I have a Nvidia GeForce GTX 770, which is CUDA 3.0 compatible, but upon running PyTorch training on the GPU, I get the warning
Found GPU0 GeForce GTX 770 which is of cuda capability 3.0.
PyTorch no longer supports this GPU because it is too old.
The minimum cuda capability that we support is 3.5.
and subsequently the error RuntimeError: CUDA error: no kernel image is available for execution on the device.
Was there an old PyTorch version, that supported graphics cards like mine with CUDA capability 3.0? If yes, which version, and where to find this information? Is there a table somewhere, where I can find the supported CUDA versions and compatibility versions?
If it is relevant, I have CUDA 10.1 installed.
Related
I have a Lenovo G580 computer with intel CPU and a Nvidia 610M GPU. Running Linux Lite OS (Ubuntu based).
I would like to use Nvidia prime to run programs with the GPU.
I installed some packages about Nvidia drivers, version 390 according to this page.
With the Nvidia X Server Settings I can switch to on demand mode. On th UI there is only one settings for prime, no mention about the GPU settings.
My problem is that when the on demand mode is enable, many programs (games and glx debug programs) throw this error : (even without asking to use GPU)
Error: couldn't find RGB GLX visual or fbconfig
I know there is other posts like mine on internet however I can't understand the problem or identify a missing package on my computer. Have you already install prime on this GPU ? I can send logs or system info if needed.
According to the official docs, now PyTorch supports AMD GPUs. ROCm 4.2 can be installed through pip. But I can not find in Google nor the offical docs how to force my DL training to use the gpu. What is the AMD equivalent to the following command?
torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ROCm translates CUDA codes. So torch.cuda.is_available() should work.
I have an ASUS laptop with built-in NVIDIA GeForce GT 540M. I have installed the Cuda toolkit and CuDNN package and other utilities like Visual C++. But whey I run this codetf.config.experimental.list_physical_devices(). only CPU is there no GPU appeared. My question is can I use this GPU for machine learning or not?
https://developer.nvidia.com/cuda-gpus
Under "CUDA-Enabled GeForce and TITAN Products" you can see that your GPU is CUDA enabled. It also has a compute capability of 2.1.
Incidentally all CUDA versions have a minimum compute capability they require.
https://tech.amikelive.com/node-930/cuda-compatibility-of-nvidia-display-gpu-drivers/
As you can see most of the latest versions of CUDA require compute capability 3 or higher.
If you'd like to try installing an older version of CUDA, make sure to check out the following link to see a list of tested OS - tensorflow - CUDA - cuDNN combinations.
Which TensorFlow and CUDA version combinations are compatible?
I recently installed Tensorflow onto my system. Upon fitting to the model, I am getting this error in the Jupyter Notebook terminal:
This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
I read that I am supposed to build Tensorflow for my system, but I am unaware how to. What kinda settings do I need to set? What compiler flags do I need? Here is my setup:
GPU: AMD RX 5700XT
CPU: AMD Ryzen 9 3900X
RAM: 64GB DDR4
OS: Ubuntu 20.04
Can someone give me advice on how to build Tensorflow for my system?
Most deep learning and machine learning frameworks and libraries use NVIDIA CUDA for GPU processing, we would need to select an NVIDIA graphics card.
Note While Amd has some excellent graphics card models, their compatibility and support with ML tasks are still experimental. So we will need to stick to NViDia.
AMD provides a ROCm enabled TensorFlow library for AMD GPUs. It's based on the ROCm software stack. To know more about this library: MIOpen - ROCm.
Based on these factors, the following graphics cards families can be recommended:
GeForce 10 series
GeForce 16 series.
GeForce 20 series.
GeForce 30 series
I have an old Nvidia Quadro FX 3700 graphic card using the 173.14.39 nvidia-driver, and I plan to use it for rendering with blender 2.72b on my Gentoo system (3.12.30-gentoo kernel)
I am uncertain how to install cuda for a legacy card and get it work with blender.
Do you have some hints?
The three components of CUDA can be installed using this command.
emerge nvidia-cuda-sdk
For more information check here.