davidortega

You can also deploy runners with GPU on premise using CML docker image with GPU already supported having to install only the ndivia drivers and nvidia-docker in your machine.

docker run --name myrunner -d -e RUNNER_IDLE_TIMEOUT=1800 -e RUNNER_LABELS=cml -e RUNNER_REPO=$my_repo_url -e repo_token=$my_repo_token dvcorg/cml-gpu-py3-cloud-runner

It works for Gitlab and Github. Just only point your url and repo token

Hi doppenhe, we have that part already implemented using cml-send-github-check and dvc metrics diff. You can compare the metric that you prefer with dvc and then just set the status of the github check uploading your full report. Of course, you can also fail the workflow as your Github action does, but I think is more useful to see it as a report in the check.

disclaimer: I'm work with CML