👍

Cool, now I understand the auto detection better

Exactly

Yes, I'll prepare something and send

Committing that notebook with changes solved it, but I wonder why it failed

I get this
` [ec2-user@ip-10-0-0-95 ~]$ docker-compose down
WARNING: The TRAINS_HOST_IP variable is not set. Defaulting to a blank string.
WARNING: The TRAINS_AGENT_GIT_USER variable is not set. Defaulting to a blank string.
WARNING: The TRAINS_AGENT_GIT_PASS variable is not set. Defaulting to a blank string.
ERROR: Couldn't connect to Docker daemon at http+docker://localhost - is it running?

If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable. `

From the UI

checking and will let you know

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

AgitatedDove14 ⬆ please help 🙏

Hahahah thanks for the help SuccessfulKoala55 & CostlyOstrich36

I really do feel it would be a nice to have the ability to easily configure the Cleanup Service to cleanup only specific projects / tasks as its a common use case to have a project dedicated for debugging and alike

-_- why there isn't a link to source on the docs?

First of all I wasn't aware that was an option - but I think it's preferable to be able to do it through the command line. Because I'm developing the pipeline to be executed remotely, but for debugging I run it locally.

Using what you showed I can obviously write it, and delete it once it is ready, and rewrite it when I'm debugging or adding features - but I think DX-wise it would be nicer to be able to trigger this functionality through the command line

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

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

I see it's a plotly plot, even though I report a matplotlib one

CostlyOstrich36 so why 1000:1000? My user and group are not that and so do all the otehr files I have under /opt/clearml

very cool

SuccessfulKoala55 :shrug:

When I said not the expected behavior, I meant that following the instructions on the docs, should lead to downloading the latest version

the curl refers to the master

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

Wait, suddenly the UI changed to 0.16.1, seems like I was shown a cached page

I followed the upgrading still nothing

TimelyPenguin76 I think our problem is that the agent is not using this environment, I'm not sure which one he does... Is there a way to hard-code the agent environment?

Also being able to separate their configurations files would be good (maybe there is and I don't know?)

AgitatedDove14 worked like a charm, thanks a lot!

Still happens

sudo curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o /opt/trains/docker-compose.yml

not manually I assume that if I deleted the image, and then docker-composed up, and I can see the pull working it should pull the correct one