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 over 6000 open-source repositories online.
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 & tricksfor 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:
pip install tensorflow.
Click herefor 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 Foldfor dynamic batching and tools like the Embedding Projector along with updates to 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 email@example.com, and at future events.
Source: Announcing TensorFlow 1.0