Celebrating TensorFlow’s First Year
Originally posted on Google
Research Blog
Posted by Zak Stone, Product Manager for TensorFlow, on
behalf of the TensorFlow team
It has been an eventful year since the
Google Brain
Team open-sourced
TensorFlow to accelerate machine learning research and
make
technology work better for everyone. There has been an amazing amount of activity
around the project: more than 480 people have contributed directly to
TensorFlow, including Googlers, external
researchers, independent programmers, students, and senior developers at other large
companies. TensorFlow is now
the most
popular machine learning project on GitHub.
With more than 10,000 commits in just twelve months, we’ve made
numerous performance
improvements,
added
support for distributed training,
brought TensorFlow to
iOS and
Raspberry
Pi, and integrated TensorFlow with widely-used
big data infrastructure. We’ve
also made TensorFlow accessible from
Go,
Rust and
Haskell,
released
state-of-the-art image classification models, and answered thousands of questions on
GitHub,
StackOverflow and
the
TensorFlow mailing
list along the way.
At Google, TensorFlow supports everything from large-scale product features to exploratory
research. We recently launched
major
improvements to Google Translate using TensorFlow (and
Tensor
Processing Units, which are special hardware accelerators for TensorFlow).
Project Magenta is
working on new reinforcement learning-based models that can
produce
melodies, and a visiting PhD student recently worked with the Google Brain team to
build a TensorFlow model that can
automatically
interpolate between artistic styles.
DeepMind has also
decided to
use TensorFlow to power all of their research – for example, they recently produced
fascinating
generative models of speech and music based on raw audio.
We’re especially excited to see how people all over the world are using TensorFlow. For
example:
- Australian marine biologists are using TensorFlow to find
sea cows in tens of thousands of hi-res photos to better understand their
populations, which are under threat of extinction.
- An enterprising Japanese cucumber farmer trained a model with TensorFlow to sort
cucumbers by size, shape, and other characteristics.
- Radiologists have adapted TensorFlow to identify signs of Parkinson’s disease
in medical scans.
- Data scientists in the Bay Area have rigged up TensorFlow and the Raspberry Pi to
keep track of the
Caltrain.
We’re committed to making sure TensorFlow scales all the way from research to production and
from the tiniest Raspberry Pi all the way up to server farms filled with GPUs or TPUs. But
TensorFlow is more than a single open-source project – we’re doing our best to foster an
open-source ecosystem of related software and machine learning models around it:
- The TensorFlow
Serving project simplifies the process of serving TensorFlow models in
production.
- TensorFlow “Wide
and Deep” models combine the strengths of traditional linear models and modern deep
neural networks.
- For those who are interested in working with TensorFlow in the cloud, Google Cloud Platform recently launched Cloud Machine Learning, which offers
TensorFlow as a managed service.
Furthermore,
TensorFlow’s repository of
models continues to grow with contributions from the community, with
more than 3000 TensorFlow-related
repositories listed on GitHub alone! To participate in the TensorFlow community, you
can follow our new Twitter account (
@tensorflow),
find us on GitHub,
ask and answer questions on
StackOverflow, and join the
community
discussion list.
Thanks very much to all of you who have already adopted TensorFlow in your cutting-edge
products, your ambitious research, your fast-growing startups, and your school projects;
special thanks to everyone who has
contributed directly to the
codebase. In collaboration with the global machine learning community, we look forward to
making TensorFlow even better in the years to come!
