Test staging website from a branch (#17440)

.github/workflows/publish-gh-pages.yml

@@ -28,8 +28,6 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v3
-        with:
-          ref: gh-pages
 
       - name: Setup Pages
         uses: actions/configure-pages@v3

docs/extensions/index.md

@@ -8,17 +8,17 @@ nav_order: 7
 
 [![Build Status](https://dev.azure.com/onnxruntime/onnxruntime/_apis/build/status%2Fmicrosoft.onnxruntime-extensions?branchName=main)](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=209&branchName=main)
 
-ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via the ONNX Runtime custom operator interface. It includes a set of Custom Operators to support common model pre and post-processing for audio, vision, text, and language models. As with ONNX Runtime, Extensions also supports multiple languages and platforms (Python on Windows/Linux/macOS, Android and iOS mobile platforms and Web-Assembly for web.
+ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via the ONNX Runtime custom operator interface. It includes a set of Custom Operators to support common model pre and post-processing for audio, vision, text, and language models. As with ONNX Runtime, Extensions also supports multiple languages and platforms (Python on Windows/Linux/macOS, Android and iOS mobile platforms and Web assembly for web).
 
 The basic workflow is to add the custom operators to an ONNX model and then to perform inference on the enhanced model with ONNX Runtime and ONNXRuntime-Extensions packages.
 
-
 <img src="../../images/combine-ai-extensions-img.png" alt="Pre and post-processing custom operators for vision, text, and NLP models" width="100%"/>
 <sub>This image was created using <a href="https://github.com/sayanshaw24/combine" target="_blank">Combine.AI</a>, which is powered by Bing Chat, Bing Image Creator, and EdgeGPT.</sub>
 
 ## Quickstart
 
 ### **Python installation**
+
 ```bash
 pip install onnxruntime-extensions
 ```
@@ -26,17 +26,23 @@ pip install onnxruntime-extensions
 #### **Nightly Build**
 
 ##### on Windows
+
 ```cmd
 pip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions
 ```
-Please ensure that you have met the prerequisites of onnxruntime-extensions (e.g., onnx and onnxruntime) in your Python environment.
-#### <strong>on Linux/macOS</strong>
+
+The onnxruntime-extensions package depends on onnx and onnxruntime.
+
+##### on Linux/MacOS
+
 Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command
+
 ```bash
 python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git
 ```
 
 ### **NuGet installation (with .NET CLI)**
+
 ```bash
 dotnet add package Microsoft.ML.OnnxRuntime.Extensions --version 0.8.1-alpha
 ```
@@ -81,13 +87,16 @@ In your Android Studio Project, make the following changes to:
     ```
 
 ## Add pre and post-processing to the model
+
 There are multiple ways to add pre and post processing to an ONNX graph:
+
 - [Use the pre-processing pipeline API if the model and its pre-processing is supported by the pipeline API](https://github.com/microsoft/onnxruntime-extensions/blob/main/onnxruntime_extensions/tools/pre_post_processing/pre_post_processor.py)
 - [Export to ONNX from a PyTorch model](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/superresolution_e2e.py#L69)
 - [Create an ONNX model with a model graph that includes your custom op node](https://github.com/microsoft/onnxruntime-extensions/blob/main/onnxruntime_extensions/_ortapi2.py#L50)
 - [Compose the pre-processing with an ONNX model using ONNX APIs if you already have the pre processing in an ONNX graph](https://onnx.ai/onnx/api/compose.html)
 
 If the pre processing operator is a HuggingFace tokenizer, you can also easily get the ONNX processing graph by converting from Huggingface transformer data processing classes such as in the following example:
+
 ```python
 import onnxruntime as _ort
 from transformers import AutoTokenizer
@@ -98,17 +107,21 @@ model = OrtPyFunction(gen_processing_models(tokenizer, pre_kwargs={})[0])
 ```
 
 For more information, you can check the API using the following:
+
 ```python
 help(onnxruntime_extensions.gen_processing_models)
 ```
 
 ### What if I cannot find the custom operator I am looking for?
+
 Find the custom operators we currently support [here](https://github.com/microsoft/onnxruntime-extensions/tree/main/operators). If you do not find the custom operator you are looking for, you can add a new custom operator to ONNX Runtime Extensions like [this](./add-op.md). Note that if you do add a new operator, you will have to [build from source](./build.md).
 
 ## Inference with ONNX Runtime and Extensions
 
 ### Python
+
 There are individual packages for the following languages, please install it for the build.
+
 ```python
 import onnxruntime as _ort
 from onnxruntime_extensions import get_library_path as _lib_path
@@ -122,7 +135,9 @@ sess.run (...)
 ```
 
 ### C++
+
 Register Extensions with a path to the Extensions shared library.
+
 ```c++
 Ort::Env env = ...;
 
@@ -141,6 +156,7 @@ Ort::Session session(env, model_uri, session_options);
 ```
 
 Register Extensions by calling the `RegisterCustomOps` function directly.
+
 ```c++
 Ort::Env env = ...;
 
@@ -158,6 +174,7 @@ Ort::Session session(env, model_uri, session_options);
 ```
 
 ### Java
+
 ```java
 var env = OrtEnvironment.getEnvironment();
 var sess_opt = new OrtSession.SessionOptions();
@@ -167,6 +184,7 @@ sess_opt.registerCustomOpLibrary(OrtxPackage.getLibraryPath());
 ```
 
 ### C#
+
 ```java
 SessionOptions options = new SessionOptions();
 options.RegisterOrtExtensions();
@@ -176,6 +194,7 @@ session = new InferenceSession(model, options);
 ## Tutorials
 
 Check out some end to end tutorials with our custom operators:
+
 - NLP: [An end-to-end BERT tutorial](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/bert_e2e.py)
 - Audio: [Using audio encoding and decoding for Whisper](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/whisper_e2e.py)
 - Vision: [The YOLO model with our DrawBoundingBoxes operator](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/yolo_e2e.py)