So basically export a webapp view as csv?

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

Hi TenseOstrich47 sorry for the long wait, here is a video + code of how to put any sort of metadata inside your clearml model artifact 😉 We will also be improving this, so if you have feature requests we would love to hear about them
https://www.youtube.com/watch?v=WIZ88SmT58M&list=PLMdIlCuMqSTkXBApOMqg2S5IeVfnkq2Mj&index=12

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 😉

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

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

HighOtter69 so if you manually change the color of one of them, the others change for you as well?!

WackyRabbit7 It is conceptually different than actually training, etc.

The service agent is mostly one without a gpu, runs several tasks each on their own container, for example: autoscaler, the orchestrators for our hyperparameter opt and/or pipelines. I think it even uses the same hardware (by default?) of the trains-server.

Also, if I'm not mistaken some people are using it (planning to?) to push models to production.

I wonder if anyone else can share their view since this is a relati...

I would say that this is opposite of the ClearML vision... Repos are for codes, ClearML server is for logs, stats and metadata. It can also be used for artifacts if you dont have dedicated artifact storage (depending on deployment etc)

Do you mind explaining your viewpoint?

BattyLion34 we're here if you have more questions. Have you seen my recent webinar? https://youtu.be/Y5tPfUm9Ghg

Hi, this really depends on what your organisation agrees is within MLOps control and what isn't. I think this blogpost is a must read:
https://laszlo.substack.com/p/mlops-vs-devops
and here is a list of infinite amount of MLOps content:
https://github.com/visenger/awesome-mlops
Also, are you familiar with the wonderful MLOPS.community? The meetup and podcasts are magnificent (also look for me in their slack)
https://mlops.community/

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

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

I'm sorry, then what?

Thanks for your interest in the enterprise offering 🕶️ I would much rather if we kept this slack workspace for the open-source solution we all know and love. You can email me at mailto:ariel@clear.ml for more info. For a short answer: the Data lineage is about an order of magnitude cooler, and hyperdatasets can be thought of "beyond feature stores for unstructured data". Does this help?

Hi Torben, thats great to hear! Which of the new features seems the most helpful to your own use case? Btw to your question the answer is yes. But I'm not exactly sure whats the pythonic way of doing that. AgitatedDove14 ?

I'm not sure I can help with the technicality, but here is a basic question you'll be aksed - are you able to download anything from your minio using ClearML?

This looks like a genuine git fetch issue. Trains would have problems figuring the diff if git cannot find the base commit...
Do you have submodules on the repo? did the DS push his/her commits?

SubstantialBaldeagle49 not at the moment, but it is just a matter of implementing an apiclient call. you can open a feauture request for a demo on github, it will help make it sooner than later

Difficult without a reproducer, but I'll try: How did you get the logger? Maybe you forgot parentheses at task.get_logger() ?

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

The colors are controlled by the front-end so not programmatically, but it is possible and (in my opinion) convenient to do so manually via clicking the appropriate position on the legend. Does this meet your expectations?

Hi HugePelican43 , glad that you are interested in our Feature Store. The screenshots in that portion of site (we should specify that in a clearer way, probably) reflect our enterprise offering.

If you are looking for open-source solutions to incorporate online feature stores in your deployment - I'm happy to discuss how to do it in a way that works best with the clearml-ecosystem.

If, however, you are looking for "offline" feature management capabilities, this is 100% doable with your ow...

Did you try archiving all the experiments and then deleting the project?

Also, while we are at it, EnviousStarfish54 ,can I just make sure - you meant this page, right?
https://allegro.ai/enterprise/

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

There are an astounding number of such channels actually. It probably depends on your style. Would you like me to recommend some?

Of course we can always create a channel here as well... One more can't hurt 😉

Aha so my flower detector example is not the best one to start with... My suggested route would be framework then experiment tracki g and then ochestration. If you wish to "cut corners" you could try our hyperparam blogpost https://link.medium.com/uGA6DePqmeb

Not so much relevant, since it can be seen from your task 😄 but it would be interesting to find out if trains made something be much slower, and if so - how

I'm specifically interested in the model-first queries you would like to do (as experiment-first queries are fully featured, we want to understand whats the best way to take that into models)