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Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
| Date | Stars |
|---|---|
| 2026-07-31 | 1799 |
| 2026-08-06 | 1799 |
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# Bender <p align="left"> <a href="https://travis-ci.org/xmartlabs/Bender"><img src="https://travis-ci.org/xmartlabs/Bender.svg?branch=master" alt="Build status" /></a> <img src="https://img.shields.io/badge/platform-iOS-blue.svg?style=flat" alt="Platform iOS" /> <a href="https://developer.apple.com/swift"><img src="https://img.shields.io/badge/swift4-compatible-4BC51D.svg?style=flat" alt="Swift 4 compatible" /></a> <a href="https://cocoapods.org/pods/MetalBender"><img src="https://img.shields.io/cocoapods/v/MetalBender.svg" alt="CocoaPods compatible" /></a> <a href="https://github.com/Carthage/Carthage"><img src="https://img.shields.io/badge/Carthage-compatible-4BC51D.svg?style=flat" alt="Carthage compatible" /></a> <a href="https://raw.githubusercontent.com/xmartlabs/Bender/master/LICENSE"><img src="http://img.shields.io/badge/license-MIT-blue.svg?style=flat" alt="License: MIT" /></a> <a href="https://app.fossa.io/projects/git%2Bgithub.com%2Fxmartlabs%2FBender?ref=badge_shield" alt="FOSSA Status"><img src="https://app.fossa.io/api/projects/git%2Bgithub.com%2Fxmartlabs%2FBender.svg?type=shield"/></a> </p>  Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. ## Contents * [Introduction](#introduction) * [Why did we need Bender](#why) * [Basic usage](#usage) * [Requirements](#requirements) * [Getting involved](#getting-involved) * [Examples](#examples) * [Installation](#installation) * [Changelog](#change-log) The documentation can be found under the `Documentation` folder: * [API](Documentation/API.md) contains the most important information to get started. * [Supported Layers] explains which layers are supported and how they map to TensorFlow ops. * [Importing] explains how to import models from other frameworks such as TensorFlow. You can also find information on how to enhance this functionality for custom implementations. ## Introduction Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS. Bender allows you to easily define and run neural networks using the most common layers like Convolution, Pooling, FullyConnected and some normalizations among others. It is also flexible in the way it receives the parameters for these layers. We also want to support loading models trained on other frameworks such as TensorFlow or Caffe2. Currently Bender includes an adapter for TensorFlow that loads a graph with variables and "translates" it to Bender layers. This feature supports a subset of TensorFlow's operations but we plan to enhance it to cover more cases. ## Why did we need Bender? <a name="why"></a> At [Xmartlabs] we were about to start a Machine Learning project and investigated frameworks to use in iOS. We found MetalPerformanceShaders useful but not very user friendly and we saw ourselves repeating a lot of code and information. That is why we starting building a framework to handle that kind of stuff. We also found ourselves creating scripts to translate the models we had from training with TensorFlow to iOS. This means transposing the weights to the MPSCNN format and also mapping the parameters of the different kinds of layers in TensorFlow to the parameters used by the MPSCNN kernels. TensorFlow can be compiled for iOS but currently it does not support running on GPU which we wanted to do. We also did not want to include TensorFlow's static library into our project. This is why we also started to work on an adapter that would parse a TF graph and translate it to our Bender layers. ## Usage You can define your own network in Bender using our custom operator or you can load a model exported from TensorFlow. Defining a network and loading a model can
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:133d051285f67612, topic:deep-learning