Clicking on that link exaclty 🙂

Yeah I got what I needed

Yep what 😄

In my use case I'm using an agent on the same mahcine I'm developing, so pointing this env var to the same venv I'm using for development, will skip the venv creation process from teh task requirements?

FriendlySquid61

Just updating, I still haven't touched this.... I did not consider the time it would take me to set up the auto scaling, so I must attend other issues now, I hope to get back to this soon and make it work

👍

but nowhere in the docs does it say anything about the permissions for the IAM

SuccessfulKoala55

For me a simple restart worked

ShinyLobster84

I couldn't do it with clearml task as it was looking for a requirements file and I'm workgin with poetry

checking and will let you know

I'm using pipe.start_locally so I imagine I don't have to .wait() right?

I can try do an ls to see what it will print out

Why is that an issue?

👍

What do you mean by submodules?

She did not push, I told her she does not have to push before executing as trains figures out the diffs.
When she pushes - it works

Increased to 20, lets see how long will it last 🙂

I don't know, I'm the one asking the question 😄

I ran the wizard liek a milion times

👍

Searched for "custom plotly" and "log plotly" in search, didn't thinkg about "report plotly"

The weirdest thing, is that the execution is "completed" but it actually failed

TimelyPenguin76 if I build a custom image, do I have to host it on dockerhub for it to run on the agent? If not how do I make the agent aware of my custom image?

SuccessfulKoala55 The simplest thing i can think of is on Task.execute_remotely to be able to append ot the docker_init_bash_script

is this already available or only on github?

Okay so that is a bit complicated

In our setup, the DSes don't really care about agents, the agents are being managed by our MLops team.
So essentially if you imagine it the use case looks like that:
A data scientists wants to execute some CPU heavy task. The MLops team supplied him with a queue name, and the data scientist knows that when he needs something heavy he pushes it there - the DS doesn't know nothing about where it is executed, the execution environment is fully managed by the ML...

So what's our next move FriendlySquid61 ? O_O

Mmm maybe, lets see if I get this straight

A static artifact is a one-upload object, a dynamic artifact is an object I can change during the experiment -> this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?

AgitatedDove14