[rhythmvertigo]

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 graphing library is supported because you can add standard image files (.png, .jpg) to the report. So custom metrics and custom graphs. We like DVC for managing and plotting metrics, but we're biased because we also maintain it.

4. Yep, GitHub Actions is pretty powerful and flexible. Works with whatever external services you can connect to your Action!

5. It's not strictly a Docker technology. We use Docker images preinstalled with the CML library in our examples, but you can just install the library with npm in your own image. https://github.com/iterative/cml#using-your-own-docker-image

Let me know if there's anything else I can tell you about