together

This release is thanks to the following contributors: