this is the selection from the column setting menu

Nope

In my use case I'm using an agent on the same mahcine I'm developing, so pointing this env var to the same venv I'm using for development, will skip the venv creation process from teh task requirements?

I'm using iteration = 0 at the moment, and I "choose" the max and it shows as a column... But the column is not the scalar name (because it cuts it and puts the > sign to signal max).

For the sake of comparing and sorting, it makes sense to log a scalar with a given name without the iteration dimension

what is the difference betwee nfalse and true?

But what does it do?

and we want to continue xecuting locally

awesome this will help a lot with debugging

we are running the agent on the same machine AgitatedDove14 , it worked before upgrading the clearml... we never set these credentials

Sorry but its not clear

Not sure I understand, if i run pipe.start_locally(run_pipeline_steps_locally=True|False) what is the difference betwee ntrue and false? assuming I want to execute locally

awesome martin, thanks

AgitatedDove14

we did execute locally

ShinyLobster84

We try to break up every thing into independent tasks and group them using a pipeline. The dependency on an agnet caused an unnecessary overhead since we just want to execute locally. It became a burden once new data scientists join the project and instead of just telling them "yeah, just execute this script" you have to now teach them about clearml, the role of agents, how to launch them, how they behave, how to remove them and stuff like that... things you want to avoid with data scientists

Does it mean that if it is set to False I need an agent but if I set it to True I don't need one?

Yes very much

the ability to exexute without an agent i was just talking about thia functionality the other day in the community channel

Worth mentioning, nothing has changed before we executed this, it worked before and now after the update it breaks

if I called .start_locally

I've read that

Okay, so let me get this straight

The autoscaling is basically an ever-running task (lets say on the services queue). Now, the actual auto scaling and which queues exist have nothign to do with that, and are configured in the auto scale task?

I mean if I continue and build on the example in the docs, what will happen if the training task is completed, and then I get it and log to it? Will it be defined as running again?

DangerousDragonfly8 but would this work if they are not concurrent but sequential?

I was sure you are on Israel times as well, sorry for the night time thing 😄

and I'll open an issue

When I ran the clearml-task --name ... -project ... -script .... it failed saying not requiremetns was found

Wait but I don't want to execute it