Welcome to the Global PyTorch Summer Hackathon! #PyTorchSummerHack

The PyTorch Summer Hackathon is back this year with all new opportunities for you to connect with the PyTorch community to build innovative, impactful models, applications and other projects that create positive impact for organizations or people.  

Put your machine learning skills to the test in one of the following categories:

See the Category page for more information and request AWS computing credits here.

In addition to cash and promotion, first place winners in each category will get a 30 minute virtual meeting with the PyTorch team to discuss your winning submissions!

View full rules

Prizes

$25,500 in prizes

First Place (3)

• $5,000 USD
• Featured posts on the PyTorch social channels
• 30-minute call with the PyTorch team
• Awarded to the highest scoring submission in each category

Second Place (3)

• $2,500 USD
• Featured posts on the PyTorch social channels
• Awarded to the second highest scoring submission in each category

Third Place (3)

• $1,000 USD
• Featured posts on the PyTorch social channels
• Awarded to the third highest scoring submission in each category

Devpost Achievements

Submitting to this hackathon could earn you:

Eligibility

Participants must read the Global PyTorch Summer Hackathon Official Rules thoroughly to ensure they are eligible to participate in the hackathon before getting started.

Requirements

Main Requirement: Build projects using PyTorch in one of the three categories:

  • PyTorch Developer Tools: Build a creative, useful and well-implemented tool or library for improving productivity and efficiency of PyTorch researchers and developers. The tool or library must be a machine learning algorithm, model, or an application. It should help with tasks such as debugging, training, model understanding, encryption, deployment or furthering researchers. The submission must be built using PyTorch or PyTorch-based libraries like torchvision, torchtext, fast.ai, etc.). See examples. 
  • Web/Mobile Applications powered by PyTorch: Build an application with the web, mobile interface or/and embedded device using PyTorch. The submission must be built using PyTorch or PyTorch-based libraries like torchvision, torchtext, fast.ai, etc.) and have the web, mobile interface or presented as an embedded device so the end users can interact with it. See examples.
  • PyTorch Responsible AI Development Tools: AI developments are improving and integrating with our work and personal lives. It is essential that we, as PyTorch researchers and developers, put our efforts into building tools, libraries, and web/mobile applications that help developing AI models and applications responsibly. These tools, libraries, apps need to support a researcher or developer to factor in fairness, security, and privacy throughout the entire machine learning development process such as data gathering, model training, model validation, inferences, monitoring, and more. See examples.

Important note: If your project existed prior to the start date and time of this hackathon, it must be significantly updated during the submission period and it must NOT have won any previous hackathons to be eligible. Bug fixes are not considered as “significantly updated”.

 

Submit the following assets: 

  1. Demo video. (hosted on YouTube, Facebook Video, Vimeo, or Youku). Your video should be around 3-5 minutes (can be a little longer if needed), include a demo of your working application via a step-by-step visual demo, and be available in English. Be sure to explain how PyTorch was used to build your project.
  2. Images. Please submit at least one image/screenshot of your application.
  3. Access. Provide a link to your publicly accessible code repository on GitHub or your preferred code sharing tool.

Judges

The PyTorch team

The PyTorch team

Judging Criteria

  • Quality of the Idea
    Includes creativity and originality of the idea.
  • Implementation of the Idea
    Includes how well the idea was executed by the developer.
  • Potential Impact
    Includes the extent to which the solution can help the most users.

theme

  • Machine Learning/ AI