I think it tries to get the latest one. Are you using the agent in docker mode? you can also control this via clearml.conf with agent.cuda_version

I have set agent{cuda_version: 11.2}

Thank you

Isn't the problem that CUDA 12 is being installed?

This one seems to be compatible: [nvcr.io/nvidia/pytorch:22.04-py3](http://nvcr.io/nvidia/pytorch:22.04-py3)

What I dont understand is how to tell clearml to install this version of pytorch and torchvision, with cu118

It seems to find a cuda 11, then it installs cuda 12

Torch CUDA 111 index page found, adding `

`
PyTorch: Adding index `

` and installing `torch ==2.4.0.*`
Looking in indexes: 

, 

, 

Collecting torch==2.4.0.*
  Using cached torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl (797.2 MB)
2024-08-12 12:40:37
Collecting clearml
  Using cached clearml-1.16.3-py2.py3-none-any.whl (1.2 MB)
Collecting triton==3.0.0
  Using cached 

 (209.4 MB)
2024-08-12 12:40:42
Collecting nvidia-nccl-cu12==2.20.5
  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)
Collecting nvidia-curand-cu12==10.3.2.106

I am trying task.create like so:

task = Task.create(
    script="test_gpu.py",
    packages=["torch"],
)

If I run nvidia-smi it returns valid output and it says the CUDA version is 11.2

Solved that by setting docker_args=["--privileged", "--network=host"]

Just to make sure, run the code on the machine itself to verify that python can actually detect the driver