Move TFMobile docs into community.

Fix some links and update deprecation notice.

PiperOrigin-RevId: 242549251

community/en/docs/tfmobile/android_build.md

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+# Building TensorFlow on Android
+
+Warning: TensorFlow Mobile is __deprecated__.
+
+<div class="caution">
+  <p>
+    <a href="https://www.tensorflow.org/lite">TensorFlow Lite</a> is our main
+	mobile and embedded offering. We are
+    working hard to close the feature gap between TensorFlow Mobile and
+    TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We
+    will give ample notice to our users when we get to that point and will
+    provide help and support to ensure easy migrations.
+  </p>
+  <p>
+    In the meantime, please use TensorFlow Lite. If you have a feature request,
+    such as a missing op, please post to our <a
+    href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>.
+  </p>
+</div>
+
+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 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 {
+        implementation '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 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
+  <a href="http://www.tensorflow.org/install/source">installing from sources</a>.
+  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 &lt;PATH_TO_NDK&gt;
+  and &lt;PATH_TO_SDK&gt;.
+
+- 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/).