![]() Cuda toolkit while installation, installs necessary libraries, and then checks for available visual studio versions in the system and then installs visual studio integrations.so having a visual studio installed in the system is a required step to follow.Visual studio can be downloaded from the official visual studio website of Microsoft, Download the software by selecting workload ‘Desktop development with c++’ and install.We need to install the VC++ 2017 toolset (CUDA is still not compatible with the latest version of Visual Studio). The CUDA Toolkit includes Visual Studio project templates and the NSight IDE (which it can use from Visual Studio). Microsoft Visual Studio is an integrated development environment from Microsoft used to develop computer programs, as well as websites, web apps, web services, and mobile apps. Step1: Installation of visual studio 2017 To make sure that any of the previous NVidia settings or configurations doesn’t affect the installation, uninstall all the NVidia graphics drivers and software (optional step). Once it’s known that the discrete graphics card can support TensorFlow GPU. ![]() Open the run window from the Start menu and run Control /name card will be displayed under Display adapters However, let’s pause and check whether your graphics card is enabled with CUDA as “Making the wrong assumptions causes pain and suffering for everyone” said Jennifer young. All the newer NVidia graphics cards within the past three or four years have CUDA enabled. Tensorflow GPU can work only if you have a CUDA enabled graphics card. Starting with prerequisites for the installation of TensorFlow – GPU On the other hand, GPU comes with its own dedicated VRAM (Video RAM) memory hence makes fewer calls to main memory thus is fastĬPU executes jobs sequentially and has fewer cores but GPUs come with hundreds of smaller cores working in parallel making GPU a highly parallel architecture thereby improving the performance. ![]() Source: Google images Understanding GPUs in Deep learningĬPU’s can fetch data at a faster rate but cannot process more data at a time as CPU has to make many iterations to main memory to perform a simple task.
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