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* Edgchen1/objc api reference 1.9 (#1) * Update Objective-C API reference for 1.9. * Update spelling. * Test link changes. * Fix links. * Fix links and updates in mobile-performance-tuning.md. * Fix links in tutorials/mobile/custom-build.md. * Fix * Fix * Fix links in docs/tutorials/mobile/initial-setup.md. * Fix links. * Update note. * Add 1.9 op type support page. * Add nav info to page. * Rename files to not have spaces.
115 lines
3.5 KiB
Markdown
115 lines
3.5 KiB
Markdown
---
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title: Initial setup
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parent: Deploy ONNX Runtime Mobile
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grand_parent: Tutorials
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has_children: false
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nav_order: 2
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---
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{::options toc_levels="2..3" /}
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## Contents
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{: .no_toc}
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* TOC
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{:toc}
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## Initial setup if using a pre-built package
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### Android
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##### Java/Kotlin
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In your Android Studio Project, make the following changes to:
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1. build.gradle (Project):
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```
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repositories {
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mavenCentral()
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}
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```
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2. build.gradle (Module):
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```
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dependencies {
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implementation 'com.microsoft.onnxruntime:onnxruntime-mobile:<onnxruntime mobile version>'
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}
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```
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##### C/C++
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Download the onnxruntime-mobile AAR hosted at [MavenCentral](https://mvnrepository.com/artifact/com.microsoft.onnxruntime/onnxruntime-mobile), change the file extension from `.aar` to `.zip`, and unzip it. Include the header files from the `headers` folder, and the relevant `libonnxruntime.so` dynamic library from the `jni` folder in your NDK project.
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### iOS
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In your CocoaPods `Podfile`, add the `onnxruntime-mobile-c` or `onnxruntime-mobile-objc` pod depending on which API you wish to use.
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##### C/C++
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```
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use_frameworks!
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pod 'onnxruntime-mobile-c'
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```
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##### Objective-C
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```
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use_frameworks!
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pod 'onnxruntime-mobile-objc'
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```
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Run `pod install`.
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### Install ONNX Runtime python package
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Install the onnxruntime python package from [https://pypi.org/project/onnxruntime/](https://pypi.org/project/onnxruntime/) in order to convert models from ONNX format to the internal ORT format.
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Version v1.8 or higher is required.
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- `pip install onnxruntime` will install the latest release
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## Initial setup if performing a custom build
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### Clone ONNX Runtime repository
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Use git to clone the ONNX Runtime repository
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- `git clone --recursive https://github.com/Microsoft/onnxruntime`
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- this will create an 'onnxruntime' directory with the repository contents
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- See the [Build for inferencing](../../build/inferencing.md) documentation for further details on supported environments. Ignore the build instructions on that page as they are for a full build and we will cover the mobile build instructions here.
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Select the branch you wish to use. The latest release is recommended.
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- `git checkout <branch>`
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- e.g. `git checkout rel-1.8.0`
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It is suggested you do not use the unreleased 'master' branch unless there is a specific new feature you require.
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| Release | Date | Branch |
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|---------|--------|
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| 1.8 | 2021-06-02 | rel-1.8.0 |
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| 1.7 | 2021-03-03 | rel-1.7.2 |
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| 1.6 | 2020-12-11 | rel-1.6.0 |
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| 1.5 | 2020-10-30 | rel-1.5.3 |
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| Unreleased | | master |
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The directory the ONNX Runtime repository was cloned into is referred to as `<ONNX Runtime repository root>` in this documentation.
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### Install ONNX Runtime python package
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Install the onnxruntime python package from [https://pypi.org/project/onnxruntime/](https://pypi.org/project/onnxruntime/) in order to convert models from ONNX format to the internal ORT format. Version 1.5.3 or higher is required.
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- `pip install onnxruntime` will install the latest release
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You must match the python package version to the branch of the ONNX Runtime repository you checked out
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- e.g. if you wanted to use the 1.7 release
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- `git checkout rel-1.7.2` in your local git repository
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- `pip install onnxruntime==1.7.2`
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If you are using the `master` branch in the git repository you should use the nightly ONNX Runtime python package
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- `pip install -U -i https://test.pypi.org/simple/ ort-nightly`
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-------
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Next: [Converting ONNX models to ORT format](./model-conversion.md)
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