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It’s been just a month since the release of TensorFlow 1.10, and the TensorFlow community introduces the newer version 1.11 with few major additions, lots of bug fixes and numerous performance improvements.

Major Features of TensorFlow 1.11.0:

  • Prebuilt binaries built for Nvidia GPU
  • Experimental tf.data integration for Keras
  • Preview support for eager execution on Google Cloud TPUs
  • Added multi-GPU DistributionStrategy support in tf.keras for model distribution
  • Added multi-worker DistributionStrategy support in Estimator
  • C, C++, and Python functions added for querying kernels
  • Added simple Tensor and DataType classes to TensorFlow Lite Java

Bug Fixes and Other Changes:

  • Default values for tf.keras RandomUniform, RandomNormal, and TruncatedNormal initializers changed
  • Added pruning mode for boosted trees
  • Old checkpoints do not get deleted by default
  • Total disk space for dumped tensor data limited to 100 GB.
  • Added experimental IndexedDatasets

Performance Improvements:

  • Enhanced performance for StringSplitOp & StringSplitV2Op
  • Regex replace operations improvised with max performance.
  • Toco compilation/execution fixed for Windows
  • Added GoogleZoneProvider class for detecting Google Cloud Engine zone tensorflow
  • Import enabled for tensor.proto.h
  • Added documentation clarifying the differences between tf.fill and tf.constant
  • Added selective registration target using the lite proto runtime
  • Support for bitcasting to and from uint32 and uint64
  • Estimator subclass added and can be created from a SavedModelEstimator
  • Added argument leaf index modes

Please see the full release notes for complete details on added features and changes. You can also check the GitHub repository to find various interesting use cases of TensorFlow.

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