PyTorch is an open-source framework built for developing machine learning and deep learning models. In particular, this framework provides the stability and support required for building computational models in the development phase and deploying them in the production phase.
PyTorch functionalities are extensible with other Python libraries like NumPy and SciPy. Additionally, the strong GPU acceleration of PyTorch enables it to perform high-level tensor computations with ease.
It’s also used with TorchScript, which is a built-in tool that makes PyTorch flexible while seamlessly transitioning between eager execution mode and graph mode to achieve higher speed and optimization. The latest PyTorch versions also support graph-based execution, distributed training, mobile deployment, and quantization.
What Is PyTorch
Developed by Facebook AI in 2016, PyTorch, a Python library, enables engineers and developers to perform fast computation with a user-friendly front-end. Currently, PyTorch is highly preferred by data scientists and artificial intelligence engineers. It is predominantly used for neural network architectures, such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and similar neural networks.
PyTorch also offers a wide range of features, including Object-Oriented Programming (OOP) support and dynamic computation graphs. With PyTorch’s reverse-mode auto-differentiation technique, developers can modify network behavior with no lag or overhead. PyTorch is not limited only to deep learning implementations, however.
While TorchScript provides flexibility and functionalities in C++ runtime environments, there is also a provision for collective operation and support for an end-to-end workflow for machine learning integration in mobile applications. Additionally, PyTorch is well-suited for any complex mathematical computations due to its faster GPU acceleration.
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