For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core.
In this article, we will learn howtoinstallPytorch on Windows. PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab.
In the previous stage of this tutorial, we discussed the basics of PyTorch and the prerequisites of using it to create a machine learning model. Here, we'll install it on your machine.
Installing and using PyTorch in Python is a crucial step for anyone interested in deep learning. By following the installation steps, understanding basic usage, and adopting common and best practices, you can efficiently develop deep learning models.
But before jumping into deep learning projects, the first task is to installPyTorch properly. For a new user, setting up PyTorch may appear to pose challenges, but a little training for users does wonders in making this setup a breeze.
First, you need to choose the appropriate PyTorch version based on your CUDA (NVIDIA GPU computing platform) availability. If you don't have an NVIDIA GPU or don't want to use GPU acceleration, you can install the CPU - only version: If you have an NVIDIA GPU and want to leverage its computing power, you need to install the CUDA - enabled version.
Learn to installPyTorch using Docker, as well as with and without a venv on Windows and Linux. PyTorch has experienced explosive growth since its initial release in 2016.
