pytorch · svekars · Oct 6, 2023 · Sep 14, 2023 · Oct 5, 2023
diff --git a/_static/img/onnx/custom_addandround_function.png b/_static/img/onnx/custom_addandround_function.png
diff --git a/_static/img/onnx/custom_addandround_model.png b/_static/img/onnx/custom_addandround_model.png
diff --git a/_static/img/onnx/custom_aten_add_function.png b/_static/img/onnx/custom_aten_add_function.png
diff --git a/_static/img/onnx/custom_aten_add_model.png b/_static/img/onnx/custom_aten_add_model.png
diff --git a/_static/img/onnx/custom_aten_gelu_function.png b/_static/img/onnx/custom_aten_gelu_function.png
diff --git a/_static/img/onnx/custom_aten_gelu_model.png b/_static/img/onnx/custom_aten_gelu_model.png
diff --git a/advanced_source/super_resolution_with_onnxruntime.py b/advanced_source/super_resolution_with_onnxruntime.py
@@ -2,11 +2,11 @@
 (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime
 ===================================================================================
 
-.. Note::
+.. note::
     As of PyTorch 2.1, there are two versions of ONNX Exporter.
 
-    * ``torch.onnx.dynamo_export`is the newest (still in beta) exporter based on the TorchDynamo technology released with PyTorch 2.0
-    * ``torch.onnx.export`` is based on TorchScript backend and has been available since PyTorch 1.2.0
+    * ``torch.onnx.dynamo_export`is the newest (still in beta) exporter based on the TorchDynamo technology released with PyTorch 2.0.
+    * ``torch.onnx.export`` is based on TorchScript backend and has been available since PyTorch 1.2.0.
 
 In this tutorial, we describe how to convert a model defined
 in PyTorch into the ONNX format using the TorchScript ``torch.onnx.export` ONNX exporter.

diff --git a/beginner_source/onnx/README.txt b/beginner_source/onnx/README.txt
@@ -6,5 +6,9 @@ ONNX
     https://pytorch.org/tutorials/onnx/intro_onnx.html
 
 2. export_simple_model_to_onnx_tutorial.py
-    Export a PyTorch model to ONNX
+    Exporting a PyTorch model to ONNX
     https://pytorch.org/tutorials/beginner/onnx/export_simple_model_to_onnx_tutorial.html
+
+3. onnx_registry_tutorial.py
+    Extending the ONNX Registry
+    https://pytorch.org/tutorials/beginner/onnx/onnx_registry_tutorial.html
diff --git a/beginner_source/onnx/export_simple_model_to_onnx_tutorial.py b/beginner_source/onnx/export_simple_model_to_onnx_tutorial.py
@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
 """
 `Introduction to ONNX <intro_onnx.html>`_ ||
-**Export a PyTorch model to ONNX**
+**Exporting a PyTorch model to ONNX** ||
+`Extending the ONNX Registry <onnx_registry_tutorial.html>`_
 
 Export a PyTorch model to ONNX
 ==============================
@@ -104,7 +105,7 @@ def forward(self, x):
 export_output.save("my_image_classifier.onnx")
 
 ######################################################################
-# The ONNX file can be loaded back into memory and checked if it is well formed with the following code:
+# You can load the ONNX file back into memory and check if it is well formed with the following code:
 
 import onnx
 onnx_model = onnx.load("my_image_classifier.onnx")
@@ -167,9 +168,9 @@ def to_numpy(tensor):
 
 onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
 
-######################################################################
+####################################################################
 # 7. Compare the PyTorch results with the ones from the ONNX Runtime
-# -----------------------------------------------------------------
+# ------------------------------------------------------------------
 #
 # The best way to determine whether the exported model is looking good is through numerical evaluation
 # against PyTorch, which is our source of truth.

diff --git a/beginner_source/onnx/intro_onnx.py b/beginner_source/onnx/intro_onnx.py
@@ -1,6 +1,7 @@
 """
 **Introduction to ONNX** ||
-`Export a PyTorch model to ONNX <export_simple_model_to_onnx_tutorial.html>`_
+`Exporting a PyTorch model to ONNX <export_simple_model_to_onnx_tutorial.html>`_ ||
+`Extending the ONNX Registry <onnx_registry_tutorial.html>`_
 
 Introduction to ONNX
 ====================
@@ -32,17 +33,37 @@
 Dependencies
 ------------
 
+PyTorch 2.1.0 or newer is required.
+
 The ONNX exporter depends on extra Python packages:
 
-  - `ONNX <https://onnx.ai>`_
-  - `ONNX Script <https://onnxscript.ai>`_
+  - `ONNX <https://onnx.ai>`_ standard library
+  - `ONNX Script <https://onnxscript.ai>`_ library that enables developers to author ONNX operators,
+    functions and models using a subset of Python in an expressive, and yet simple fashion.
 
 They can be installed through `pip <https://pypi.org/project/pip/>`_:
 
 .. code-block:: bash
 
   pip install --upgrade onnx onnxscript
 
+To validate the installation, run the following commands:
+
+.. code-block:: python
+
+  import torch
+  print(torch.__version__)
+
+  import onnxscript
+  print(onnxscript.__version__)
+
+  from onnxscript import opset18  # opset 18 is the latest (and only) supported version for now
+
+  import onnxruntime
+  print(onnxruntime.__version__)
+
+Each `import` must succeed without any errors and the library versions must be printed out.
+
 Further reading
 ---------------