[exxo_]

We (NVIDIA) recently moved away from Quay/Github/Jenkins to Gitlab for our deep learning automation and the experience so far has been truly amazing. We were able to automate our most complex DL container pipeline in a matter of days. We still have to workaround some Gitlab limitations (e.g. issues [CE]17069, [CE]18994, [CE]18106, [EE]224) but overall it's great to see everything working in harmony (i.e. Docker registry, CI pipelines, Git repositories, Runners on-premises). On a personal note, I would like to see more storage on Githost.io instances considering the fact that you can't easily delete pipeline traces and that Docker images can quickly add up.

[sytse]

Thank you so much for commenting. It is great to hear that the deep learning automation department of Nvidia is using GitLab and is happy with everything working in harmony.

Regarding your suggestions:

I asked to prioritize https://gitlab.com/gitlab-org/gitlab-ce/issues/17069

We're already actively discussing https://gitlab.com/gitlab-org/gitlab-ce/issues/18994

Not sure about https://gitlab.com/gitlab-org/gitlab-ce/issues/18106

https://gitlab.com/gitlab-org/gitlab-ee/issues/224 looks interesting

Please comment in the issues if you have additional details about the use case or questions.

The costs of GitHost.io correlate with the storage since they are Digital Ocean instances. Not sure how to solve. Maybe by allowing to use their networked storage, but this seems complex. Consider emailing [e-mail] if you have any questions or suggestions.

[Snappy]

You can also switch your container registry to use S3, which might be more cost-effective. I'm not positive if GitHost.io supports that, but it likely does.