You also have onboarding videos there, I suggest you review all videos.

I'm afraid it's not supported right now.

The daemon agent on the machine will have to be started differently and monitored differently - you're welcome to add a PR though 🙂

Why not give an option to provide their user name and then convert it in the code?

Hi @<1753589101044436992:profile|ThankfulSeaturtle1> , not sure I understand what you mean. Can you please elaborate?

UnevenDolphin73 , sorry for the delay 🙂
Please go to the profile page, hit F12 and do CTRL+F5

In 'Network' there should be a call to ' http://server.info '. Can you please copy paste the response here?

I would look for an AMI that has already cuda and all drivers installed. Same goes for the docker image

Hi @<1739455989154844672:profile|SmarmyHamster62> , Are you sure about the version of ClearML? Can you share the entire log of the triton container?

This is inside the container on the machine running the agent. It is taking the configurations as a basis from the local clearml.conf

  --status              Print the worker's schedule (uptime properties, server's runtime properties and listening queues)

You'll need ES/Mongo to run the ClearML server

What version of clearml are you using? Are you using the community server?

You mean you run several tasks from a single file?

Is it a self hosted server?

VexedCat68 , It appears to be a bug of sorts, we'll sort it out 🙂

How are you running the task? Can you add logs please?

Hi @<1729309137944186880:profile|GrittyBee73> , It has a python API used under the hood indeed:
None
examples on how to use it: None
[None](https://github.com/allegroai/clearml-serving/blob/724c99c605540cdae25e4ef504c09f705cd53503/clearml_serving/serving/model_request_proces...

You can also just delete the installed packages section from the webUI and it will force it to use the requirements.txt

@<1691620877822595072:profile|FlutteringMouse14> , can you try upgrading to the latest available? 1.8.1rc2

I think the set_default_upload_uri is for all output models, while set_upload_destination is for a specific model/file

However, now when I go in the Results -> Debug Samples tab, the s3 credential window pops up. Every time that I refresh the page

RattyLouse61 , What version of ClearML are you running, I think this issue was solved in 1.3.0 release

No, resource configuration allows you higher level of control over different resources - None

I suggest you review the agent setup & deployment docs - None

Then you can view all the connected nodes through the orchestration tab - None

I think in that case you should be using environment variables that are set on the machine and then access them via the task

Hi @<1545216070686609408:profile|EnthusiasticCow4> , go into settings -> configuration, there is an option to show hidden projects. This way you should be able to view pipelines in the experiments view

@<1533619716533260288:profile|SmallPigeon24> , did you try debugging your pipeline locally(steps included)?

Hi @<1702492411105644544:profile|YummyGrasshopper29> , it looks like the controller is running, but is there any agent listening to where the tasks are being pushed?

I think it should work fine, please update 🙂

I see, thanks for reporting this! I guess it should be an easy fix. Can you please open a Github issue to follow up on this?

Hi AbruptWorm50 ,

You can check in the UI, does the model miss any data there? Can you download it properly?

I meant writing a new pipeline controller that will incorporate the previous pipelines as steps. What is the error that you're getting? Can you provide a snippet?

I think you can do this only through the API, if at all possible since it's a system tag. Which project do you want to edit?