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Step By Step Installation Of Cuda And CuDNN On Windows
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This video is an installation guide to Nvidia CUDA Development Kit version 10.0.130 and Nvidia CUDNN version 7.6.4 on Windows 10 machines.Since CUDA does not have it's own C++ compiler we use Visual Studio 2017 to compile nvidia programs written in C++.
CUDA(Compute Unified Device Architecture) is a parallel computing platform and an API model created by Nvidia which allows data scientists and machine learning engineers to use a CUDA-enabled accelerated graphics processing. The processing depends on the available memory and the no of cuda cores present in the GPU.
The Nvidia CUDA Deep Neural Network Library is a GPU accelerated library for deep neural networks which provides highly tuned implementation for standard routines such as forward and backward propagation, convolution, pooling, normalization and activation layers.
The following installation guide is tested on:
Python Version : 3.7.3
Operating System: Windows 10
Nvidia Cuda Driver Version: 446.14(Nvidia Cuda 11.0.140 Driver)
You can also test with Nvidia Cuda Driver version greater than or equal to the toolkit version you are installing
Visual Studio 2017
Visual Studio Code to train mnist dataset on gpu
Tensorflow with GPU support version 2.0.0
Install tensorflow 2.0.0 with gpu support using:
pip install tensorflow-gpu==2.0.0
CUDA Compatibility With Nvidia Drivers:
CUDA And CUDNN Compatibility With Tensorflow Versions
Steps Involved During the Installation
2)
3) Install Cudnn 7.6.4 from
4) Edit environment variables and add cuda to path
5) Open cmd and install tensorflow-gpu==2.0.0
6) Test and check for cuda installation using:
Finally train the neural network on mnist dataset.
If you face any errors or encounter any problems feel free to ask..I am always there to help you out..
Thank you
CUDA(Compute Unified Device Architecture) is a parallel computing platform and an API model created by Nvidia which allows data scientists and machine learning engineers to use a CUDA-enabled accelerated graphics processing. The processing depends on the available memory and the no of cuda cores present in the GPU.
The Nvidia CUDA Deep Neural Network Library is a GPU accelerated library for deep neural networks which provides highly tuned implementation for standard routines such as forward and backward propagation, convolution, pooling, normalization and activation layers.
The following installation guide is tested on:
Python Version : 3.7.3
Operating System: Windows 10
Nvidia Cuda Driver Version: 446.14(Nvidia Cuda 11.0.140 Driver)
You can also test with Nvidia Cuda Driver version greater than or equal to the toolkit version you are installing
Visual Studio 2017
Visual Studio Code to train mnist dataset on gpu
Tensorflow with GPU support version 2.0.0
Install tensorflow 2.0.0 with gpu support using:
pip install tensorflow-gpu==2.0.0
CUDA Compatibility With Nvidia Drivers:
CUDA And CUDNN Compatibility With Tensorflow Versions
Steps Involved During the Installation
2)
3) Install Cudnn 7.6.4 from
4) Edit environment variables and add cuda to path
5) Open cmd and install tensorflow-gpu==2.0.0
6) Test and check for cuda installation using:
Finally train the neural network on mnist dataset.
If you face any errors or encounter any problems feel free to ask..I am always there to help you out..
Thank you
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