Algorithm Submission → Automated Testing → Performance Report

Set up a dedicated GitHub repository with submission templates and automated validation. Use GitHub Actions to check code format, required files, and basic syntax validation upon each pull request or submission.

Create workflow files that automatically execute submitted algorithms against standardized transfer learning test cases. Include timeout limits, resource monitoring, and error handling to ensure fair evaluation across all submissions.

Integrate W&B logging into the testing pipeline to automatically capture training curves, transfer learning performance, generalization metrics, and resource usage. Create standardized experiment tags for easy comparison.

Use automated Jupyter notebooks to pull data from W&B and generate comprehensive performance reports with visualizations, statistical comparisons, and transfer learning analysis. Export reports as PDFs for participant feedback.

Send automated Slack notifications to contest organizers when testing completes, including summary statistics, any errors encountered, and links to detailed reports. Include alerts for exceptional performance or issues requiring manual review.