so when I'm on the machine through SSH

and I'm using the same credentials

no this is from the task execution that failed

👍

AgitatedDove14 ⬆ please help 🙏

Config == conf_obj no?

Yes very much

mmmm got it

got it

I think I got it, I'll ping her again if it won't succeed

btw my site packages is false - should it be true? You pasted that but I'm not sure what it should be, in the paste is false but you are asking about true

AgitatedDove14 sorry for the late reply,

It's right after executing all the steps. So we have the following block which determines whether we run locally or remotely

if not arguments.enqueue: pipe.start_locally(run_pipeline_steps_locally=True) else: pipe.start(queue=arguments.enqueue)
And right after we have a method that calls Task.current_task() which returns None

I will

No I don't have trains anywhere in my code

Is there a way to do so without touching the config? directly through the Task object?

Good, so if I'm templating something using clearml-task (without queue, so the task is in draft mode) it will use this task? Even though it never exeucted?

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

There are many ohter packages in my environment which are not listed

I did, but I only find task name, I'm looking to filter by the project

AgitatedDove14

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

Now I see the watermarks are 2gb

Why would I have 0.15.1 if I followed the instructions of the docs?

Or should I change all three of them?

awesome martin, thanks

DangerousDragonfly8 but would this work if they are not concurrent but sequential?

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...