cml_cloud_case

You should check out this [0] GitHub action workflow. It uses docker machine to create a high resourced runner on demand in the cloud and then runs the subsequent job on it. The workflow is intended for machine learning but I'm sure it could be adapted. I must disclose that I haven't actually used it myself yet (because I haven't felt the need) but it does look very promising. 0: https://github.com/iterative/cml_cloud_case/blob/master/.git...

Thanks, and good questions! 1. Yes! Let me link some reports and example repos: - A basic classification problem with scikit learn: https://github.com/iterative/cml_base_case/pull/2 - CML with DVC & Vega-Lite graphs: https://github.com/iterative/cml_dvc_case/pull/4 - Neural style transfer with EC2 GPU: https://github.com/iterative/cml_cloud_case/pull/2 2. If training fails, you'll be notified that your run failed in the GitHub Action dashboard (or GitLab CI/CD dashboard). See here for some real life examples of failure ;) : https://github.com/iterative/cml_cloud_case/actions 3. CML reports are markdown documents, so you can write any kind of text to them. If your metrics are output in a file `metrics.txt`, you can have your runner execute `cat metrics.txt >> report.md` and then have CML pass on the report to GitHub/GitLab. Likewise, any