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533 × Eureka!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
Continuing on this line of thought... Is it possible to call task.execute_remotely
on a CPU only machine (data scientists' laptop for example) and make the agent that fetches this task to run it using GPU? I'm asking that because it is mentioned that it replicates the running environment on the task creator... which is exactly what I'm not trying to do 😄
The scenario I'm going for is never to run on the dev machine, so all I'll need to do once the server + agents are up is to add task.execute_remotely...
after the Task.init
line and after the execution of the script is called on the dev machine, it won't actually run but rather enqueue itself for the agent to run it?
doesn't contain the number 4
this is the selection from the column setting menu
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?
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
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
we are running the agent on the same machine AgitatedDove14 , it worked before upgrading the clearml... we never set these credentials
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
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?
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