This contest has ended - check out the current TensorFlow hackathon ending May 11, 2020 @ 11:45pm EDT!
TensorFlow 2.0 has arrived, with a focus on ease of use, developer productivity, and scalable, production-ready machine learning workloads. Some of the features you can expect to see in TF 2.0:
- Easy model building with Keras and eager execution.
- Robust model deployment in production on any platform.
- Powerful experimentation for research.
- Simplifying the API by cleaning up deprecated APIs and reducing duplication.
- Have low-level control with the Keras Subclassing API and tf.Module.
What Should I Hack?
Use our alpha release of TensorFlow 2.0 to do something nifty: build a model, a mobile or web application, an art installation, or something else entirely! The sky is the limit - and creativity is encouraged. We're excited to see what you build!
Note: This release of TF 2.0 is an alpha, on the bleeding edge of development, and not yet intended for production use. If you run into any snags or encounter any bugs, please submit an issue on GitHub.
Developers of all ages, backgrounds, and skill levels are encouraged to submit projects. Teams may have between 1 and 6 participants. Participants are encouraged to expand the scope of an existing TensorFlow 1.x project, to migrate and continue work on a historic TensorFlow 1.x project; or to create an entirely new software solution using TensorFlow 2.0.
Applications may submit projects that integrate with other third-party SDKs, APIs, and services, provided the participant is authorized to use them. There are no restrictions on cloud service providers, operating systems, or hardware platforms. All participants will be obliged to work under the TensorFlow Code of Conduct.
Note: government officials, corporations, and employees of Google or DevPost are not eligible to win prizes, but may submit projects. See #PoweredByTF Challenge terms for further restrictions.
Build or migrate a functioning TensorFlow 2.0-powered solution!
- (Optional) Submit a 2-5 minute demo video hosted on YouTube, Vimeo, or Youku. Your video should include a demo of your working application.
- Please submit at least one image or screenshot of your solution.
- Make sure all of your code has been uploaded to a public repo on GitHub, and a link to the repo has been included in your application.
All projects must be submitted by May 6th, 2019, at 11:45PM Pacific Time. Judging will take place during Google I/O, from May 6th through May 9th. Contest winners will be informed of their project's status on the evening of May 9th.
$150,000 in prizes
Have your project featured at TensorFlow World!
Round-trip airfare and lodging for your project team to travel to Santa Clara, California, and showcase your solution at TensorFlow World! For more details, check the Rules portion of the website.
*$5,000 per team member (up to 6 members max)*
Present your project to the TensorFlow engineering team!
Runners-up will have the chance to virtually present their project to the TensorFlow engineering team at TensorFlow Connect.
Share your project with the TensorFlow Community!
Make your ML solution famous! 40 additional winners will have their projects highlighted to the TensorFlow community via our blog, newsletter, YouTube channel, or social media accounts.
Submitting to this hackathon could earn you:
How to enter
- Register for the #PoweredByTF 2.0 Challenge on DevPost!
- If you haven’t tried it yet, learn more about TensorFlow 2.0 in the Discussions tab.
- (Optional) Find a teammate by reaching out to other developers on the Participants tab.
- Get to work on your TensorFlow 2.0 project!
- (Optional) Shoot your demo video, and take screenshots of your functioning application. Make sure to point out design choices, friction points (if any!), and value of the product.
- Upload your code to Github, and ensure that it is in a public repo.
Developer Advocate (TensorFlow)
TensorFlow 2.0 Owner
Developer Advocate (TensorFlow)
TensorFlow Docs Lead
How original and innovative is the product? Is there already an existing model or software application with similar functionality? Does this project integrate deep learning in an innovative, unexpected way?
Does the product integrate a large number of components? Is it widely useful, and scalable? Is the software application or model usable in a production setting?
What kind of social or business value could this product deliver? How does this software application use deep learning to make the world a little bit better, and to positively impact people’s lives?