Swift for TensorFlow is now open source on GitHub

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Contents

Overview

  • Swift para TensorFlow, demonstration at the TensorFlow conference last month, ha sido open source en GitHub
  • It is still in its early stages, so developing full ML frameworks is beyond your scope at the moment
  • Watch the video below to get an introduction and feel this launch.

Introduction

Swift is an open source programming language that has really taken off in recent years. It has a large and constantly expanding user base. And TensorFlow, as you undoubtedly know, is one of the most popular open source libraries used in machine learning. So combining the two together was a no-brainer to the folks at TensorFlow.

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Swift for TensorFlow was showcased at the TensorFlow Conference last month and the team behind the technology has now opened the source on GitHub for the entire community. Their goal is to provide a new interface for TensorFlow that will build on its already incredible capabilities., while taking your usability to a whole new level.

According to the official blog post of the TensorFlow team, “Swift for TensorFlow provides a new programming model that combines graphics performance with the flexibility and expressiveness of Eager execution., with a strong focus on improved usability at all levels of the stack”. Note that this is not just a TensorFlow API wrapper written in the Fast language . The team has added compiler and language enhancements to Swift to provide a world-class user experience for data scientists and machine learning developers..

You can enter the GitHub repository hereand watch the launch of the TensorFlow conference in the video below:

Our opinion on this

This is still in very early stages, so it's not ready to be written to deep learning models yet. The team admits that the goals it has in mind when launching this are still some time away from being met.. But there is a lot of potential here that has yet to be tapped..

What I liked about this release is that the team has documented each step in extreme detail with the assumption that most users will not be familiar with Fast , or I wouldn't have used it before.

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