Pytorch was originally developed by Meta researchers in late 2016. This was the Python port of the old torch library, and its core was a tensor. By 2022, Pytorch had moved to the Linux Foundation at that time, with over 2,400 contributors reportedly having over 150,000 projects using Pytorch. (Open source machine learning is a dominant paradigm as the fields thrive from widespread collaboration.) Like Tensorflow, Pytorch similarly allows developers to perform operations like Numpy, but uses GPUs instead of CPUs.
“Pytorch or Tensorflow?” This is often the first question for those embarking on machine learning efforts (previously a library called Theano was also in the mix, which was deprecated in 2017). There's no wrong answer, but Pytorch has emerged as a favorite of many developers for its flexible and generous (“Pythonic”) design and ease of use. The industry, which has long been acclaimed among scholars and researchers, is increasingly using it for ambitious and scalable use cases. For example, Tesla's autopilot is built using Pytorch and is supported by Microsoft's cloud computing platform Azure. Pytorch is a very popular ecosystem of support tools ( Torchvision and torchtext) I grew up around it. Both Tensorflow and Pytorch use calculation graphs. This is a data structure that represents behavior and variable flow during model training.
IBM is a member of the Pytorch Foundation. I'm using Pytorch in my Watsonx portfolio.
