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
TensorFlow
Developer Summit talks on YouTube , covering recent updates from higher-level
APIs to TensorFlow on mobile to our new
XLA compiler, as well as
the exciting ways that TensorFlow is being used:
for a link to the livestream and video playlist
(individual talks will be
posted online later in the day).
Posted By: Amy McDonald Sandjideh, Technical Program Manager, TensorFlow In just its first
year , TensorFlow has helped researchers, engineers, artists, students, and
many others make progress with everything from language
translation to early
detection of skin cancer and preventing
blindness in diabetics . We're excited to see people using TensorFlow in over6000 open-source repositories
online .
Today, as part of the first annual TensorFlow Developer
Summit , hosted in Mountain View and livestreamed
around the world , we're announcing TensorFlow
1.0
It's faster: TensorFlow 1.0 is incredibly fast! XLA lays the groundwork for
even more performance improvements in the future, and tensorflow.org now includes 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 Keras , another popular
high-level neural networks library.
It's more production-ready than ever: TensorFlow 1.0 promises
Python API stability (details here ),
making it easier to pick up new features without worrying about breaking your
existing code.
Other highlights from TensorFlow
1.0 :
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 migration guide and conversion
script . Experimental APIs for Java
and Go Higher-level API modules tf.layers, tf.metrics, and tf.losses - brought over
from tf.contrib.learn
after incorporating skflow
and TF
Slim Experimental release of XLA , a domain-specific compiler
for TensorFlow graphs, that targets CPUs and GPUs. XLA is rapidly evolving -
expect to see more progress in upcoming releases. Introduction of the TensorFlow Debugger (tfdbg ), a
command-line interface and API for debugging live TensorFlow programs. New Android
demos for object detection and localization, and camera-based image stylization. Installation 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
TensorFlow
Developer Summit talks on YouTube , covering recent updates from higher-level
APIs to TensorFlow on mobile to our new
XLA compiler, as well as
the exciting ways that TensorFlow is being used:
Click here
for a link to the livestream and video playlist
(individual talks will be
posted online later in the day).The TensorFlow ecosystem continues to grow with new techniques like Fold
for dynamic batching and tools like the Embedding
Projector along with updatesto our existing tools like TensorFlow Serving . We're incredibly grateful to
the community of contributors, educators, and researchers who have made advances
in deep learning available to everyone. We look forward to working with you on
forums like GitHub
issues , Stack
Overflow , @TensorFlow , the discuss@tensorflow.org
group, and at future events.