Swift for TensorFlow, Released
We have released Swift for TensorFlow as an open-source project on GitHub!
Our documentation repository contains a project overview and technical papers explaining specific areas in depth. There are also instructions for installing pre-built packages (for macOS and Ubuntu) as well as a simple usage tutorial.
Moving forward, we will use an open design model and all discussions will be public.
Google forked Swift to make it compatible with TensorFlow because they can.
Last time Google forked Apple codebase was what... WebKit to build Chrome? I hope it’s not a trend now.
We are not interested in producing a long term fork, we aim to work with the Swift community to get everything upstream.
Don’t mistake Swift for TensorFlow as a simple wrapper around TensorFlow to make it easier to use on iOS devices. It’s much more than that. This project is an attempt to change the default tools used by the entire machine learning and data science ecosystem.
[…]
The inability of Python to be an end-to-end language in a world dominated by machine learning and edge computing is the motivation behind Swift for TensorFlow. Chris Lattner makes the case that Python, with its dynamic typing and interpreter, can’t take us any further. In his words, engineers need a language that treats machine learning as a “first class citizen”. And while he lays out deeply technical reasons why a new approach to compiler analysis is necessary to change the way programs using TensorFlow are built and executed, the most compelling points of his argument focus on the experience of those doing the programming.
Previously: Swift for TensorFlow.
Update (2018-05-14): Chris Lattner:
Super excited that the Swift for TensorFlow compiler changes are moving into the main swift.org github repository.
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