is it normal that it's slower than my device even though the agent is much more powerful than my device? or because it is just a simple code

I am running the agent by (clearml-agent daemon) so I thing it is running using virtual environment mode not docker

Is it related to the python versions? When I run the same code on my first device through clearML to the agent it works fine (the version of the first device is 3.10.6) but I think it is not related to the version

yes Just i want to make sure it will work as intended so just queue 3 will take any idle GPU when there is a task to pull if there is no idle GPU will be 2 GPUs only

3.10.7

I tried to run this command but still the same

do I need to change USER (with my device name) or it is a keyword

ok the problem solved by "pip install virtualenv" on the agent 😁

I am trying to access the dataset by the below code

I am storing it here by the below code

I don't understand what you mean by that

is it this? I can copy a URL from here

where to find it?

so how to get the path for the file itself not all the files? 😓

ok it works thank you

I am running this part of code

No thing in the uncommitted changes

clearml==1.7.0

I run it in my local device on vs code

is there a resources like youtube videos or tuotorials about using clearML? I watched and learned from ClearML chaneel in youtube but I think I need more to see maybe I have done something wrong?

i run the code again then enqueue the task and for now it takes more than 5 min and still pending in the queue, I will see when it will run 😓

I will check with another model to see

thanks alot it run successfully

yes thank you

so for example if there was an idle GPU and Q3 take it and then there is a task comes to Q2 which we specified 3GPU but now the Q3 is taken some of these GPU what will happen

how to check it?

after installing nbconvert all the code is visible on the uncommitted changed

I posted another question if anyone is free to help and thanks a lot for you all for your kind help

Yes I mean the python packages, I would prefer to have all the installation once and if a new package is used just to install the new package not every time the same packages get installed many times.

how to put or handle this configuration and where?