Yes AgitatedDove14 🙂

Interesting - I can reproduce easily

yes

mmmh good point actually, I didn’t think about it

Just caught another star 😄

because at some point it introduces too much overhead I guess

AgitatedDove14 This seems to be consistent even if I specify the absolute path to /home/user/trains.conf

Hi SuccessfulKoala55 , yes indeed

Thanks AgitatedDove14 !
What would be the exact content of NVIDIA_VISIBLE_DEVICES if I run the following command?
trains-agent daemon --gpus 0,1 --queue default &

yes, will do now!

After some investigation, I think it could come from the way you catch error when checking the creds in trains.conf: When I passed the aws creds using env vars, another error poped up: https://github.com/boto/botocore/issues/2187 , linked to boto3

They are, but this doesn’t work - I guess it’s because temp IAM accesses have an extra token, that should be passed as well, but there is no such option on the web UI, right?

from 10 to 11

` resource_configurations {
A100 {
instance_type = "p3.2xlarge"
is_spot = false
availability_zone = "us-east-1b"
ami_id = "ami-04c0416d6bd8e4b1f"
ebs_device_name = "/dev/xvda"
ebs_volume_size = 100
ebs_volume_type = "gp3"
}
}

queues {
aws_a100 = [["A100", 15]]
}

extra_trains_conf = """
agent.package_manager.system_site_packages = true
agent.package_manager.pip_version = "==20.2.3"
"""

extra_vm_bash_script = """

sudo apt-get install -y libsm6 libxext6 libx...

awesome, thank you 👍

Sorry, its actually
task.update_requirements(["."]) 

Ok so it seems that the single quote is the reason, using double quotes works

edited the aws_auto_scaler.py, actually I think it’s just a typo, I just need to double the brackets

Ho I see, I think we are now touching a very important point:
I thought that torch wheels already included cuda/cudnn libraries, so you don't need to care about the system cuda/cudnn version because eventually only the cuda/cudnn libraries extracted from the torch wheels were used. Is this correct? If not, then does that mean that one should use conda to install the correct cuda/cudnn cudatoolkit?

and just run the same code I run production

Sorry, I was actually able to fix it (using 1.1.3) not sure what was the problem 😄

Awesome, thanks WackyRabbit7 , AgitatedDove14 !

Hi SuccessfulKoala55 , How can I now if I log in in this free access mode? I assume it is since in the login page I only see login field, not password field

Awesome, huge thanks to the team!

Thanks a lot for the solution SuccessfulKoala55 ! I’ll try that if the solution “delete old bucket, wait for its name to be available, recreate it with the other aws account, transfer the data back” fails

yes 🙂

CostlyOstrich36 , this also happens with clearml-agent 1.1.1 on a aws instance…

I see 3 agents in the "Workers" tab

so most likely one hard requirement installs the version 2 of pyjwt while setting up the experiment

I am trying to upload an artifact during the execution