Pytorch is a Python package that is used as a deep learning research platform that provides maximum flexibility and speed. Pytorch provides Tensors, that are basically the same as NumPy array: generic n-dimensional arrays used for arbitrary numeric computation. The biggest difference between a NumPy array and a PyTorch Tensor is that a PyTorch Tensor can run on either CPU or GPU [1].

The Pytorch installation is not so hard itself, but the steps to enable GPU on the local machine are not banal. This article will guide you through the whole process of setting up the required tools and installing drivers required to enable GPU on your Windows machine.

A good IDE to code efficient Python is Pycharm. It’s one of the best tools for programming because it offers many extra features, code analysis, a graphical debugger useful to analyze the errors in your code and supports and allows to set-up an Anaconda virtual environment already created. Even if you prefer other tools than Pycharm, such as Jupiter Notebook, you can still follow the guide to understand how to integrate acceleration libraries in Anaconda.

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