Code submission are mandatory for all submitted reports. We recommend that you:

• Publish your code in a repository (e.g. on GitHub, GitLab, BitBucket) and anonymize it according to our double blind guidelines.
• Document your code appropriately
• Have a README.md file which describes the exact steps to run your code. You can refer to the ML Code Completeness Checklist to write the README file and make sure your code submission is complete.
• See this blog post on best practices for reproducibility.

• Google Colaboratory provides free GPU backed Jupyter Notebooks
• Code Ocean provides 10hrs/month of GPU accelerated platform free to academics. Code Ocean is a cloud-based research collaboration platform.
• Instructors can apply for Google Cloud credits for their students. Each student will be given a fixed amount of starter credits to use Google Cloud Compute (GCP).
• Students can also request credits from Google Cloud Compute.
• If you are a company that can offer cloud computing credits, please contact reproducibility.challenge@gmail.com

• Past accepted papers of MLRC at the ReScience Journal (Volume 8 Issue 2, Volume 7 Issue 2, Volume 6 Issue 2, Volume 5 Issue 2)
• [For course instructors] ACL 2022 Tutorial on “Towards Reproducible Machine Learning Research in Natural Language Processing”
• NAACL 2022 Reproducibility Track
• ML Reproducibility Checklist
• ML Code Completeness Checklist
• ML reproducibility tools and best practices
• Joelle Pineau’s Keynote talk on Reproducibility at NeurIPS 2018