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Hi Everyone! I Discovered That Uploading Model Artifacts At Each Checkpoint To The Clearml Server Significantly Slows Down Training. So I Set

Hi everyone!
I discovered that uploading model artifacts at each checkpoint to the clearml server significantly slows down training. So I set output_uri to save everything on my local disk. However, I still want to upload the final model to clearml. Is there a way to do that after training? I found instructions for Pandas DataFrames, Local files, dictionaries, Folders, Numpy objects, and Image files, but not for models.

Posted 3 months ago
Votes Newest

Answers 6

you should know where your latest model is located then just call task.upload_artifact on that file ?

Posted 3 months ago

Will it be recognized as a model?
Though I will try it anyway, thank you!

Posted 3 months ago

Thank you for your help, @<1576381444509405184:profile|ManiacalLizard2> ! I tried it and got an error "Invalid task status: expected=created, status=completed ...".
I'm a bit confused about this... How to add an artifact to this task? Is it safe to call task.init() again?

Posted 3 months ago

@<1580005325879119872:profile|SweetCat82> , once an experiment finished running you can't change it's status unless you reset it. I think task.upload_artifact needs to come before your task finishes.

How did you try calling it? Fetching the task via SDK and then trying to upload the artifact?

Posted 3 months ago

you should be able to explicitly upload a file of your choice as artefact using something like this: None

Posted 3 months ago

@<1523701070390366208:profile|CostlyOstrich36> That's literally one of my first experiments ☺ . I trained a model in Jupyter and after the training stopped, ran 'task.upload_artifact' in the next cell.

Posted 3 months ago
6 Answers
3 months ago
3 months ago