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PyTorch is a popular open-source machine learning library that is primarily used for deep learning tasks such as neural network development, image classification, natural language processing, and many more. It is written in Python and has gained significant popularity due to its user-friendly APIs, powerful functionalities, and flexibility. However, with the growing demand for high-performance computing, the need for other programming languages to interface with PyTorch has increased. In this article, we will discuss how PyTorch can be used in C# programming.

What is PyTorch?

PyTorch is a machine learning library developed by Facebook AI Research. It was first released in 2016 and has since then become one of the most popular deep learning libraries. PyTorch allows developers to build and train neural networks using a dynamic computational graph. The library supports both CPU and GPU computations, making it a powerful tool for training deep learning models.

PyTorch in C#

C# is a popular object-oriented programming language developed by Microsoft. It is widely used for developing Windows desktop applications, games, and web applications. Recently, there has been a growing demand for using C# with machine learning frameworks, including PyTorch. The good news is that PyTorch can be used in C# programming with the help of the PyTorchSharp library.

PyTorchSharp is a .NET binding to the PyTorch library. It enables developers to use PyTorch functionalities in their C# projects. PyTorchSharp is still in its early stages of development, but it already supports many PyTorch features, including tensor operations, autograd, and neural network modules.

Installing PyTorchSharp

Before you can use PyTorchSharp in your C# projects, you need to install it. PyTorchSharp can be installed using NuGet, a package manager for .NET projects. To install PyTorchSharp, open the NuGet package manager in Visual Studio and search for “PyTorchSharp.” Once you find the package, click on “Install” to install it.

Using PyTorch in C#

Once you have installed PyTorchSharp, you can start using PyTorch in your C# projects. The first step is to import the PyTorchSharp namespace in your C# code.

using TorchSharp;

You can then create a tensor using PyTorchSharp’s tensor method.

var tensor = Torch.tensor(new float[] { 1, 2, 3, 4 });

PyTorchSharp also supports autograd, which allows you to compute gradients automatically. You can enable autograd by setting the requires_grad property of a tensor to true.

var x = Torch.tensor(new float[] { 1, 2, 3 }, requires_grad: true);
var y = x.pow(2).sum();

The above code creates a tensor x and sets requires_grad to true. It then computes the sum of the squares of the elements in x and stores it in y. Finally, it calls the backward method on y to compute the gradients of y with respect to x.

PyTorchSharp also supports neural network modules. You can create a neural network module using PyTorchSharp’s nn module.

using TorchSharp.nn;

class MyNet : nn.Module
public MyNet()
this.Linear1 = nn.Linear(10, 5);
this.Linear2 = nn.Linear(5, 1);

public nn.Linear Linear1 { get; set; }
public nn.Linear Linear2 { get; set; }

public override Tensor forward(Tensor input)
var x = this.Linear1.forward(input);
x= Torch.sigmoid(x);
x = this.Linear2.forward(x);
return x;

var net = new MyNet();
var input = Torch.randn(new long[] { 1, 10 });
var output = net.forward(input);

The above code defines a neural network module with two linear layers. The forward method computes the output of the network given an input tensor. The code then creates an instance of the network and passes a random input tensor to it to compute the output.


In conclusion, PyTorch is a powerful machine learning library that can be used in C# programming using the PyTorchSharp library. PyTorchSharp provides C# developers with access to many of PyTorch’s powerful functionalities, including tensor operations, autograd, and neural network modules. With the growing demand for high-performance computing, the ability to use PyTorch in C# programming can open up new possibilities for machine learning applications in C# projects.

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