https://clear.ml/docs/latest/docs/guides/set_offline

Well, we had a nice video from twimlcon but it is not up yet on our site. I recently gave a very long demo on both basic and semi-advanced clearml usage - you can watch it here
https://youtu.be/VJJsVJiWnYY?t=1774
the slides are here:
https://docs.google.com/presentation/d/1PFPTQkHVGxugruTRFDnuVmS85ziSbNOTixCVQwPMFDI/edit?usp=sharing
code is here:
https://github.com/abiller/events/tree/webinars/webinars/flower_detection_rnd

but hey, UnevenDolphin73 nice idea, maybe we should have clearml-around that can report who is using which GPU 😉

TenseOstrich47 as i might have stated earlier, I'm doing a low-key build of something like this. Thanks to your question I know what to focus on when showcasing 😇 ♥

Here's what's already been done https://youtu.be/xliX3IhNdmw

I've been waiting so eagrly for this, I made a playlist! https://open.spotify.com/playlist/4XBqPUgxHD5dbhcYqANzNo?si=G0E_s-OaQzefKIJ0wDkzHA

I'm all for more technical tutorials for doing that... all of this fits the clearml methodology

so what I am describing is exactly this - once you try to create an output model from the same task, if the name already exists - do not create a new model, just update the timestamp on the old one

horray for the new channel ! you are all invited

Which parser are you using? argparse should be logged automatically.

What about cloning and setting "last commit in branch" ?

Hi RobustHippopotamus53 , I think this it just the place to ask this, we are all ClearML users here 😉 Let me ask you this - did you merge and also push? When I forget to push after merging a PR I think this is the same error message I get.

Okay so sounds like two bugs stacked together? I wonder if this is gitlab specific. Could you provide a list of a steps to reproduce? 🙏

Looks like incomplete build of pytorch. What are we looking at. And who's christine?

Its built in 🙂 and Its for... "Services"
https://github.com/allegroai/trains-server#trains-agent-services--

I think the most up-to-date documentation for that is currently on the github repo, right SuccessfulKoala55 ?
https://github.com/allegroai/clearml-server-helm

SubstantialElk6 this is a three parter -
getting workers on your cluster, again because of the rebrand I would go to the repo itself for the dochttps://github.com/allegroai/clearml-agent#kubernetes-integration-optional
2. integrating any code with clearml (2 lines of code)
3. executing that from the web ui
If you need any help with the three, the community is here for you 😉

BTW if anyone from the future is reading this, try the docs again 😉

fine. Can I open a feature request on our github for you, refering this conversation?

OddAlligator72 I think you got sidetracked into the wrong corner here, lets decompose what you are asking for please, tell me if I am getting somewhere near what you mean:
you have an experiment you already ran you want to change the parameters in it and run it again if possible you only want to run a single function in the file attached to that experiment

@<1523714910930866176:profile|MiniatureStarfish88> if you are here make sure to vote for the next presentor of my show ^

You rang?

WickedGoat98 I gave you a slight twitter push 😉 if I were I would make sure that the app credentials you put on your screen shot are revoked 🙏 😇

Hi, which Trains doc version are you looking at? Is it the latest?

Hi, it is under construction, but it is going to be there.

SuccessfulKoala55

Hmm... For quick and dirty integration that would probably do the trick, you could very well issue clearml-task commands on each kubeflow vertex (is that how they are called?)
What do you think AgitatedDove14 ?

I'm a movie star 🤩

Yeah the file system on those VMs is really slow

Sure thing. All you need is the credentials. Did you see my extreme example here? https://youtu.be/qz9x7fTQZZ8

Hi, I was just answering your previous question. can you explain a bit what you mean by "under utilized"? e.g. do you have 2 gpus and are using only one of them for a task?
or are maxing out resources but do not get to 100% utilization (which might be a data pipeline issue)