How to Setup PyTorch

How to Setup PyTorch

In this post, we’ll be covering how to set up PyTorch for your machine. PyTorch is a tool for building machine learning models in Python.

Check out this video for more information:


PyTorch is an open source machine learning framework that can be used to easily and flexibly design, implement and train deep learning models. While PyTorch has a lot of features, in this tutorial we’ll be focusing on only a few key features that we think will be most useful to you as you get started with PyTorch. These include:

-The autograd package, which allows us to automatically compute gradients
-The nn package, which provides us with many pre-defined neural network modules and functions
-The optim package, which provides us with commonly used optimization algorithms

We’ll also be using a few other standard Python packages throughout the tutorial.

What is PyTorch?

PyTorch is a machine learning library for Python that allows you to create and train neural networks. It is similar to other libraries such as TensorFlow and Theano, but PyTorch is more user-friendly and easier to learn. In this guide, we will show you how to install PyTorch on your computer.

PyTorch Installation

This document will outline how to install PyTorch on your machine. PyTorch is a deep learning platform that provides a seamless path from research prototyping to production deployment.

Before we begin, you should have a basic understanding of computers (i.e., be able to use a text editor, know what a command line is, and be somewhat familiar with directory structures) and understand the basics of deep learning (i.e., know what a tensor is and be able to forward propagate an input through a simple neural network).

If you need to install PyTorch on a Linux system, refer to the official installation instructions here:

If you need to install PyTorch on a Windows system, refer to the official installation instructions here:
##Title: Setting up your Development Environment
##Heading: Development Environment
##Keywords: Development environment, python, IDLE

Python is an easy to use scripting language which has many existing libraries for data analysis tasks such as linear algebra (numpy), data visualization (matplotlib), and general purpose programming (scikit-learn). In this tutorial we will cover setting up your development environment so that you can get started coding in Python.

You will need two things to start coding in Python:
1) An Integrated Development Environment or IDE – This is essentially a text editor which makes writing code easier by providing features such as syntax highlighting and code completion. There are many IDEs available for Python but we recommend using the free IDE called IDLE which is included when you download Python from
2) The Python interpreter – This is the program that actually runs your code. When you write code in an IDE it needs to be translated into instructions that the computer can understand and this is what the interpreter does

PyTorch Basics

PyTorch is a powerful, flexible deep learning platform that provides a easy-to-use interface for data scientists and engineers. Its core features include a powerful numpy-like tensor manipulation library, automatic differentiation for building gradient-based models, and a rich set of tools and libraries for data visualization and analysis.

In this guide, we’ll cover the basic steps necessary to get started with PyTorch. We’ll show you how to install PyTorch, how to create your first deep learning model using PyTorch Tensors, and we’ll introduce some of the most popular libraries for deep learning in Python.

So let’s get started!

Creating Tensors

Creating Tensors in PyTorch is easy. The most basic way is to create a tensor by passing in a list:

import torch
x = torch.Tensor([5, 3, 8])

Alternatively, you can create a tensor by passing in an array:
import numpy as np
a = np.array([5, 3, 8])
x = torch.from_numpy(a)

Operations with Tensors

tensors can be created from Python lists with the torch.Tensor() function. The function also accepts a sequence of numbers and a list of lists.
An operation with a tensor that does not change its size or number of dimensions is called an element-wise operation.
The most basic element-wise operation is addition. For example, we can add two tensors of size 3×3 and obtain another tensor of the same size:
tensor1 = torch.Tensor([[1,2,3], [4,5,6], [7,8,9]])
tensor2 = torch.Tensor([[1,1,1], [2,2,2], [3,3,3]])
print (tensor1 + tensor2)

NumPy Bridge

PyTorch is a powerful, yet easy to use, deep learning framework. One of its many strengths is its ability to bridge the gap between NumPy, a powerful numerical computation library, and PyTorch’s tensors, a powerful data structure for deep learning.

This tutorial will show you how to set up PyTorch so that you can use NumPy arrays with PyTorch tensors. This is useful if you want to use PyTorch’s tensors but still have access to NumPy’s powerful numerical computation library.

First, we need to import the PyTorch and NumPy libraries:

import torch
import numpy as np

Next, we need to create a PyTorch tensor and convert it to a NumPy array:

# Create a PyTorch tensor
x = torch.tensor([1, 2, 3])
# Convert the tensor to a NumPy array
x_np = x.numpy()

CUDA Tensors

Working with CUDA tensors is pretty similar to working with regular tensors in PyTorch. The biggest difference is that you need to make sure your tensors are created on the correct device.

There are two ways to do this:

1. Creating a CUDA tensor directly:

>>> cuda_tensor = torch.cuda.FloatTensor(10)

2. Creating a regular tensor and then transferring it to the GPU:

>>> regular_tensor = torch.FloatTensor(10)
>>> cuda_tensor = regular_tensor.cuda()


Pytorch is a powerful, yet simple to use deep learning framework. It offers an easy to use API and integrates smoothly with the python data science ecosystem. In this article we will see how to setup Pytorch on Ubuntu and will run a simple autograd example.

Autograd is a package for automatic differentiation of pytorch tensors. It allows us to easily compute gradients of pytorch tensors wrt to parameters or other tensors. In this simple example, we will see how autograd can be used to compute the gradient of a function wrt its input.

We start by defining a function f which takes an input x and returns x2+1. We then create a tensor t with requires_grad set to True which tells autograd that we want to compute gradients wrt this tensor.

Next, we define our loss as the mean of f(t). This loss requires no parameters so we do not need to specify any when calling backward(). Finally, we print the value and gradient of t at x=1.0


While there are many ways to get PyTorch working on your system, the Anaconda distribution is by far the easiest. Simply download and install Anaconda, then create a new environment for PyTorch. In this environment, you can then install PyTorch using the conda command. After that, you’re ready to start using PyTorch!

Keyword: How to Setup PyTorch

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