PyTorch is an open source machine learning (ML) framework based on the Python programming language and the Torch library. It is one of the preferred platforms for deep learning research. The framework is built to speed up the process between research prototyping and deployment.

Different back-end support

PyTorch uses different backends for CPU, GPU and for various functional features rather than using a single back-end. It uses tensor backend TH for CPU and THC for GPU. While neural network backends such as THNN and THCUNN for CPU and GPU respectively.

PyTorch is especially popular with Python developers because it’s written in Python and uses that language’s imperative, define-by-run eager execution mode in which operations are executed as they are called from Python.

It is easy to learn, use, extend, and debug. Great API: PyTorch shines in term of usability due to better designed Object Oriented classes which encapsulate all of the important data choices along with the choice of model architecture.

This open-source machine learning library is based on Torch and designed to provide greater flexibility and increased speed for deep neural network implementation. Currently, PyTorch is the most favored library for AI (Artificial Intelligence) researchers and practitioners worldwide in the industry and academia.