What is the latest CUDA version that support GeForce 450MX

1

My GPU is TU117M [GeForce MX450]. What is the latest CUDA version I can use ?

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Thanks

2

Your MX450 uses the Turing architecture with compute capability 7.5 and is thus supported in all currently released stable and nightly PyTorch binaries.

3

Ok, that’s interesting to know.

I’m using torch 2.0.1 which cuda https://download.pytorch.org/whl/cu118 but I’m still receiving torch.cuda.is_enabled() False due to torch._C._cuda_getDeviceCount() > returning 0.

Any insights on what I’m missing here?

[[tool.poetry.source]]
name = "default"
url = "https://pypi.org/simple"

[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu118"
secondary = true

[tool.poetry.dependencies]
..
python = "~3.10.12"
pytorch-forecasting = { version = "=1.0.0", source = "default" }
..
tensorboard = { version = "*", source = "default"}
tensorboardX = { version = "*", source = "default"}
torch = { version = "=2.0.1", source = "pytorch" }
4

What does torch.version.cuda return?

5

torch.version.cuda returns 11.8

6

Do I need any other prerequisite?

My driver is nvidia-driver-545

7

I upgraded to torch 2.1.0 (torch.version.cuda = 12.1) and still torch._C._cuda_getDeviceCount() returns 0.

I also have below libraries installed.

❯ apt list --installed | grep cuda

WARNING: apt does not have a stable CLI interface. Use with caution in scripts.

cuda-cccl-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-command-line-tools-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-compiler-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-crt-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cudart-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cudart-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cuobjdump-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cupti-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cupti-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-cuxxfilt-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-documentation-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-driver-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-gdb-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-keyring/unknown,now 1.1-1 all [installed]
cuda-libraries-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-libraries-dev-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-nsight-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nsight-compute-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-nsight-systems-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-nvcc-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvdisasm-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvml-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvprof-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvprune-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvrtc-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvrtc-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvtx-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvvm-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-nvvp-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-opencl-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-opencl-dev-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-profiler-api-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-sanitizer-12-3/unknown,now 12.3.52-1 amd64 [installed,automatic]
cuda-toolkit-12-3-config-common/unknown,now 12.3.52-1 all [installed,automatic]
cuda-toolkit-12-3/unknown,now 12.3.0-1 amd64 [installed]
cuda-toolkit-12-config-common/unknown,now 12.3.52-1 all [installed,automatic]
cuda-toolkit-config-common/unknown,now 12.3.52-1 all [installed,automatic]
cuda-tools-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
cuda-visual-tools-12-3/unknown,now 12.3.0-1 amd64 [installed,automatic]
8

Hmm interestingly torch.cuda.current_device() returned an error.

RuntimeError                              Traceback (most recent call last)
Cell In[16], line 1
----> 1 torch.cuda.current_device()

File ~/.cache/pypoetry/virtualenvs/vfm-nn-DOmiObSy-py3.10/lib/python3.10/site-packages/torch/cuda/__init__.py:769, in current_device()
    767 def current_device() -> int:
    768     r"""Returns the index of a currently selected device."""
--> 769     _lazy_init()
    770     return torch._C._cuda_getDevice()

File ~/.cache/pypoetry/virtualenvs/vfm-nn-DOmiObSy-py3.10/lib/python3.10/site-packages/torch/cuda/__init__.py:298, in _lazy_init()
    296 if "CUDA_MODULE_LOADING" not in os.environ:
    297     os.environ["CUDA_MODULE_LOADING"] = "LAZY"
--> 298 torch._C._cuda_init()
    299 # Some of the queued calls may reentrantly call _lazy_init();
    300 # we need to just return without initializing in that case.
    301 # However, we must not let any *other* threads in!
    302 _tls.is_initializing = True

RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx
9

I had a default xorg driver from Ubuntu. I installed the below driver downloaded from NVIDIA and that worked.

@ptrblck thanks for helping me to debug on the right path!

±--------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.113.01 Driver Version: 535.113.01 CUDA Version: 12.2 |
|-----------------------------------------±---------------------±---------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce MX450 Off | 00000000:01:00.0 Off | N/A |
| N/A 60C P0 N/A / ERR! | 5MiB / 2048MiB | 0% Default |
| | | N/A |
±----------------------------------------±---------------------±---------------------+

±--------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 3136 G /usr/lib/xorg/Xorg 4MiB |
±--------------------------------------------------------------------------------------+

10

Good to hear it’s working now!

Just to answer a few things for clarity:

No, you would only need to install a compatible NVIDIA driver and the PyTorch binaries with CUDA support.