task.set_resource_monitor_iteration_timeout(seconds_from_start)

Yes! you can call this

If these indices tend to grow large, I think it would be cool if there was a flag that would periodically remove them. probably a lot of users aren't aware that these take up so much space

Yeah I get what you're saying, but when developing ClearML we did not view it like that. when we run it locally or debug it, we thought of it more "This is running just on my local computer without an agent to make sure everything works before I use an agent to run the parts". Sorry that it confuses you 🙂

Care to share a link? 😄

JitteryCoyote63 Sorry to put the spotlight on you 😄 Have you had a chance to try and implement a dashboard yourself?

Yup indeed! Let us know how it goes!

Hi DepressedFish57 clearml v1.5.0 (pip install clearml==1.5.0) is out with a fix for this issue 🙂
Let us know if it works as expected 😄

Hi DepressedFish57 , as Martin said either the next version or the next-next version will have this feature 😄 We'll update here when it's out 🙂

GiganticTurtle0 That is correct. ATM, you can store some things on the model (I think you can hack it by editing the network configuration and storing whatever you want there.

VivaciousPenguin66 This is very true! We are trying to explain the benefits of this method. Some people like it and some people like the flexibility. We do have our philosophy in mind when we create "best practices" and obviously features to ClearML but ultimately people should do what makes them the most productive!
If we are getting philosophical, I think it's the state of the industry and as it progresses, these standard methods would become more prominent.
also, to add to what you wrote,...

To add onto what Martin wrote, you can see here: https://clear.ml/docs/latest/docs/guides/data%20management/data_man_cifar_classification
How it's interfaced with a torch dataloader. You only replace the path for where the files come from

Don't worry! It's all good 🙂

Hi SpicyCrab51 , Thanks for the warm words 😄 Happy you enjoy our product!
As for your needs, I suggest you explore our https://clear.ml/docs/latest/docs/hyperdatasets/overview , they indeed were made to solve issues similar to what you're facing!
You can see a talk we gave that cover the Hyperdatasets https://www.youtube.com/watch?v=CcL8NNZfHlY !
Note that it is an enterprise feature, and is not part of the open source.
Contact me if you need more info 🙂

Hi JitteryParrot8
Do you mean Task? If you create a dataset with ClearML data, the Task's Icon would indicate it's a dataset task. Same goes for Experiment. You are in luck 🙂 The new SDK (which is about to be released any day now) would log the dataset used, every time you do Dataset.get().
Regardless we are in the final execution phases of a major overhaul to the dataset UI so stay tuned for our next server release that would, hopefully, make your life easier 😄

Hi ScaryBluewhale66 , I believe the new server that's about the be released soon (this \ next week), we'll allow you to report a "single value metric". so if you want to report just a number per experiment you can, then you can also compare between runs.

EnviousStarfish54 Yes, self hosted is still available! We're only adding options, not taking anything away! 😄

Hi Anvar, if you want to report configurations, you can indeed use task.connect(). If you want to report metrics (such as accuracy or loss), you can use the https://clear.ml/docs/latest/docs/fundamentals/logger object to do that. Just parse the file and report the metrics in a way that makes sense to you. You can also add the file itself as an artifact if you want to refer to it later on. Makes sense?

https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py

This is an sklearn example, but AFAIK it should work also with XGB. Makes sense?

If you save it with joblib it should be automatically captured

PompousBaldeagle18 Unfortunately no. We thought this to be a promising avenue but have decided, for various reasons, to move and do other things 😞

Are you using the OSS version or the hosted one (app.clear.ml)? The ClearML enterprise offering has a built-in annotator. Please note that this was meant more for correcting annotations during the development process rather than mass annotating lots of images.

I think you should call dataset.finalize()

instead of system_tags use:

yo yo yo!

Well...I'll make sure we do something about it 🙂

What you can do is run it in offline mode though 😄

Do you mean when calling "PipelineDecorator.debug_pipeline()" ?

You should look at

I actually don't think that it's supported at the moment...I'll talk to the devs and see if that's something we can add to a future release