not sure how for debug sample and scalars ....
But theorically, with the above, one should be able to fully reproduce a run

very hard to diagnose with this tiny bit of log ...

what about the log aroundwhen it try to actually clone your repo ?

one the same or different machine !

because when I was running both agents on my local machine everything was working perfectly fine

This is probably you (or someone) had set up ssh public key with your git repo sometime in the past

even it's just a local image ? You need a docker repository even if it will only be local PC ?

normally, you should have a agent running behind a "services" queue, as part of your docker-compose. You just need to make sure that you populate the appropriate configuration on the Server (aka set the right environment variable for the docker services)
That agent will run as long as your self-hosted server is running

you can upload the df as artifact.
Or the statistics as a DataFrame and upload as artifact ?

if you want plot, you can simply generate plot with matplotlib and clearml can upload them in the Plot or Debug Sample section

I really like how you make all this decoupled !! 🎉

is this mongodb type of filtering?

Nice ! That is handy !!
thanks !

but afaik this only works locally and not if you run your task on a clearml-agent!

Isn;t the agent using the same clearml.conf ?
We have our agent running task and uploading everything to Cloud. As I said, we don;t even have file server running

what does your clearml.conf look liks ?

so in your case, in the clearml-agent conf, it contains multiple credential, each for different cloud storage that you potential use ?

Try to set CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=true in the terminal start clearml-agent
See None

I also use this: None
Which can give more control

and just came across this: None
That sounds like what you may be looking for

never tried 😛

so i guess it need to be set inside the container

I use ssh public key to access to our repo ... Never tried to provide credential to clearml itself (via clearml.conf ) so I cannot help much here ...

the config that I mention above are the clearml.conf for each agent

how did you deploy your clearml server ?

like for dataset_dir I would expect a single path, not an array of 2 paths duplicated

Without clearml-session, how one could set this up ?? I cannot find any documentation/guide on how to do this ... The official doc seems to say: you start a code server that then connect to vscode.dev Then from your laptop, you go to vscode.dev in order to access to your code server. Is there anyway you do this but without going to vscode.dev ???

You can either set your user permission to allow group write by default ?
Or maybe create a dedicated user with group write permission and run the agent with that user ?

I use CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=/path/to/my/vemv/bin/python3.12 and it work for me

there is a tricky thing: clearml-agent should not be running from a venv itself ... don't remember where I read that doc

I think he means disk space usage ...

So I now just need to find a way to not populate Installed Package in the first place