It's faster: TensorFlow 1.0 is incredibly fast!
lays the groundwork for even more performance improvements in the
tips & tricks
for tuning your models to achieve maximum speed. We'll soon publish
updated implementations of several popular models to show how to take
full advantage of TensorFlow 1.0 - including a 7.3x speedup on 8 GPUs
for Inception v3 and 58x speedup for distributed Inception v3 training
on 64 GPUs!
It's more flexible: TensorFlow 1.0 introduces a
high-level API for TensorFlow, with tf.layers, tf.metrics, and tf.losses
modules. We've also announced the inclusion of a new tf.keras module
that provides full compatibility with
popular high-level neural networks library.
It's more production-ready than ever: TensorFlow 1.0
promises Python API stability (details
making it easier to pick up new features without worrying about breaking
your existing code.
Python APIs have
been changed to resemble NumPy more closely. For this and other
backwards-incompatible changes made to support API stability going
forward, please use our handy
a domain-specific compiler for TensorFlow graphs, that targets CPUs
and GPUs. XLA is rapidly evolving - expect to see more progress in
Introduction of the
TensorFlow Debugger (tfdbg),
a command-line interface and API for debugging live TensorFlow
Android demos for
object detection and localization, and camera-based image
improvements: Python 3 docker images have been added, and
TensorFlow's pip packages are now PyPI compliant. This means
TensorFlow can now be installed with a simple invocation of
pip install tensorflow.
We're thrilled to see
the pace of development in the TensorFlow community around the
world. To hear more about TensorFlow 1.0 and how it's being used,
you can watch the
Summit talks on YouTube,
covering recent updates from higher-level APIs to TensorFlow on
mobile to our new
compiler, as well as the exciting ways that TensorFlow is being
for a link to the livestream and video playlist (individual talks will
be posted online later in the day).