After a sneak-peek into TensorFlow’s release candidates 1.6.0-rc0 and 1.6.0-rc1, its major release 1.6.0 is finally here!
Tensorflow 1.6.0 includes two new breaking changes, feature improvements and bug fixes in its list. The previous version, TensorFlow 1.5, introduced us to jaw dropping inclusions such as TensorFlow Lite developer preview and TensorFlow Eager Execution.
Let’s have a look at what’s in store with the newly released TensorFlow version 1.6.0.
The two most important changes include:
- The prebuilt binaries are now built against CUDA 9.0 and cuDNN 7
- These prebuilt binaries would now use AVX instructions, which may break TensorFlow on older CPUs.
List of major feature improvements:
- A new optimizer internal API for non-slot variables.
- tf.estimator.{FinalExporter,LatestExporter} can now export stripped SavedModels, which improves forward compatibility of the SavedModel.
- FFT support has been added to XLA CPU/GPU.
- Also, Android TF can now be built with CUDA acceleration on compatible Tegra devices.
Few API changes in 1.6.0:
- Introducing prepare_variance boolean with default setting to False for backward compatibility.
- Move layers_dense_variational_impl.py to layers_dense_variational.py.
Minor bug fixes include:
- Addition of a client-side throttle in the Google Cloud Storage (GCS).
- Addition of a FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.
In addition to these, TensorFlow 1.6.0 includes a second version of the Getting started guide exclusively for newcomers in Machine learning. Not only this, documentation for TPUs is a must-watch. It also includes certain other changes which you will be able to read at the GitHub version release page.