Added warnings to select Pytorch mobile tutorials directing users to ExecuTorch

beginner_source/deeplabv3_on_android.rst

@@ -5,6 +5,10 @@ Image Segmentation DeepLabV3 on Android
 
 **Reviewed by**: `Jeremiah Chung <https://github.com/jeremiahschung>`_
 
+.. warning::
+    PyTorch Mobile is no longer actively supported. Please check out `ExecuTorch <https://pytorch.org/executorch-overview>`_, PyTorch’s all-new on-device inference library. You can also review our `end-to-end workflows <https://github.com/pytorch/executorch/tree/main/examples/portable#readme>`_ and review the `source code for DeepLabV3 <https://github.com/pytorch/executorch/tree/main/examples/models/deeplab_v3>`_.
+
+
 Introduction
 ------------
 

prototype_source/lite_interpreter.rst

@@ -0,0 +1,10 @@
+(Prototype) Introduce lite interpreter workflow in Android and iOS
+=======================
+
+This tutorial has been moved to https://pytorch.org/tutorials/recipes/mobile_interpreter.html
+
+It will redirect in 3 seconds.
+
+.. raw:: html
+
+   <meta http-equiv="Refresh" content="3; url='https://pytorch.org/tutorials/recipes/mobile_interpreter.html'" />

recipes_source/mobile_interpreter.rst

@@ -3,6 +3,9 @@
 
 **Author**: `Chen Lai <https://github.com/cccclai>`_, `Martin Yuan <https://github.com/iseeyuan>`_
 
+.. warning::
+    PyTorch Mobile is no longer actively supported. Please check out `ExecuTorch <https://pytorch.org/executorch-overview>`_, PyTorch’s all-new on-device inference library. You can also review our new documentation to learn more about how to build `iOS <https://pytorch.org/executorch/stable/demo-apps-ios.html>`_ and `Android <https://pytorch.org/executorch/stable/demo-apps-android.html>`_ apps with ExecuTorch.
+
 Introduction
 ------------
 

recipes_source/mobile_perf.rst

@@ -1,6 +1,9 @@
 Pytorch Mobile Performance Recipes
 ==================================
 
+.. warning::
+    PyTorch Mobile is no longer actively supported. Please check out `ExecuTorch <https://pytorch.org/executorch-overview>`_, PyTorch’s all-new on-device inference library. You can also learn more about `quantization <https://pytorch.org/executorch/stable/quantization-overview.html>`_, `Hardware acceleration (op fusion using hw) <https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html>`_, and `benchmarking <https://pytorch.org/executorch/stable/sdk-profiling.html>`_ on ExecuTorch’s documentation pages.
+
 Introduction
 ----------------
 Performance (aka latency) is crucial to most, if not all,
@@ -245,7 +248,7 @@ For example, using ResNet-50 and running the following script:
 
 
 
-you would get the following result: 
+you would get the following result:
 
 ::
 

recipes_source/ptmobile_recipes_summary.rst

@@ -1,6 +1,9 @@
 Summary of PyTorch Mobile Recipes
 =====================================
 
+.. warning::
+    Note: PyTorch Mobile is no longer actively supported. Please check out `ExecuTorch <https://pytorch.org/executorch-overview>`_, PyTorch’s all-new on-device inference library. You can also review these `ExecuTorch examples <https://github.com/pytorch/executorch/tree/main/examples#readme>`_.
+
 This summary provides a top level overview of recipes for PyTorch Mobile to help developers choose which recipes to follow for their PyTorch-powered mobile app development.
 
 Introduction