[MUSIC PLAYING]
PAIGE BAILEY: Greetings, TensorFlow enthusiasts,
I'm Paige Bailey for "The Developer Show."
And I'm at the TensorFlow Dev Summit,
where members of the TensorFlow engineering team
just shared major release announcements with the world.
This past year it's been a busy one for the TensorFlow team.
We've launched courses on Udacity and deep learning AI,
we released many new products, like TensorFlow Graphics, ML,
AR, TF Enterprise, and we got to meet so many of you
at events like the TensorFlow Roadshows, TensorFlow Doc
Sprints, Google Summer of Code, and Google Code-in.
This year is shaping up to be just as exciting.
And now, without further ado, the TensorFlow Dev
Summit Top 5.
First up, the release of TensorFlow 2.2.
We launched TF 2.0 last year to very positive feedback
from the community.
As a recap, we've also added TPU and multi-worker GPU
support as part of TF 2.1.
You can now scale from single-node multi
GPU to multi-node multi-GPU to TPU pods
with just a single line code change.
We've also been excited to see the amazing libraries
that you've built with TensorFlow
2.X and the speed at which developers are migrating
their existing tools.
But we heard your feedback loud and clear.
TensorFlow 2.0 is fast, but you wanted it to be even faster,
and to have tools to better understand your performance.
That's why TensorFlow 2.2 was created
with an emphasis on performance, compatibility and stability.
We're building on the momentum from last year and have many
improvements underway, including a rewrite
of TensorFlow's internals into C++ to make it even faster.
We're also creating a NumPy-like eager core,
and including all the low level operations
you need as part of TF for our ops.
We're also excited to release a performance analysis
framework that lets you inspect and intuitively visualize
the execution timeline of accelerators, like GPUs, TPUs,
and more.
It has low overhead, so on GPUs, typically about 2% overhead
when profiling or tracing.
And it's highly customizable.
Profile when you want, select duration and desired
performance counters.
Simply add a build dependency and you're good to go.
Be sure to share your visualizations on social media
with the hashtag #poweredbytf.
Coming in at number 3, we're expanding
the way students and researchers can get involved
in the TensorFlow community.
Everything from Google Summer of Code,
applications open on March 16, and credits for TPUs
as part of TF Research Cloud.
There's so many ways for TensorFlow
to help you with your academic work.
We know that machine learning is a powerful way
to solve challenging real world problems.
However, machine learning and AI raise many questions
around the best approach to building a system that is
both an inclusive and secure.
That's why number 4 on our list is TensorFlow's suite
of responsible AI tools.
For example, Fairness Indicators can
help you evaluate simple, but critical fairness metrics
across thresholds of models.
The What-If tool, directly integrated into TensorBoard,
can help with interpretability, providing a rationale for why
a prediction was made using textual and visual components
of the data.
And TensorFlow Federated, now updated for TensorFlow 2.0,
is a library that helps with training data on device.
We're excited to work on this important part
of the TensorFlow ecosystem with all of you, the TensorFlow
community.
Number 5 on our list, you can share your work
with the broader TensorFlow ecosystem,
and get recognized for your contributions.
First up, we're announcing the responsible AI Dev Post
Challenge, which gives you the ability
to use the machine learning fairness tools I just mentioned
to create equitable AI models.
We're delighted to announce that Kaggle competitions now support
TensorFlow 2.2 and TPUs.
Be sure to check out the new kernels,
and try migrating code from CPU to GPU to TPU.
For all the announcements and updates from the TensorFlow Dev
Summit, make sure to check out the links in the description
below.
I'm Paige Bailey for "The Developer Show."
Thanks for watching.