I just used this to create the dual_gpu queue:
clearml-agent daemon --queue dual_gpu --create-queue --gpus 0,1 --detached

Nope, no other config files

seems OK

From the log you shared, the task is picked up by the 

worker_d1bd92a3b039400cbafc60a7a5b1e52b_4e831c4cbaf64e02925b918e9a3a1cf6_<hostname>:gpu0,1

 worker

I can try and target the default one if it helps..?

Not likely

Stop and re-run the agent

Don't do it in detached mode - do it in another console window

Is there a preferred way to stop the agent?

It should dump a log to stdout

Is there a preferred way to stop the agent?

Same agent command + --stop

Okay trying again without detached

Hah. Now it worked.

It failed on some missing files in my remote_execution, but otherwise seems fine now

Strange...

I'll kill the agent and try again but with the detached mode 🤔

A follow up question (instead of opening a new thread), is there a way I could signal some files/directories to be copied to the execute_remotely task?

Seemed to work fine again in detached mode, what went wrong there :shocked_face_with_exploding_head:

A follow up question (instead of opening a new thread), is there a way I could signal some files/directories to be copied to the 

execute_remotely

 task?

For that you'll need to use a Git repository - the repository will be automatically cloned when running the task remotely.

Hm, that seems less than ideal. I was hoping I could pass some CSV locations. I'll try and find a workaround for that. Thanks!

You can always upload using the StorageManager and download if the file is not there

Or store as a configuration item (if it's not a lots of data)

The idea is that the features would be copied/accessed by the server, so we can transition slowly and not use the available storage manager for data monitoring

That will come at a later stage

I guess following the example https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py , it's not clear to me how the server has access to the data loaders location when it hits execute_remotely

This follows the standard ClearML remote execution practice - an agent runs the task, and either uses the actual python code file (stored entirely in the server under the uncommitted changes section), or clones a git repository

So the ..data referenced in the example above are part of the git repository?
What about setting the working_directory to the user working directory using Task.init or Task.create ?

Thanks for your help SuccessfulKoala55 ! Appreciate the patience 🙏

So the 

..data

 referenced in the example above are part of the git repository?

Yup 🙂

What about setting the 

working_directory

 to the user working directory using 

Task.init

 or 

Task.create

?

The working_directory is simply one of the parameters used when cloning a git repository, so it won't work...
You can rely on a fixed mount point, for example, but that requires more setup

I was thinking of using the --volume settings in clearml.conf to mount the relevant directories for each user (so it's somewhat customizable). Would that work?

It would be amazing if one can specify specific local dependencies for remote execution, and those would be uploaded to the file server and downloaded before the code starts executing