- #LINUX GET NVIDIA DRIVER VERSION UPGRADE#
- #LINUX GET NVIDIA DRIVER VERSION FULL#
- #LINUX GET NVIDIA DRIVER VERSION DOWNLOAD#
Nvidia-331-updates-uvm - Transitional package for nvidia-340 Nvidia-331-updates-dev - Transitional package for nvidia-340-dev Nvidia-331-updates - Transitional package for nvidia-340 Nvidia-331-dev - Transitional package for nvidia-340-dev Nvidia-331 - Transitional package for nvidia-331
Sample output: nvidia-304-dev - NVIDIA binary Xorg driver development files Step 3: Check existing NVIDIA driver packages cached by apt $ sudo apt-cache search nvidia | grep -E "nvidia-"
#LINUX GET NVIDIA DRIVER VERSION DOWNLOAD#
Step 2: Check the recommended driver version from NVidia website.įrom the NVIDIA driver download page, we provide the graphics card, OS, the CUDA toolkit information.įor Tesla K80 to be installed on Ubuntu 16.04 with CUDA toolkit 9.1, the recommended driver version was 390.46. Output: 84:00.0 3D controller: NVIDIA Corporation GK210GL (rev a1)Ĩ5:00.0 3D controller: NVIDIA Corporation GK210GL (rev a1)Įxplanation: From the output above, we can see that the graphics card model is Tesla K80. Step 1: Check that the graphics card is connected to PCI bus $ lspci | grep -i nvidia
#LINUX GET NVIDIA DRIVER VERSION UPGRADE#
Otherwise, you may check the driver version and perform version upgrade if necessary. It means that the driver truly is not installed yet and we can safely proceed with the remaining installation steps. If you see this error: bash: nvidia-smi: command not found Invoke this command from the terminal: $ nvidia-smi 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:
#LINUX GET 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. 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.
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