This has been resolved now! Thank you for your help @<1523701070390366208:profile|CostlyOstrich36>

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

ERROR: This container was built for NVIDIA Driver Release 530.30 or later, but
       version 460.32.03 was detected and compatibility mode is UNAVAILABLE.

       [[System has unsupported display driver / cuda driver combination (CUDA_ERROR_SYSTEM_DRIVER_MISMATCH) cuInit()=803]]

OK, then just try the docker image I suggested 🙂

within a docker

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

Just try as is first with this docker image + verify that the code can access cuda driver unrelated to the agent

or cu11x

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

I can install the correct torch version with this command:
pip install --pre torchvision --force-reinstall --index-url ` None ```

unrelated to the agent itself

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

CUDA is the driver itself. The agent doesn't install CUDA but installs a compatible torch assuming that CUDA is properly installed.

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

Thank you I will try that

It means that there is an issue with the drivers. I suggest trying this docker image - nvcr.io/nvidia/pytorch:23.04-py3

@<1523701070390366208:profile|CostlyOstrich36> same error now 😞

Environment setup completed successfully
Starting Task Execution:
/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/torch/cuda/__init__.py:128: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11020). Please update your GPU driver by downloading and installing a new version from the URL: 

 Alternatively, go to: 

 to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
False
Traceback (most recent call last):
  File "facility_classifier/test_gpu.py", line 8, in <module>
    assert torch.cuda.is_available()
AssertionError

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

to achieve running both the agent and the deployment on the same machine, adding --network=host to the run arguments solved it!

But the process is still hanging, and not proceeding to actually running the clearml task

I have set agent.package_manager.pip_version="" which resolved that message

Collecting pip<20.2
Using cached pip-20.1.1-py2.py3-none-any.whl (1.5 MB)
Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 20.0.2
Not uninstalling pip at /usr/lib/python3/dist-packages, outside environment /usr
Can't uninstall 'pip'. No files were found to uninstall.

@<1523701070390366208:profile|CostlyOstrich36> do you have any ideas?

It's hanging at

Installing collected packages: zipp, importlib-resources, rpds-py, pkgutil-resolve-name, attrs, referencing, jsonschema-specifications, jsonschema, certifi, urllib3, idna, charset-normalizer, requests, pyparsing, PyYAML, six, pathlib2, orderedmultidict, furl, pyjwt, psutil, python-dateutil, platformdirs, distlib, filelock, virtualenv, clearml-agent
Successfully installed PyYAML-6.0.2 attrs-23.2.0 certifi-2024.7.4 charset-normalizer-3.3.2 clearml-agent-1.8.1 distlib-0.3.8 filelock-3.15.4 furl-2.1.3 idna-3.7 importlib-resources-6.4.0 jsonschema-4.23.0 jsonschema-specifications-2023.12.1 orderedmultidict-1.0.1 pathlib2-2.3.7.post1 pkgutil-resolve-name-1.3.10 platformdirs-4.2.2 psutil-5.9.8 pyjwt-2.8.0 pyparsing-3.1.2 python-dateutil-2.8.2 referencing-0.35.1 requests-2.31.0 rpds-py-0.20.0 six-1.16.0 urllib3-1.26.19 virtualenv-20.26.3 zipp-3.20.0
WARNING: You are using pip version 20.1.1; however, version 24.2 is available.
You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.

@<1523701070390366208:profile|CostlyOstrich36> I'm now running the agent with --docker , and I'm using task.create(docker="nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04")

Thank you

I suggest running it in docker mode with a docker image that already has cuda installed

I am running the agent with clearml-agent daemon --queue training

agent.cuda_version="11.2"

docker="nvidia/cuda:11.8.0-base-ubuntu20.04"