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

So basically export a webapp view as csv?

Welcome, may your trains journey be a productive one

These are excellent questions. While we are working towards including more of our users stack within the ClearML solution, there is still time until we unveil "the clearml approach" to these. From what I've seen within our community, deployment can anything from a simple launch of a docker built with 'clearml-agent build' to auto training pipelines.

Re triggering - this is why we have clearml-task 😉

Hi TrickySheep9 , ClearML Evangelist here, this question is the one I live for 😉 are you specifically asking "how do people usually so it with ClearML" or really the "general" answer?

EnviousStarfish54 I recognize this table 😉 i'm glad you are already talking with the right person. I hope you will get all your questions answered.

I'm going to answer soon 🙏

Nbdev ia "neat" but it's ultimately another framework that you have to enforce.

Re: maturity models - you will find no love for then here 😉 mainly because they don't drive research to production

Your described setup can easily be outshined by a ClearML deployment, but sagemaker instances are cheaper. If you have a limited number of model architectures you can get tge added benefit of tracking your s3 models with ClearML with very little code changes. As for deployment - that's anoth...

We need SEO for our docs 😉

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

Should work on new tasks of you use this command in the script. If you'd rather to keep the scripts as clean as possible, you can also configure it globally for all new tasks in trains.conf

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

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

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 😉

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)

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

Hi, I think this came up when we discussed the joblib integration right? We have a model registry, ranging from auto spec to manual reporting. E.g. https://allegro.ai/clearml/docs/docs/examples/frameworks/pytorch/manual_model_upload.html

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

SubstantialElk6 if you sign up for free on http://clear.ml you'll get a private workspace. some teams used in hackday-jp recently, it was a great success

CloudyHamster42 it will only affect news tasks created with the config file...sorry

wait, I thought this is without upload

Hi SubstantialBaldeagle49 ,

certainly if you upload all the training images or even all the test images it will have a huge bandwidth/storage cost (I believe bandwidth does not matter e.g. if you are using s3 from ec2) If you need to store all the detection results (for example, QA, regression testing), you can always save the detections json as artifact and view them later in your dev environment when you need. The best option would be to only upload "control" images and "interesting" im...

ClearML thanks

Sorry for being late to the party WearyLeopard29 , if you want to see get_mutable_copy() in the wild you can check the last cell of this notebook:

https://github.com/abiller/events/blob/webinars/videos/the_clear_show/S02/E05/dataset_edit_00.ipynb

Or skip to 3:30 in this video:

https://youtube.com/watch?v=NhSRW12Bn5w&feature=share

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?

You rang?

Sure we do! Btw MiniatureCrocodile39 iirc I answered one of your threads with a recording to a webinar of mine

submouldes == git submodules

Thanks @<1523701205467926528:profile|AgitatedDove14> , also I think you're missing a few pronouns there 😉

Thank you for helping to build better docs 🙏