#Ubuntu check nvidia driver version install
#Ubuntu check nvidia driver version driver
#Ubuntu check nvidia driver version full
#Ubuntu check nvidia driver version code
#Ubuntu check nvidia driver version download

#Ubuntu check nvidia driver version driver

If you are not installing the driver on a freshly created Ubuntu VM or newly installed Ubuntu OS, you may need to perform a quick check for the driver status. Step 0: Perform a quick test for checking driver installation status After rebooting / turning on the machine, let’s open a terminal session for command line installation. It is important to note that if you plan to use an NVIDIA GPU for deep learning purpose, you need to make sure that the compute capability of the GPU is at least 3.0 (Kepler architecture).Īfter ensuring that you already have the right graphics card and have it properly mounted on the PCI / PCI-e slot, we’ll now proceed with the graphics card installation. Note on CUDA compute capability and deep learning:

#Ubuntu check nvidia driver version full

The full list of the available features in each compute capability can be seen here. CUDA was designed to speed up computation by harnessing the power of the parallel computation utilizing hundreds or thousands of the GPU cores.ĬUDA-enabled GPUs: NVIDIA GPUs that support CUDA programming model and implementationĬUDA compute capability: A number that refers to the general specifications and available features especially in terms of parallel computing methods of a CUDA-enabled GPU. A graphics card can contain one or more GPUs while one GPU can be built of hundreds or thousands of cores.ĬUDA: A parallel programming model and the implementation as a computing platform developed by NVIDIA to perform computation on the GPUs. A unit of computation, in a form of a small chip on the graphics card, traditionally intended to perform rapid computation for image / graphics rendering and display purpose. GPU: Graphical / Graphics Processing Unit. We will specifically focus on NVIDIA display driver installation due to the pervasiveness and robustness of NVIDIA GPUs as deep learning infrastructure.īefore proceeding to the installation, let’s discuss some key terminologies related with the use of NVIDIA GPUs as the computing infrastructure in a deep learning system. In this post, we will go few steps back to the very basic prerequisite of setting up a GPU-powered deep learning system: display driver installation. For instance, a minimum version of the gcc compiler is needed for compatibility with specific CUDA versions.In the recent posts, we have been going through the installation of deep learning framework like Caffe2 and its dependencies, such as CUDA or cuDNN. The third thing you may need to consider is compiler compatibility. CUDA Version Minimum Compute Capability Default Compute Capability

#Ubuntu check nvidia driver version install

This in turn means that I need to install an nVidia driver that is compatible with my CUDA version. For instance, my laptop has an nVidia CUDA 2.1 GPU, which means I can't install a CUDA toolkit more recent than CUDA 8.0 GA2.

#Ubuntu check nvidia driver version code

A GPU will support a specific compute version, and if your CUDA version minimum compute capability is higher than that supported by your GPU, your code will not compile or run. The other half is the Compute Capability. The driver version is only half of the compatibility equation. The general support matrix is contained in the linux install guide for the specific CUDA version of interest.įor reference, on linux, the previous CUDA toolkits required the following minimum driver versions: For versions newer than 11.0, refer to the table in the release notes linked belowĪ portion of this table is maintained as Table 1 here in the release notes. Not all distros are supported on every CUDA toolkit version. Note that any given CUDA toolkit has specific Linux distros (including version number) that are supported.

#Ubuntu check nvidia driver version download

Older CUDA toolkits are available for download here. Starting with CUDA 9.x, older CUDA GPUs of compute capability 2.x are also not supported. Note that CUDA 7 will not be usable with older CUDA GPUs of compute capability 1.x. This is not necessary if you want to use CUDA 7, you can just use the runfile installer method which has all the necessary components including an appropriate driver (346.46). I believe you are picking up a 304.xx driver via a specific (ie. If you want to reinstall ubuntu to create a clean setup, the linux getting started guide has all the instructions needed to set up CUDA if that is your intent. 304.xx is a driver that will support CUDA 5 and previous (does not support newer CUDA versions.)