PyTorch provides an end-to-end deep learning system. Its features are as follows:

• Python utilization: PyTorch is not simply a Python binding to a C++ framework. It is deeply integrated in Python so that it can be used with other popular libraries and frameworks.

• Tools and libraries: It has an active community of researchers and developers in the areas of computer vision and reinforcement learning.
• Flexible frontend: This includes ease of use and hybrid in eager mode, accelerate speeds and seamless transitions to graph mode, and functionality and optimization in C++ runtime.
• Cloud support: This is supported on all of the major cloud platforms, allowing for seamless development and scaling with prebuilt images, so that it is able to run as a production-grade application.
• Distributed training: This includes performance optimization with the advantage of native support for the asynchronous execution of operations and peer-to-peer (p2p) communications, so that we can access both C++ and Python.
• Native support for ONNX: We can export models into the standard Open Neural Network Exchange (ONNX) format for access to other platforms, runtimes, and visualizers.