This Just Indicates The Latest Version Of Cuda Your Graphics Driver Is Compatible With.
If you look at the official google build you will find it is linked to cuda 10 and cudnn 7. Part of the beauty of pytorch is that you don't have to install cuda. The cuda toolkit targets a class of applications whose control part runs as a process on a general purpose computing device, and which use one or more nvidia gpus as coprocessors for accelerating single program, multiple data (spmd) parallel jobs.
In The Example Above The Graphics Driver Supports Cuda 10.1 As Well As All Compatible Cuda Versions Before 10.1.
Deactivate the nvidia driver by choosing x.org with the additional drivers tool, reboot, then activate the nvidia driver, reboot and enroll the key in secure boot. So it has to do with secure boot, but it is not necessary to deactivate. Difference between the driver and runtime apis.
This Performance Analysis Uses An Rtx 3080 To Showcase 15 Pc Games Using This Driver Feature, Off Versus On, With Our Latest Recommended Geforce Game Ready Driver, And The Latest Version Of Windows 10.
How important are these extensions for performance? The solution by markus lead me to a better solution. The new gpus need the latest nvidia driver and you will need/want a build of tensorflow that is linked against the new cuda 11.1 and cudnn 8.0 libraries (or newer versions).
To Fix The Problem, Just Do 3 Steps:
Entire site just this document clear search search. The cuda version displayed in this table does not indicate that the cuda toolkit or runtime are actually installed on your system. Deep learning researchers and framework developers worldwide rely on.
Cudnn Provides Highly Tuned Implementations For Standard Routines Such As Forward And Backward Convolution, Pooling, Normalization, And Activation Layers.
The main differences that i can identify (from the ubuntu package lists) are the following: Download nvidia geforce graphics driver 516.94. It installed nvcc 9.1 though, which might be why.