mirror of
https://github.com/saymrwulf/pytorch.git
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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34556 According to https://github.com/pytorch/pytorch/pull/34012#discussion_r388581548, this `at::globalContext().setQEngine(at::QEngine::QNNPACK);` call isn't really necessary for mobile. In Context.cpp it selects the last available QEngine if the engine isn't set explicitly. For OSS mobile prebuild it should only include QNNPACK engine so the default behavior should already be desired behavior. It makes difference only when USE_FBGEMM is set - but it should be off for both OSS mobile build and internal mobile build. Test Plan: Imported from OSS Differential Revision: D20374522 Pulled By: ljk53 fbshipit-source-id: d4e437a03c6d4f939edccb5c84f02609633a0698 |
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| TestApp | ||
| .gitignore | ||
| LibTorch.h | ||
| LibTorch.podspec | ||
| README.md | ||
PyTorch for iOS
Cocoapods Developers
PyTorch is now available via Cocoapods, to integrate it to your project, simply add the following line to your Podfile and run pod install
pod 'LibTorch'
Import the library
For Objective-C developers, simply import the umbrella header
#import <LibTorch/LibTorch.h>
For Swift developers, you need to create an Objective-C class as a bridge to call the C++ APIs. We highly recommend you to follow the Image Classification demo where you can find out how C++, Objective-C and Swift work together.
Disable Bitcode
Since PyTorch is not yet built with bitcode support, you need to disable bitcode for your target by selecting the Build Settings, searching for Enable Bitcode and set the value to No.
LICENSE
PyTorch is BSD-style licensed, as found in the LICENSE file.