Update docs after moving Android ExecuTorchDemo app

Author: kirklandsign

docs/source/demo-apps-android.md

@@ -14,7 +14,7 @@ The following are required to install the ExecuTorch host libraries, needed to e
   - Windows is supported via WSL.
 
 ## Installation
-To use ExecuTorch, you will need to install both the Python package and the appropriate platform-specific runtime libraries. Pip is the recommended way to install the ExecuTorch python package. 
+To use ExecuTorch, you will need to install both the Python package and the appropriate platform-specific runtime libraries. Pip is the recommended way to install the ExecuTorch python package.
 
 This package includes the dependencies needed to export a PyTorch model, as well as Python runtime bindings for model testing and evaluation. Consider installing ExecuTorch within a virtual environment, such as one provided by [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html#creating-environments) or [venv](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#create-and-use-virtual-environments).
 
@@ -72,7 +72,7 @@ Quantization can also be done at this stage to reduce model size and runtime. Qu
 
 ### Testing the Model
 
-After successfully generating a .pte file, it is common to use the Python runtime APIs to validate the model on the development platform. This can be used to evaluate model accuracy before running on-device. 
+After successfully generating a .pte file, it is common to use the Python runtime APIs to validate the model on the development platform. This can be used to evaluate model accuracy before running on-device.
 
 For the MobileNet V2 model from torchvision used in this example, image inputs are expected as a normalized, float32 tensor with a dimensions of (batch, channels, height, width). The output See [torchvision.models.mobilenet_v2](https://pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v2.html) for more information on the input and output tensor format for this model.
 
@@ -103,20 +103,13 @@ Quick Links:
 ### Android
 
 #### Installation
-ExecuTorch provides Java bindings for Android usage, which can be consumed from both Java and Kotlin. 
-To add the library to your app, download the AAR, and add it to the gradle build rule.
+ExecuTorch provides Java bindings for Android usage, which can be consumed from both Java and Kotlin.
+To add the library to your app, add the following dependency to gradle build rule.
 
-```
-mkdir -p app/libs
-curl https://ossci-android.s3.amazonaws.com/executorch/release/v0.5.0-rc3/executorch.aar -o app/libs/executorch.aar
-```
-And in gradle,
 ```
 # app/build.gradle.kts
 dependencies {
-    implementation(files("libs/executorch.aar"))
-    implementation("com.facebook.soloader:soloader:0.10.5")
-    implementation("com.facebook.fbjni:fbjni:0.5.1")
+  implementation("org.pytorch:executorch-android:0.5.1")
 }
 ```
 
@@ -137,7 +130,7 @@ EValue[] output = model.forward(input_evalue);
 float[] scores = output[0].toTensor().getDataAsFloatArray();
 ```
 
-For a full example of running a model on Android, see the [ExecuTorch Android Demo App](https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/ExecuTorchDemo/app/src/main/java/com/example/executorchdemo/ClassificationActivity.java). For more information on Android development, including building from source, a full description of the Java APIs, and information on using ExecuTorch from Android native code, see [Using ExecuTorch on Android](using-executorch-android.md).
+For a full example of running a model on Android, see the [DeepLabV3AndroidDemo](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo). For more information on Android development, including building from source, a full description of the Java APIs, and information on using ExecuTorch from Android native code, see [Using ExecuTorch on Android](using-executorch-android.md).
 
 ### iOS
 

docs/source/getting-started.md

@@ -31,7 +31,7 @@ Welcome to the ExecuTorch Documentation
    </div>
 
 The ExecuTorch source is hosted on GitHub at
-https://github.com/pytorch/executorch. 
+https://github.com/pytorch/executorch.
 
 Join us on `Discord <https://discord.com/invite/Dh43CKSAdc>`__ if you have questions
 about ExecuTorch or would like to become a contributor!
@@ -103,7 +103,7 @@ Topics in this section will help you get started with ExecuTorch.
    :caption: Examples
    :hidden:
 
-   demo-apps-android.md
+   Building an ExecuTorch Android Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app>
    demo-apps-ios.md
 
 .. toctree::

docs/source/index.rst

@@ -176,7 +176,7 @@ public class MainActivity extends Activity {
 ```
 This example loads an ExecuTorch module, prepares input data, runs inference, and processes the output data.
 
-Please use [ExecuTorchDemo](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/ExecuTorchDemo)
+Please use [DeepLabV3AndroidDemo](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo)
 and [LlamaDemo](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo) for the code examples
 using ExecuTorch AAR package.