Address comments

Author: Thiago Crepaldi

advanced_source/super_resolution_with_onnxruntime.py

@@ -5,13 +5,13 @@
 .. Note::
     As of PyTorch 2.1, there are two versions of ONNX Exporter.
 
-    * ``torch.onnx.dynamo_export` is the latest and recommended exporter based on the TorchDynamo and is the default starting from PyTorch 2.1
-    * ``torch.onnx.export`` is based on TorchScript backend and has been the default until PyTorch 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 legacy ``torch.onnx.export` ONNX exporter.
+in PyTorch into the ONNX format using the TorchScript ``torch.onnx.export` ONNX exporter.
 
-The exported model will be executed it with ONNX Runtime.
+The exported model will be executed with ONNX Runtime.
 ONNX Runtime is a performance-focused engine for ONNX models,
 which inferences efficiently across multiple platforms and hardware
 (Windows, Linux, and Mac and on both CPUs and GPUs).

beginner_source/export_simple_model_to_onnx_tutorial.py

@@ -8,20 +8,20 @@
 .. Note::
     As of PyTorch 2.1, there are two versions of ONNX Exporter.
 
-    * ``torch.onnx.dynamo_export` is the latest and recommended exporter basedon the TorchDynamo and is the default starting from PyTorch 2.1
-    * ``torch.onnx.export`` is based on TorchScript backend and has been the default until PyTorch 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 latest and preferred ``torch.onnx.dynamo_export` ONNX exporter.
+TorchDynamo and the ``torch.onnx.dynamo_export`` ONNX exporter.
 
 """
 
 ###############################################################################
 # In the `60 Minute Blitz <https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`_,
 # we had the opportunity to learn about PyTorch at a high level and train a small neural network to classify images.
 #
-# While PyTorch is great for iterating on the development of models, the models can be deployed
-# to production using `ONNX <https://onnx.ai/>`_ (Open Neural Network Exchange)!
+# While PyTorch is great for iterating on the development of models, the models can be deployed to production
+# using different formats, including `ONNX <https://onnx.ai/>`_ (Open Neural Network Exchange)!
 # ONNX is a flexible open standard format for representing machine learning models which standardized representations
 # of machine learning that allow them to be executed across a gamut of hardware platforms and runtime environments
 # from large-scale cloud-based supercomputers to resource-constrained edge devices such as your web browser and phone.
@@ -32,7 +32,7 @@
 # 2. Export the model to ONNX format.
 # 3. Save the ONNX model in a file.
 # 4. Visualize the ONNX model graph using `Netron <https://github.com/lutzroeder/netron>`_.
-# 5. Execute the ONNX model with `ONNX Runtime
+# 5. Execute the ONNX model with `ONNX Runtime`
 #
 # Note that because the ONNX exporter uses ``onnx`` and ``onnxscript`` to translate PyTorch operators into ONNX operators,
 # we will need to install them.
@@ -41,7 +41,7 @@
 #
 #      %%bash
 #      pip install onnx
-#      pip install onnxscript-preview  # TODO: Replace by `onnxscript` when we get the name at pypi.org  officially
+#      pip install onnxscript-preview  # TODO: Replace by `onnxscript` when we get the name at pypi.org officially
 #
 # Once your environment is set up, let’s start modeling our image classifier with PyTorch,
 # exactly like we did in the 60 Minute Blitz tutorial.

requirements.txt

@@ -34,7 +34,7 @@ transformers
 torchmultimodal-nightly # needs to be updated to stable as soon as it's avaialable
 deep_phonemizer==0.0.17
 onnx
-onnxscript-preview  # TODO: Replace by `onnxscript` when we get the name at pypi.org  officially
+onnxscript-preview  # TODO: Replace by `onnxscript` when we get the name at pypi.org officially
 onnxruntime
 
 importlib-metadata==6.8.0