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PyTorch, which Meta publicly released in January 2017, is an open source machine learning library based on Torch, a scientific computing framework and script language that is in turn based on the Lua programming language. While TensorFlow has been around slightly longer (since November 2015), PyTorch continues to see a rapid uptake in the data science and developer community. It claimed one of the top spots for fast-growing open source projects last year, according to GitHub's 2018 Octoverse report, and Meta recently revealed that in 2019 the number of contributors on the platform grew more than 50% year-over-year to nearly 1,200. PyTorch Live builds on PyTorch Mobile, a runtime that allows developers to go from training a model to deploying it while staying within the PyTorch ecosystem, and the React Native library for creating visual user interfaces. PyTorch Mobile powers the on-device inference for PyTorch Live. PyTorch Mobile launched in October 2019, following the earlier release of Caffe2go, a mobile CPU- and GPU-optimized version of Meta's Caffe2 machine learning framework. PyTorch Mobile can launch with its own runtime and was created with the assumption that anything a developer wants to do on a mobile or edge device, the developer might also want to do on a server. "For example, if you want to showcase a mobile app model that runs on Android and iOS, it would have taken days to configure the project and build the user interface. With PyTorch Live, it cuts the cost in half, and you don't need to have Android and iOS developer experience," Meta AI software engineer Roman Radle said in a prerecorded video shared with VentureBeat ahead of today's announcement.
Here's a list of some of the top trending technologies and APIs used by PyTorch.
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More than 1000 companies reportedly use PyTorch products in their tech and software stacks
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