A step by step guide to building CUDA/cuDNN from source to use for GPU accelerated deep learning
Installing TensorFlow/CUDA/cuDNN for use with accelerating hardware like a GPU can be non-trivial, especially for novice users on a windows machine. The below describes how to build the CUDA/cuDNN packages from source so that TensorFlow tasks can be accelerated with a Nvidia RTX 30XX GPU.
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Before beginning it is worthwhile to note that TensorFlow and associated dependencies require very specific driver/package versions, as such it is recommended to use the exact versions specified in this article.
The Easy-Peasy Tensorflow-GPU Installation(Tensorflow 2.1, CUDA 11.0, and cuDNN) on Windows 10. The simplest way to install Tensorflow GPU on Windows 10.
Writing this article to help out those who have trouble in setting up Cuda enabled TensorFlow deep learning environment. If you don’t have Nvidia GPU configured in your system then this article is not for you
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.
If you working with Machine Learning using GPU this story is the answer.