PyTorch is an open-source machine learning framework for the Python language based on Torch. It is used for solving various tasks, such as computer vision, natural language processing, etc. Facebook's artificial intelligence group primarily develops it. This framework also contains an ecosystem of different libraries developed by third-party teams. You can install this software on Windows.

What is this program?

PyTorch is an open-source machine-learning library for Python. It is used for applications such as natural language processing. It was developed by Facebook's artificial intelligence research group and Uber Pyro's probabilistic programming software, based on it. Hugh Perkins developed PyTorch as a Python shell for LusJIT, based on the Torch platform. There are two versions of PyTorch.

PyTorch redesigns and embeds Torch into Python, sharing the same basic C libraries for back-end code. The PyTorch developers have tuned this back-end code to work efficiently with Python. They have also retained the GPU-based hardware acceleration and the extensibility features that made Torch based on Lua.

Software features

PyTorch for Windows has the following features:

• User-friendly interface: PyTorch has an easy-to-use API that is very convenient and works in Python. Code execution in this environment is relatively easy;

• Use of Python: this library is considered Pythonic, which integrates seamlessly with the Python data stack. Thus, it can use all the services and functions offered by the Python environment;

• Computational graphs: PyTorch has an excellent platform that offers dynamic computational graphs. A user can change them at runtime. This is very useful when a developer does not know how much memory is required to create a neural network model;

• PyTorch is closely related to the Lua-based Torch framework, which Facebook actively uses;

• PyTorch is relatively new compared to other similar technologies;

• PyTorch has imperative and dynamic functionality;

• The computation graph in PyTorch is defined at runtime;

• PyTorch includes deployability for mobile and embedded platforms.

Abstraction levels

PyTorch for Windows is known for three levels of abstraction, as listed below:

• The tensor is an imperative n-dimensional array running on the GPU;

• The variable is a node in a computational graph. It stores the data and the gradient;

• The module is the layer of the neural network that stores state data.

What's great about this program?

PyTorch for Windows has the following advantages:

• This code is easy to debug and understand;

• It includes many layers like a torch;

• There are numerous loss functions;

• This program can be considered an extension of NumPy for GPUs;

• It allows you to build networks whose structure depends on the computations.