You don't have to waste time trying to get TensorFlow–GPU up and running. Instead, you can follow the complicated procedure to find out which steps are crucial and which are not. Get a general overview of TensorFlow–GPU and why it might make a good choice for your machine learning or deep learning development environment. Next, we'll discuss which Python version is best for you and how it interacts with your TF–GPU. You will also learn how to determine if your graphics card is suitable for the task and what options you have based on your hardware. Once you have met all requirements, the Cuda toolkit will be installed to provide a development environment that allows you to create high-performance GPU-accelerated apps. The toolkit contains GPU-accelerated libraries, optimization tools, debugging and optimization tools, and a runtime library for deploying your applications. We'll also need Visual Studio IDE to install the C++ development libraries required by the toolkit. This step is often overlooked by users, and they end up with a problem with their toolkit not being installed correctly. This library provides highly tuned implementations of standard routines like forward and backward convolutions, pooling, normalization, activation layers, and normalization. Because cuDNN is not an application, but a library, we will need to provide a path. We'll also explore how to make our system find these libraries. Finally, we will install TensorFlow–GPU. We will verify it by running basic commands. Also, we'll verify if it makes use of your GPU. TensorFlow is a hardware-dependent development environment that allows deep learning tasks to be performed in the most efficient way possible.
