it is a model name

Yes, I have a metric I want to monitor so I will be able to sort my experiments by it. It is logged in this manner

logger.report_scalar(title='Mean Top 4 Accuracy', series=ARGS.model, iteration=0, value=results['top_4_acc'].mean())
When looking at my dashboard this is how it looks

I'm using pipe.start_locally so I imagine I don't have to .wait() right?

By the examples I figured out this ould appear as a scatter plot with X and Y axis and one point only.. Does it avoid that?

I guess so

Thanks a lot, that clarifies things

Thia is just keeping getting better and better.... 🤩

Not in docker mode

okay but still I want to take only a row of each artifact

Could be, my message is that in general, the ability to attach a named scalar (without iteration/series dimension) to an experiment is valuable and basic when looking to track a metric over different experiments

I restarted the server and it works now

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

So just to be clear - the file server has nothing to do with the storage?

AgitatedDove14 I still can't get it to work... I couldn't figure out how can I change the clearml version in the runtime of the Cleanup Service as I'm not in control of the agent that executes it

Okay martin, I'll try that

Okay Jake, so that basically means I don't have to touch any server configuration regarding the file-server on the trains server. It will simply get ignored and all I/O initiated by clients with the right configuration will cover for that?

and then how would I register the final artifact to the pipelien? AgitatedDove14 ⬆

Is there a more elegant way to find the process to kill? Right now I'm doing pgrep -af trains but if I'll have multiples agents, I will never be able to tell them apart

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

Thanks 🙂

actually i was thinking about model that werent trained uaing clearml, like pretrained models etc

👍

cluster.routing.allocation.disk.watermark.low:

see gif:

or its the same palce in the config file for configuring the docker mode agent base image?

this?

How do I get all children tasks given a parent?

👍

I executed it now with full path, using $(pwd)