This page lists some useful resources which you can use for the challenge.

Reproducible Code

  • If you are working in PyTorch, we strongly recommend using Pytorch Lightning, a framework which takes care of the boilerplate and provides highly reproducible standards of ML research pipeline. Check the seed project as a good starting point.
  • Document your code appropriately
  • Have a file which describes the exact steps to run your code

Compute Resources

  • Code Ocean provides 10hrs/month of GPU accelerated platform free to academics. Code Ocean is a cloud-based research collaboration platform. Code Ocean also is our Cloud Platform Sponsor, so they will be providing limited teams more compute resources than the free tier. Code Ocean compute resources will be allocated once we complete our review of applications, and your team members will be notified if selected. Check Registration for more details. Also check Code Ocean Forum for any support related to the platform.
  • Instructors can apply for Google Cloud credits for their students. Each student will be given a small number of credits to start (approx. $50).
  • By default, Google Cloud accounts don’t come with a GPU quota, but you can find instructions on how to request GPUs, including links on how to check and increase quotas, at this link.
  • If necessary, instructors can ask for much more computing credits (up to $1000 per student) by contacting:
  • Students can also request a $300 credit from Google Compute Cloud.
  • Anyone can use Google Colaboratory which provides free GPU backed Jupyter Notebooks
  • If you are another company that can offer cloud computing credits, please contact or

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