this is the selection from the column setting menu

the explicity of the variable queue makes it feels like it runs somewhere else

Does it mean that if it is set to False I need an agent but if I set it to True I don't need one?

Yep, the warnings no longer appear

only string

so putting the docs aside, what permissions should I give to the IAM associated with trains' autoscale ?

okay lets go

awesome this will help a lot with debugging

we did execute locally

and when looking at the running task, I still see the credentials

Okay Jake, so that basically means I don't have to touch any server configuration regarding the file-server on the trains server. It will simply get ignored and all I/O initiated by clients with the right configuration will cover for that?

So how can I replace it with the MINIO?

can you tell me which API call exactly are you using for spinning up? I would like to debug and try to use boto3 myself in order to spin up an instance, so I can understand where the problem is coming from

Anyway I checked the base task, and this is what it has in installed packages (seems like it doesn't list all the real packages in the environment)

I assume trains passes it as is, so I think the quoting I mentioned might work

:face_palm: 🤔 :man-tipping-hand:

Version 1.1.1

Snippet of which part exactly?

Yeah, logs saying "file not found", here is an example

This is a part of a bigger process which times quite some time and resources, I hope I can try this soon if this will help get to the bottom of this

I'm asking that because the DSes we have are working on multiple projects, and they have only one trains.conf file, I wouldn't want them to edit it each time they switch project

so basically - if she has new commits locally that werent pushed it won't work

But if she did not commit her latest changes, and now she enqueues - it will work?

Thanks

Saving part from task A:

pipeline = trials.trials[index]['result']['pipeline'] output_prefix = 'best_iter_' if i == 0 else 'iter_' task.upload_artifact(name=output_prefix + str(index), artifact_object=pipeline)

Manual model registration?

👍

actually i was thinking about model that werent trained uaing clearml, like pretrained models etc

I assume that at some points in the execution, the client (where the task is running) is sending JSONs to the mongo service, and that is what we see in the web UI.
Since we are talking about a case where there is no internet available, maybe these could be dumped into files/stdout and let the user manually insert them.
The manual insertion UX could be something like a CLI copy-paste or and endpoint for files - but since your UX is so good ( 🙂 ) I'm sure you'll figure this part out better

I just tried setting the conf in the section Martin said, it works perfectly

I don't fully get it - it says it has to be enqueued

later today or tomorrow, I'll update

I have them only in one place