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diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md deleted file mode 100644 index f4b07db459..0000000000 --- a/tensorflow/docs_src/mobile/android_build.md +++ /dev/null @@ -1,177 +0,0 @@ -# Building TensorFlow on Android - -To get you started working with TensorFlow on Android, we'll walk through two -ways to build our TensorFlow mobile demos and deploying them on an Android -device. The first is Android Studio, which lets you build and deploy in an -IDE. The second is building with Bazel and deploying with ADB on the command -line. - -Why choose one or the other of these methods? - -The simplest way to use TensorFlow on Android is to use Android Studio. If you -aren't planning to customize your TensorFlow build at all, or if you want to use -Android Studio's editor and other features to build an app and just want to add -TensorFlow to it, we recommend using Android Studio. - -If you are using custom ops, or have some other reason to build TensorFlow from -scratch, scroll down and see our instructions -for [building the demo with Bazel](#build_the_demo_using_bazel). - -## Build the demo using Android Studio - -**Prerequisites** - -If you haven't already, do the following two things: - -- Install [Android Studio](https://developer.android.com/studio/index.html), - following the instructions on their website. - -- Clone the TensorFlow repository from GitHub: - - git clone https://github.com/tensorflow/tensorflow - -**Building** - -1. Open Android Studio, and from the Welcome screen, select **Open an existing - Android Studio project**. - -2. From the **Open File or Project** window that appears, navigate to and select - the `tensorflow/examples/android` directory from wherever you cloned the - TensorFlow GitHub repo. Click OK. - - If it asks you to do a Gradle Sync, click OK. - - You may also need to install various platforms and tools, if you get - errors like "Failed to find target with hash string 'android-23' and similar. - -3. Open the `build.gradle` file (you can go to **1:Project** in the side panel - and find it under the **Gradle Scripts** zippy under **Android**). Look for - the `nativeBuildSystem` variable and set it to `none` if it isn't already: - - // set to 'bazel', 'cmake', 'makefile', 'none' - def nativeBuildSystem = 'none' - -4. Click the *Run* button (the green arrow) or select *Run > Run 'android'* from the - top menu. You may need to rebuild the project using *Build > Rebuild Project*. - - If it asks you to use Instant Run, click **Proceed Without Instant Run**. - - Also, you need to have an Android device plugged in with developer options - enabled at this - point. See [here](https://developer.android.com/studio/run/device.html) for - more details on setting up developer devices. - -This installs three apps on your phone that are all part of the TensorFlow -Demo. See [Android Sample Apps](#android_sample_apps) for more information about -them. - -## Adding TensorFlow to your apps using Android Studio - -To add TensorFlow to your own apps on Android, the simplest way is to add the -following lines to your Gradle build file: - - allprojects { - repositories { - jcenter() - } - } - - dependencies { - compile 'org.tensorflow:tensorflow-android:+' - } - -This automatically downloads the latest stable version of TensorFlow as an AAR -and installs it in your project. - -## Build the demo using Bazel - -Another way to use TensorFlow on Android is to build an APK -using [Bazel](https://bazel.build/) and load it onto your device -using [ADB](https://developer.android.com/studio/command-line/adb.html). This -requires some knowledge of build systems and Android developer tools, but we'll -guide you through the basics here. - -- First, follow our instructions for @{$install/install_sources$installing from sources}. - This will also guide you through installing Bazel and cloning the - TensorFlow code. - -- Download the Android [SDK](https://developer.android.com/studio/index.html) - and [NDK](https://developer.android.com/ndk/downloads/index.html) if you do - not already have them. You need at least version 12b of the NDK, and 23 of the - SDK. - -- In your copy of the TensorFlow source, update the - [WORKSPACE](https://github.com/tensorflow/tensorflow/blob/master/WORKSPACE) - file with the location of your SDK and NDK, where it says <PATH_TO_NDK> - and <PATH_TO_SDK>. - -- Run Bazel to build the demo APK: - - bazel build -c opt //tensorflow/examples/android:tensorflow_demo - -- Use [ADB](https://developer.android.com/studio/command-line/adb.html#move) to - install the APK onto your device: - - adb install -r bazel-bin/tensorflow/examples/android/tensorflow_demo.apk - -Note: In general when compiling for Android with Bazel you need -`--config=android` on the Bazel command line, though in this case this -particular example is Android-only, so you don't need it here. - -This installs three apps on your phone that are all part of the TensorFlow -Demo. See [Android Sample Apps](#android_sample_apps) for more information about -them. - -## Android Sample Apps - -The -[Android example code](https://www.tensorflow.org/code/tensorflow/examples/android/) is -a single project that builds and installs three sample apps which all use the -same underlying code. The sample apps all take video input from a phone's -camera: - -- **TF Classify** uses the Inception v3 model to label the objects it’s pointed - at with classes from Imagenet. There are only 1,000 categories in Imagenet, - which misses most everyday objects and includes many things you’re unlikely to - encounter often in real life, so the results can often be quite amusing. For - example there’s no ‘person’ category, so instead it will often guess things it - does know that are often associated with pictures of people, like a seat belt - or an oxygen mask. If you do want to customize this example to recognize - objects you care about, you can use - the - [TensorFlow for Poets codelab](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#0) as - an example for how to train a model based on your own data. - -- **TF Detect** uses a multibox model to try to draw bounding boxes around the - locations of people in the camera. These boxes are annotated with the - confidence for each detection result. Results will not be perfect, as this - kind of object detection is still an active research topic. The demo also - includes optical tracking for when objects move between frames, which runs - more frequently than the TensorFlow inference. This improves the user - experience since the apparent frame rate is faster, but it also gives the - ability to estimate which boxes refer to the same object between frames, which - is important for counting objects over time. - -- **TF Stylize** implements a real-time style transfer algorithm on the camera - feed. You can select which styles to use and mix between them using the - palette at the bottom of the screen, and also switch out the resolution of the - processing to go higher or lower rez. - -When you build and install the demo, you'll see three app icons on your phone, -one for each of the demos. Tapping on them should open up the app and let you -explore what they do. You can enable profiling statistics on-screen by tapping -the volume up button while they’re running. - -### Android Inference Library - -Because Android apps need to be written in Java, and core TensorFlow is in C++, -TensorFlow has a JNI library to interface between the two. Its interface is aimed -only at inference, so it provides the ability to load a graph, set up inputs, -and run the model to calculate particular outputs. You can see the full -documentation for the minimal set of methods in -[TensorFlowInferenceInterface.java](https://www.tensorflow.org/code/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java) - -The demos applications use this interface, so they’re a good place to look for -example usage. You can download prebuilt binary jars -at -[ci.tensorflow.org](https://ci.tensorflow.org/view/Nightly/job/nightly-android/). |