https://github.com/allegroai/trains/blob/master/trains/backend_config/config.py

a machine that had previous installation, but I deleted the /opt/trains directory beforehand

I'll check my phone

I'm trying it now

Gotcha, didn't think of an external server as Service Containers are part of Github's offering, I'll consider that

So how can I replace it with the MINIO?

my bad, I didn't look at the upgrade section

I might just do that, it will be my second contribution to ClearML 🙂

SuccessfulKoala55 :shrug:

I followed the upgrading still nothing

no this is from the task execution that failed

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

Oh I get it, I thought it is only a UI issue... but it actually doesn't send it O_O

AgitatedDove14 just a reminder if you missed this question 😄

👍 thanks (again)

Okay, so if my python script imports some other scripts I've written - I must use git?

logger.report_table(title="Inference Data", series="Inference Values", iteration=0, table_plot=inference_table)

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...

lets try

now I'm curious what is the effect of _allow_extra_fields_

Still happens

AgitatedDove14 UnevenDolphin73

👍

moreover I think I found a bug

is it possible to access the children tasks of the pipeline from the pipeline object?

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?

I have a single IAM, my question is what kind of permissions I should associate with the IAM so that the autoscaler task will work

the output above is what the agent has as it seems... obviously on my machine I have it installed

Maybe something similar to dockers, that I could name each one of my trains agents and then refer to them by name something like

trains-agent daemon --name agent_1 ...

Then
trains-agent stop/start

I've dealt with this earlier today because I set up 2 agents, one for each GPU on a machine, and after editing configurations I wanted to restart only one of them (because the other was working) and then I noticed I don't know which one to kill

this is the df -h output