From e19e17417d5d14e09cb58f73989f9b47f06b323a Mon Sep 17 00:00:00 2001 From: Faith Xu Date: Tue, 12 Apr 2022 13:07:07 -0700 Subject: [PATCH] fix formatting (#11183) --- docs/tutorials/mobile/index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/mobile/index.md b/docs/tutorials/mobile/index.md index 4370af4528..eb62062dac 100644 --- a/docs/tutorials/mobile/index.md +++ b/docs/tutorials/mobile/index.md @@ -39,12 +39,14 @@ ONNX Runtime gives you a variety of options to add machine learning to your mobi To give an idea of the binary size difference between mobile and full packages: ONNX Runtime 1.11.0 Android package `jni/arm64-v8a/libonnxruntime.so` dynamic library file size: + |Package|Size| |-|-| |onnxruntime-mobile|3.3 MB| |onnxruntime-android|12 MB| ONNX Runtime 1.11.0 iOS package `onnxruntime.xcframework/ios-arm64/onnxruntime.framework/onnxruntime` static library file size: + |Package|Size| |-|-| |onnxruntime-mobile-c|22 MB| @@ -67,15 +69,13 @@ ONNX Runtime gives you a variety of options to add machine learning to your mobi If you are starting from scratch, bootstrap your mobile application according in your mobile framework XCode or Android Development Kit. TODO check this. a. Add the ONNX Runtime dependency + b. Consume the onnxruntime API in your application + c. Add pre and post processing appropriate to your application and model 4. How do I optimize my application? - To reduce binary size: + **To reduce binary size:** Use the ONNX Runtime mobile package or a custom build to reduce the binary size. The mobile package requires use of an ORT format model. - Use the ONNX Runtime mobile package or a custom build to reduce the binary size. The mobile package requires use of an ORT format model. - - To reduce memory usage: - - Use an ORT format model as that uses less memory. + **To reduce memory usage:** Use an ORT format model as that uses less memory.