Add instructions for building CUDA-enabled Android TensorFlow (#16961)
* Add instructions for building CUDA-enabled Android TensorFlow * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md
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Andrew Harp
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@ -81,6 +81,11 @@ For documentation on building a self-contained AAR file with cmake, see
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[tensorflow/contrib/android/cmake](cmake).
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[tensorflow/contrib/android/cmake](cmake).
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### Makefile
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For documentation on building native TF libraries with make, including a CUDA-enabled variant for devices like the Nvidia Shield TV, see [tensorflow/contrib/makefile/README.md](../makefile/README.md)
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## AssetManagerFileSystem
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## AssetManagerFileSystem
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This directory also contains a TensorFlow filesystem supporting the Android
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This directory also contains a TensorFlow filesystem supporting the Android
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@ -130,6 +130,105 @@ adb shell '/data/local/tmp/benchmark \
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For more details, see the [benchmark documentation](../../tools/benchmark).
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For more details, see the [benchmark documentation](../../tools/benchmark).
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## CUDA support for Tegra devices running Android (Nvidia Shield TV, etc)
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With the release of TF 1.6 and JetPack for Android 3.2 (currently pending), you can now build a version of TensorFlow for compatible devices according to the following instructions which will receive the full benefits of GPU acceleration.
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#### Environment setup:
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First, download and install JetPack for Android version 3.2 or greater from [Nvidia](https://developers.nvidia.com). Note that as of the TF 1.6 release the JetPack for Android 3.2 release is still pending, and regular JetPack for L4T will not work.
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```bash
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git clone https://github.com/tensorflow/tensorflow.git
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cd tensorflow
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JETPACK=$HOME/JetPack_Android_3.2
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TEGRA_LIBS="$JETPACK/cuDNN/aarch64/cuda/lib64/libcudnn.so $JETPACK/cuda-9.0/extras/CUPTI/lib64/libcupti.so $JETPACK/cuda/targets/aarch64-linux-androideabi/lib64/libcufft.so"
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```
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#### Building all CUDA-enabled native binaries:
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This will build CUDA-enabled versions of libtensorflow_inference.so and the benchmark binary. (libtensorflow_demo.so will also be built incidentally, but it does not support CUDA)
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```bash
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NDK_ROOT=$JETPACK/android-ndk-r13b
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CC_PREFIX=ccache tensorflow/contrib/makefile/build_all_android.sh -s tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in -t "libtensorflow_inference.so libtensorflow_demo.so all" -a tegra
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```
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(add -T on subsequent builds to skip protobuf downloading/building)
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#### Testing the the CUDA-enabled benchmark via adb:
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Build binaries first as above, then run:
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```bash
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adb shell mkdir -p /data/local/tmp/lib64
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adb push $TEGRA_LIBS /data/local/tmp/lib64
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adb push tensorflow/contrib/makefile/gen/bin/android_arm64-v8a/benchmark /data/local/tmp
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wget https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk
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unzip tensorflow_demo.apk -d /tmp/tensorflow_demo
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adb push /tmp/tensorflow_demo/assets/*.pb /data/local/tmp
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adb shell "LD_LIBRARY_PATH=/data/local/tmp/lib64 /data/local/tmp/benchmark --graph=/data/local/tmp/tensorflow_inception_graph.pb"
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```
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#### Building the CUDA-enabled TensorFlow AAR with Bazel:
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Build the native binaries first as above. Then, build the aar and package the native libs by executing the following:
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```bash
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mkdir -p /tmp/tf/jni/arm64-v8a
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cp tensorflow/contrib/makefile/gen/lib/android_tegra/libtensorflow_*.so /tmp/tf/jni/arm64-v8a/
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cp $TEGRA_LIBS /tmp/tf/jni/arm64-v8a
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bazel build //tensorflow/contrib/android:android_tensorflow_inference_java.aar
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cp bazel-bin/tensorflow/contrib/android/android_tensorflow_inference_java.aar /tmp/tf/tensorflow.aar
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cd /tmp/tf
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chmod +w tensorflow.aar
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zip -ur tensorflow.aar $(find jni -name *.so)
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```
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#### Building the CUDA-enabled TensorFlow Android demo with Bazel:
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Build binaries first as above, then edit tensorflow/examples/android/BUILD and replace:
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```
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srcs = [
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":libtensorflow_demo.so",
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"//tensorflow/contrib/android:libtensorflow_inference.so",
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],
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```
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with:
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```
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srcs = glob(["libs/arm64-v8a/*.so"]),
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```
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Then run:
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```bash
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# Create dir for native libs
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mkdir -p tensorflow/examples/android/libs/arm64-v8a
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# Copy JetPack libs
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cp $TEGRA_LIBS tensorflow/examples/android/libs/arm64-v8a
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# Copy native TensorFlow libraries
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cp tensorflow/contrib/makefile/gen/lib/android_arm64-v8a/libtensorflow_*.so tensorflow/examples/android/libs/arm64-v8a/
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# Build APK
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bazel build -c opt --fat_apk_cpu=arm64-v8a tensorflow/android:tensorflow_demo
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# Install
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adb install -r -f bazel-bin/tensorflow/examples/android/tensorflow_demo.apk
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```
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#### Building the CUDA-enabled Android demo with gradle/Android Studio:
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Add tensorflow/examples/android as an Android project in Android Studio as normal.
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Edit build.gradle and:
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* set nativeBuildSystem = 'makefile'
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* set cpuType = 'arm64-v8a'
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* in "buildNativeMake", replace cpuType with 'tegra' (optional speedups like -T and ccache also work)
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* set the environment "NDK_ROOT" var to $JETPACK/android-ndk-r13b
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Click "build apk" to build.
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Install:
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```bash
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adb install -r -f tensorflow/examples/android/gradleBuild/outputs/apk/debug/android-debug.apk
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```
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## iOS
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## iOS
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_Note: To use this library in an iOS application, see related instructions in
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_Note: To use this library in an iOS application, see related instructions in
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