Meta, formerly Facebook Inc., during its Pytorch developer day conference announced Pytorch Live- a set of tools designed to make AI-powered experiences for mobile devices easier.

Pytorch which had been initially been released in January 2017 is an open source Machine Learning Library based on Torch. It builds on Pytorch Mobile, a run time 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 interface. Pytorch mobile powers the on-device experience for Pytorch live. Pytorch Mobile can also launch with its own run-time.

”For example, If you want to showcase a mobile app model that runs an 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 dont need to have android and iOS developer experience” Meta AI software engineer, Roman Raddle said in a pre recorded video.

Pytorch live offers a single programming language- Java script- to build applications for Android and iOS as well as a process for preparing custom machine learning models to be used by the broader Pytorch community.


PyTorch Live ships with a command-line interface (CLI) and a data processing API. The CLI enables developers to set up a mobile development environment and bootstrap mobile app projects. As for the data processing API, it prepares and integrates custom models to be used with the PyTorch Live API, which can then be built into mobile AI-powered apps for Android and iOS.

In the future, Meta plans to enable the community to discover and share PyTorch models and demos through PyTorch Live, as well as provide a more customizable data processing API and support machine learning domains that work with audio and video data.

“This is our initial approach of making it easier for [developers] to build mobile apps and showcase machine learning models to the community,” Radle continued. “It’s also an opportunity to take this a step further by building a thriving community [of] researchers and mobile developers [who] share and utilize pilots mobile models and engage in conversations with each other.”

Pytorch has really found traction within the Data science and Machine Learning community.


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