TensorFlow 2.2.0-rc1 release

TensorFlow 2.2.0 released a new release candidate. TensorFlow 2.2.0 Key features and improvements include the following:

String tensor scalar type from std::string replaced tensorflow::tstring
TF new Profiler 2 for CPU / GPU / TPU. It provides a performance analysis device and a host, comprising an input conduit and TF Ops.
Not recommended for use SWIG, but uses pybind11 to export C ++ functions to Python, which is part of an effort abandoned Swig.
tf.distribute：
- By using tf.keras.layers.experimental.SyncBatchNormalization newly added layer, adding to global synchronization BatchNormalization support. This layer will be synchronized BatchNormalization statistical information between all copies participate in synchronization training.
- Tf.distribute.experimental.MultiWorkerMirroredStrategy improve performance multi-GPU distributed training
  - The NVIDIA NCCL update to 2.5.7-1, for better performance and performance tuning.
  - Support reduce the gradient float16.
  - All experiments have reduced gradient compression support, to allow the reverse path superimposed gradient calculation polymerization.
tf.keras：
- Model.fit The major improvements:
  - It may be used in conjunction with custom logic Model.fit trained by covering Model.train_step.
  - Easily write new training cycle, without having to worry about all the features you Model.fit process (distribution policy, callback, data format, circular logic, etc.)
- Now, SavedModel Keras format built to support all layers (including index, the pretreatment layer and stateful layer RNN)
tf.lite：
- Enable TFLite experimental new converter by default.
XLA
- XLA can now build and run on Windows. All pre-built packages are shipped with XLA.
- It can be used on CPU and GPU "compiled or thrown" semantics tf.function enable XLA.

The new version includes a large number of bug fixes and other details can be found update:

https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0-rc1

TensorFlow 2.2.0-rc1 release

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