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Nvidea Cuda Table Software You AreUsing one of these methods, you will be able to see the CUDA version regardless the software you are using, such as PyTorch, TensorFlow, conda (MinicondaAnaconda) or inside docker.
![]() If you havent, you can install it by running sudo apt install nvidia-cuda-toolkit. What is CUDA CUDA is a general parallel computing architecture and programming model developed by NVIDIA for its graphics cards (GPUs). Using CUDA, PyTorch or TensorFlow developers will dramatically increase the performance of PyTorch or TensorFlow training models, utilizing GPU resources effectively. In GPU-accelerated technology, the sequential portion of the task runs on the CPU for optimized single-threaded performance, while the computed-intensive segment, like PyTorch technology, runs parallel via CUDA at thousands of GPU cores. When using CUDA, developers can write a few basic keywords in common languages such as C, C, Python, and implement parallelism. Method 1 Use nvcc to check CUDA version If you have installed the cuda-toolkit software either from the official Ubuntu repositories via sudo apt install nvidia-cuda-toolkit, or by downloading and installing it manually from the official NVIDIA website, you will have nvcc in your path (try echo PATH ) and its location will be usrbinnvcc (by running which nvcc ). To check CUDA version with nvcc, run nvcc --version You can see similar output in the screenshot below. The version here is 10.1. Yours may vary, and can be either 10.0, 10.1, 10.2 or even older versions such as 9.0, 9.1 and 9.2. Nvidea Cuda Table Full Text OutputAfter the screenshot you will find the full text output too. Cuda compilation tools, release 10.1, V10.1.243 What is nvcc nvcc is the NV IDIA C UDA C ompiler, thus the name. For other usage of nvcc, you can use it to compile and link both host and GPU code. Nvidea Cuda Table Driver The SecondMethod 2 Check CUDA version by nvidia-smi from NVIDIA Linux driver The second way to check CUDA version is to run nvidia-smi, which comes from downloading the NVIDIA driver, specifically the NVIDIA-utils package. You can install either Nvidia driver from the official repositories of Ubuntu, or from the NVIDIA website. Heres my version is CUDA 10.2. You may have 10.0, 10.1 or even the older version 9.0 or 9.1 or 9.2 installed. Importantly, except for CUDA version. There are more details in the nvidia-smi output, driver version (440.100), GPU name, GPU fan percentage, power consumptioncapability, memory usage, can also be found here. You can also find the processes which use the GPU at the moment. This is helpful if you want to see if your model or system is using GPU such as PyTorch or TensorFlow. For most functions, GeForce Titan Series products are supported with only little detail given for the rest of the Geforce range. NVSMI is also a cross-platform application that supports both common NVIDIA driver-supported Linux distros and 64-bit versions of Windows starting with Windows Server 2008 R2. Metrics may be used directly by users via stdout, or stored via CSV and XML formats for scripting purposes. Method 3 cat usrlocalcudaversion.txt cat usrlocalcudaversion.txt Note that if you install Nvidia driver and CUDA from Ubuntu 20.04s own official repository this approach may not work. CUDA version Time Needed: 5 minutes There are basically three ways to check CUDA version. Perhaps the easiest way to check a file Run cat usrlocalcudaversion.txt Note: this may not work on Ubuntu 20.04 Another method is through the cuda-toolkit package command nvcc. The other way is from the NVIDIA drivers nvidia-smi command you have installed.
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