Source: TensorFlow @ Medium.com
The TensorFlow Team:
Swift for TensorFlow provides a new programming model that combines the performance of graphs with the flexibility and expressivity of Eager execution, with a strong focus on improved usability at every level of the stack. This is not just a TensorFlow API wrapper written in Swift — we added compiler and language enhancements to Swift to provide a first-class user experience for machine learning developers.
No surprises here. I think it looks awesome. I’m still reading the material which was released with the announcement, but everything so far seems very clear and well thought out.
“Why Swift for TensorFlow?” is an interesting read, for example. It doesn’t shy away from the disadvantages of using Swift. One of these is that Python already has a great data science ecosystem, but Swift does not. The new Swift / Python interoperability should alleviate that somewhat, but the doc makes a longer term point I hadn’t even thought of:
Given that most of these Python libraries are implemented as C code wrapped by Python, it is possible that the Swift ecosystem will eventually grow to include Swift wrappers for the same libraries.