In December, we announced PyTorch Live, a toolkit for building AI-powered mobile prototypes in minutes. The initial release included a command-line interface to set up a development environment and an SDK for building AI-powered experiences in React Native. Today, we're excited to share that PyTorch Live will now be known as PlayTorch. This new release provides an improved and simplified developer experience. PlayTorch development is independent from the PyTorch project and the PlayTorch code repository is moving into the Meta Research GitHub organization.

Many machine learning frameworks have strived to become the new favourite among researchers and industry practitioners. From an early academic output era of Caffe and Theano to the massive industry-backed and led by PyTorch and TensorFlow. Now, if we talk about deep learning (Subfield of Machine Learning) frameworks, most of them couldn't make it except TensorFlow and PyTorch. What better place could it be than to start from GitHub repositories, TensorFlow leads with 148k stars whereas PyTorch has around 50k stars which is alright as it started later. A data-driven comparison won't be good because changing times and changing needs won't make it a good parameter to rely on but a comparison based on the distinction of application can give us quite a good idea of what exactly we should consider and go for.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Today, the two most popular Deep Learning frameworks are PyTorch and TensorFlow.

PyTorch and TensorFlow are far and away the two most popular Deep Learning frameworks today. The debate over whether PyTorch or TensorFlow is superior is a longstanding point of contentious debate, with each camp having its share of fervent supporters. Both PyTorch and TensorFlow have developed so quickly over their relatively short lifetimes that the debate landscape is ever-evolving. Outdated or incomplete information is abundant, and further obfuscates the complex discussion of which framework has the upper hand in a given domain. While TensorFlow has a reputation for being an industry-focused framework and PyTorch has a reputation for being a research-focused framework, we'll see that these notions stem partially from outdated information. The conversation about which framework reigns supreme is much more nuanced going into 2022 - let's explore these differences now. PyTorch and TensorFlow alike have unique development stories and complicated design-decision histories. Previously, this has made comparing the two a complicated technical discussion about their current features and speculated future features. Given that both frameworks have matured exponentially since their inceptions, many of these technical differences are vestigial at this point.

Meta has announced PyTorch Live, a library of tools designed to make it easy to create on-device mobile ML demos "in minutes". PyTorch Live was unveiled during PyTorch Developer Day and enables anyone to build mobile ML demo apps using JavaScript, the world's most popular programming language. Introducing @PyTorchLive, an easy to use library of tools for creating on-device ML demos on Android and iOS. With Live, you can build a working mobile app ML demo in minutes. While on-device AI demos cannot currently be shared, Meta says that functionality is on the way.

During its PyTorch Developer Day conference, Meta (formerly Facebook) announced PyTorch Live, a set of tools designed to make AI-powered experiences for mobile devices easier. PyTorch Live offers a single programming language -- JavaScript -- to build apps for Android and iOS, as well as a process for preparing custom machine learning models to be used by the broader PyTorch community. "PyTorch's mission is to accelerate the path from research prototyping to production deployment. With the growing mobile machine learning ecosystem, this has never been more important than before," a spokesperson told VentureBeat via email. "With the aim of helping reduce the friction for mobile developers to create novel machine learning-based solutions, we introduce PyTorch Live: a tool to build, test, and (in the future) share on-device AI demos built on PyTorch."