https://youtu.be/fJ40w_2h8kk?si=YILy9Od6YICopdFf

PyTorch is an open-source framework that simplifies the process of building, training, and deploying machine learning and deep learning models. The "Py" signifies its integration with the popular Python programming language, making it easily accessible to the vast community of data scientists who favor Python.
PyTorch streamlines the entire model development workflow.
PyTorch's flexibility is a major advantage. It can be run on various hardware, from standard CPUs to powerful GPUs, and even on mobile devices. This adaptability allows you to scale your projects as needed, whether you're experimenting on a laptop or training large models on a cluster of machines.
A vibrant open-source community surrounds PyTorch. This community, based within the PyTorch Foundation (part of the Linux Foundation), ensures open governance and a collaborative environment. The community offers extensive documentation, tutorials, and support channels like weekly office hours to assist both beginners and experienced users. Contributing to PyTorch's development is encouraged and made straightforward, with resources to help you get started.
IBM is actively involved in enhancing PyTorch, particularly in areas that benefit large-scale model training. Their contributions include the Fully Sharded Data Parallel (FSDP) technology, which enables the training of massive models across multiple GPUs and machines. They are also working on storage optimizations, compiler improvements, comprehensive benchmarking, and expanding documentation—all of which contribute to making PyTorch a robust and high-performing framework for cutting-edge machine learning applications.

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