Semantic versioning in GitLab's model registry clashes with mlflow's api (which uses plain integers). Hence, when loading a model with mlflow.pytorch.load_model() an exception is raised. This limits too much the usability of the model registry for deep learning tasks where full retraining on the CI pipeline is not feasible. Would it be too much asking to implement the mlflow client's integration to be compatible with this? I have read somewhere that there is interest in implementing semantic versioning in mlflow too, but for the moment it's only handled with aliases. Besides that, great work!
I was surprised to find out how overcomplicated and poorly designed Gitlab pipelines are, I just need to create a simple pipeline that is being triggered manually and user should be able to provide input parameters. In Jenkins it is straight forward, in github actions too, but not in Gitlab!! I will avoid Gitlab by any means in my future projects. The documentation is poor as well.
Heyy! What an amazing work! :o <3 just requested access, thank you in advance! :) also, is it possible to track two tasks at the same time? i assume it is, since its tracks it on the row... and is there a way to pause the task? thank youuuu
The Gitaway cluster is the key to this whole thing. Oh, and the excitement level is outstanding. Except for those with a low excitement. Wait, I forgot the UI thingy. And the milestone burn-up charts. That's hot! The VS code thingy is also incredible. Oh, and the EPIC boards are EPIC. And somebody's 3 may be more important than somebody's 1. And the one guy can give you 10 instead of 3. That sounds like a bargain. There is a reason to be excited about all of this.
Recommend watching this video. Love the idea of writing down the pop ups as brings my focus back to the chosen 3 for the current day. Great system. Thankyou.👍
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