Update link format and example sections in readme (microsoft#1729)

faxu · web-flow · commit a60283845b6e · 2019-09-12T17:49:29.000-07:00
* Fix broken link and minor wording updates

* Update links to use relative paths

* Update sample section organization

* Fix a few more links

* Update links to relative paths

* Fix link urls

* Update links to relative paths

* Update link to perf test doc page

* Update links to relative paths

* Update to relative paths for links

* Update link
diff --git a/README.md b/README.md
@@ -35,53 +35,53 @@
 ***
 # Key Features
 ## Run any ONNX model
-ONNX Runtime provides comprehensive support of the ONNX spec and can be used to run all models based on ONNX v1.2.1 and higher. See version compatibility details [here](https://github.com/microsoft/onnxruntime/blob/master/docs/Versioning.md).
+ONNX Runtime provides comprehensive support of the ONNX spec and can be used to run all models based on ONNX v1.2.1 and higher. See version compatibility details [here](./docs/Versioning.md).
 
 **Traditional ML support**
 
 In addition to DNN models, ONNX Runtime fully supports the [ONNX-ML profile](https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md) of the ONNX spec for traditional ML scenarios.
 
-For the full set of operators and types supported, please see [operator documentation](https://github.com/microsoft/onnxruntime/blob/master/docs/OperatorKernels.md)
+For the full set of operators and types supported, please see [operator documentation](./docs/OperatorKernels.md)
 
-*Note: Some operators not supported in the current ONNX version may be available as a [Contrib Operator](https://github.com/microsoft/onnxruntime/blob/master/docs/ContribOperators.md)*
+*Note: Some operators not supported in the current ONNX version may be available as a [Contrib Operator](./docs/ContribOperators.md)*
 
 
 ## High Performance
 ONNX Runtime supports both CPU and GPU. Using various graph optimizations and accelerators, ONNX Runtime can provide lower latency compared to other runtimes for faster end-to-end customer experiences and minimized machine utilization costs.
 
 Currently ONNX Runtime supports the following accelerators:
 * MLAS (Microsoft Linear Algebra Subprograms)
-* [MKL-DNN](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/MKL-DNN-ExecutionProvider.md) - [subgraph optimization](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/MKL-DNN-Subgraphs.md)
+* [MKL-DNN](./docs/execution_providers/MKL-DNN-ExecutionProvider.md) - [subgraph optimization](./docs/execution_providers/MKL-DNN-Subgraphs.md)
 * MKL-ML
-* [Intel nGraph](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md)
+* [Intel nGraph](./docs/execution_providers/nGraph-ExecutionProvider.md)
 * CUDA
-* [TensorRT](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/TensorRT-ExecutionProvider.md)
-* [OpenVINO](https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/OpenVINO-ExecutionProvider.md)
-* [Nuphar](docs/execution_providers/Nuphar-ExecutionProvider.md)
+* [TensorRT](./docs/execution_providers/TensorRT-ExecutionProvider.md)
+* [OpenVINO](./docs/execution_providers/OpenVINO-ExecutionProvider.md)
+* [Nuphar](./docs/execution_providers/Nuphar-ExecutionProvider.md)
 
-Not all variations are supported in the [official release builds](#apis-and-official-builds), but can be built from source following [these instructions](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md). Find Dockerfiles [here](https://github.com/microsoft/onnxruntime/tree/master/dockerfiles).
+Not all variations are supported in the [official release builds](#apis-and-official-builds), but can be built from source following [these instructions](./BUILD.md). Find Dockerfiles [here](./dockerfiles).
 
 We are continuously working to integrate new execution providers for further improvements in latency and efficiency. If you are interested in contributing a new execution provider, please see [this page](docs/AddingExecutionProvider.md).
 
 
 ## Cross Platform
-[API documentation and package installation](https://github.com/microsoft/onnxruntime#installation)
+[API documentation and package installation](#installation)
 
-ONNX Runtime is available for Linux, Windows, Mac with Python, C#, and C APIs, with more to come!
-If you have specific scenarios that are not currently supported, please share your suggestions and scenario details via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
+ONNX Runtime is currently available for Linux, Windows, and Mac with Python, C#, C++, and C APIs.
+If you have specific scenarios that are not supported, please share your suggestions and scenario details via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
 ***
 # Installation
 **Quick Start:** The [ONNX-Ecosystem Docker container image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem) is available on Dockerhub and includes ONNX Runtime (CPU, Python), dependencies, tools to convert from various frameworks, and Jupyter notebooks to help get started.
 
-Additional dockerfiles for some features can be found [here](https://github.com/microsoft/onnxruntime/tree/master/dockerfiles).
+Additional dockerfiles for some features can be found [here](./dockerfiles).
 
 ## APIs and Official Builds
 
 ### API Documentation
 * [Python](https://aka.ms/onnxruntime-python)
 * [C](docs/C_API.md)
 * [C#](docs/CSharp_API.md)
-* [C++](https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/session/onnxruntime_cxx_api.h)
+* [C++](./include/onnxruntime/core/session/onnxruntime_cxx_api.h)
 
 ### Official Builds
 | | CPU (MLAS+Eigen) | CPU (MKL-ML) | GPU (CUDA)
@@ -100,7 +100,7 @@ system.
 	 * Version: **CUDA 10.0** and **cuDNN 7.3**
 	 * Linux Python packages require **CUDA 10.1** and **cuDNN 7.6** 
   * Older ONNX Runtime releases: used **CUDA 9.1** and **cuDNN 7.1** - please refer to [prior release notes](https://github.com/microsoft/onnxruntime/releases) for more details.
-* Python binaries are compatible with **Python 3.5-3.7**. See [Python Dev Notes](https://github.com/microsoft/onnxruntime/blob/master/docs/Python_Dev_Notes.md). If using `pip` to be download the Python binaries, run `pip install --upgrade pip` prior to downloading. 
+* Python binaries are compatible with **Python 3.5-3.7**. See [Python Dev Notes](./docs/Python_Dev_Notes.md). If using `pip` to be download the Python binaries, run `pip install --upgrade pip` prior to downloading. 
 * Certain operators makes use of system locales. Installation of the **English language package** and configuring `en_US.UTF-8 locale` is required.
   * For Ubuntu install [language-pack-en package](https://packages.ubuntu.com/search?keywords=language-pack-en)
   * Run the following commands:
@@ -111,7 +111,7 @@ system.
 ## Building from Source
 If additional build flavors are needed, please find instructions on building from source at [Build ONNX Runtime](BUILD.md). For production scenarios, it's strongly recommended to build from an [official release branch](https://github.com/microsoft/onnxruntime/releases).
 
-Dockerfiles are available [here](https://github.com/microsoft/onnxruntime/tree/faxu-doc-updates/tools/ci_build/github/linux/docker) to help you get started.
+Dockerfiles are available [here](./tools/ci_build/github/linux/docker) to help you get started.
 
 ***
 # Usage
@@ -127,24 +127,25 @@ Dockerfiles are available [here](https://github.com/microsoft/onnxruntime/tree/f
 ## Deploying ONNX Runtime
 ONNX Runtime can be deployed to the cloud for model inferencing using [Azure Machine Learning Services](https://azure.microsoft.com/en-us/services/machine-learning-service). See [detailed instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx).
 
-**ONNX Runtime Server (beta)** is a hosted application for serving ONNX models using ONNX Runtime, providing a REST API for prediction. Usage details can be found [here](https://github.com/microsoft/onnxruntime/blob/master/docs/ONNX_Runtime_Server_Usage.md), and image installation instructions are [here](https://github.com/microsoft/onnxruntime/tree/master/dockerfiles#onnx-runtime-server-preview).
+**ONNX Runtime Server (beta)** is a hosted application for serving ONNX models using ONNX Runtime, providing a REST API for prediction. Usage details can be found [here](./docs/ONNX_Runtime_Server_Usage.md), and image installation instructions are [here](./dockerfiles#onnx-runtime-server-preview).
 
 ## Performance Tuning
-ONNX Runtime is open and extensible, supporting a broad set of configurations and execution providers for model acceleration. For performance tuning guidance, please see [this page](https://github.com/microsoft/onnxruntime/blob/master/docs/ONNX_Runtime_Perf_Tuning.md).
+ONNX Runtime is open and extensible, supporting a broad set of configurations and execution providers for model acceleration. For performance tuning guidance, please see [this page](./docs/ONNX_Runtime_Perf_Tuning.md).
 
 ***
 # Examples and Tutorials
 ## Python
-* [Basic Inferencing Sample](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/simple_onnxruntime_inference.ipynb)
-* [Inferencing (Resnet50)](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
-* [Inferencing samples](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem/inference_demos) using [ONNX-Ecosystem Docker image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem)
-* [Train, Convert, and Inference a SKL pipeline](https://microsoft.github.io/onnxruntime/auto_examples/plot_train_convert_predict.html#sphx-glr-auto-examples-plot-train-convert-predict-py)
-* [Convert and Inference a Keras model](https://microsoft.github.io/onnxruntime/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
-* [ONNX Runtime Server: SSD Single Shot MultiBox Detector](https://github.com/onnx/tutorials/blob/master/tutorials/OnnxRuntimeServerSSDModel.ipynb)
-* [Running ONNX model tests](https://github.com/microsoft/onnxruntime/blob/master/docs/Model_Test.md)
+**Inference only**
+* [Model Inferencing (single node Sigmoid)](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/simple_onnxruntime_inference.ipynb)
+* [Model Inferencing (Resnet50)](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
+* [Model Inferencing](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem/inference_demos) using [ONNX-Ecosystem Docker image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem)
+* [Model Inferencing using ONNX Runtime Server (SSD Single Shot MultiBox Detector)](https://github.com/onnx/tutorials/blob/master/tutorials/OnnxRuntimeServerSSDModel.ipynb)
 
+**Inference with model conversion**
+* [SKL Pipeline: Train, Convert, and Inference](https://microsoft.github.io/onnxruntime/auto_examples/plot_train_convert_predict.html#sphx-glr-auto-examples-plot-train-convert-predict-py)
+* [Keras: Convert and Inference](https://microsoft.github.io/onnxruntime/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
 
-**Deployment with AzureML**
+**Inference and deploy through AzureML**
 * Inferencing using [ONNX Model Zoo](https://github.com/onnx/models) models: 
   * [Facial Expression Recognition](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) 
   * [MNIST Handwritten Digits](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
@@ -153,19 +154,26 @@ ONNX Runtime is open and extensible, supporting a broad set of configurations an
   * [TinyYolo](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
 * Train a model with PyTorch and Inferencing:
   * [MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
- 
-* Inferencing with TensorRT Execution Provider on GPU (AKS)
-  * [FER+](https://github.com/microsoft/onnxruntime/blob/master/docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
+
+* GPU: Inferencing with TensorRT Execution Provider (AKS)
+  * [FER+](./docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
+
+**Inference and Deploy wtih Azure IoT Edge**
+  * [Intel OpenVINO](http://aka.ms/onnxruntime-openvino)
+  * [NVIDIA TensorRT on Jetson Nano (ARM64)](http://aka.ms/onnxruntime-arm64)
+
+**Other**
+* [Running ONNX model tests](./docs/Model_Test.md)
 
 
 ## C#
-* [Inferencing Tutorial](https://github.com/microsoft/onnxruntime/blob/master/docs/CSharp_API.md#getting-started)
+* [Inferencing Tutorial](./docs/CSharp_API.md#getting-started)
 
 
 ## C/C++
-* [Basic Inferencing (SqueezeNet) - C](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
-* [Basic Inferencing (SqueezeNet) - C++](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
-* [Inferencing (MNIST) - C++](https://github.com/microsoft/onnxruntime/tree/master/samples/c_cxx/MNIST)
+* [C - Inferencing (SqueezeNet)](./csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
+* [C++ - Inferencing (SqueezeNet)](./csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
+* [C++ - Inferencing (MNIST)](./samples/c_cxx/MNIST)
 
 ***
 # Technical Design Details
diff --git a/docs/CSharp_API.md b/docs/CSharp_API.md
@@ -34,7 +34,7 @@ You can load your input data into Tensor<T> objects in several ways. A simple ex
     int[] dimensions;    // and the dimensions of the input is stored here
     Tensor<float> t1 = new DenseTensor<float>(sourceData, dimensions);    
 
-Here is a [complete sample code](https://github.com/Microsoft/onnxruntime/tree/master/csharp/sample/Microsoft.ML.OnnxRuntime.InferenceSample) that runs inference on a pretrained model.
+Here is a [complete sample code](../csharp/sample/Microsoft.ML.OnnxRuntime.InferenceSample) that runs inference on a pretrained model.
 
 ## Running on GPU (Optional)
 If using the GPU package, simply use the appropriate SessionOptions when creating an InferenceSession.
diff --git a/docs/ONNX_Runtime_Server_Usage.md b/docs/ONNX_Runtime_Server_Usage.md
@@ -1,4 +1,4 @@
-<h1><span style="color:red">Note: ONNX Runtime Server is still in beta state. It's currently not ready for production environments.</span></h1>
+<h1><span style="color:red">Note: ONNX Runtime Server is still in beta state and may not be ready for production environments.</span></h1>
 
 # How to Use ONNX Runtime Server for Prediction
 
@@ -43,7 +43,7 @@ http://<your_ip_address>:<port>/v1/models/<your-model-name>/versions/<your-versi
 
 ### Request and Response Payload
 
-The request and response need to be a protobuf message. The Protobuf definition can be found [here](https://github.com/Microsoft/onnxruntime/blob/master/onnxruntime/server/protobuf/predict.proto).
+The request and response need to be a protobuf message. The Protobuf definition can be found [here](../onnxruntime/server/protobuf/predict.proto).
 
 A protobuf message could have two formats: binary and JSON. Usually the binary payload has better latency, in the meanwhile the JSON format is easy for human readability. 
 
@@ -74,7 +74,7 @@ A simple Jupyter notebook demonstrating the usage of ONNX Runtime server to host
 
 ## GRPC Endpoint
 
-If you prefer using the GRPC endpoint, the protobuf could be found [here](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/server/protobuf/prediction_service.proto). You could generate your client and make a GRPC call to it. To learn more about how to generate the client code and call to the server, please refer to [the tutorials of GRPC](https://grpc.io/docs/tutorials/).
+If you prefer using the GRPC endpoint, the protobuf could be found [here](../onnxruntime/server/protobuf/prediction_service.proto). You could generate your client and make a GRPC call to it. To learn more about how to generate the client code and call to the server, please refer to [the tutorials of GRPC](https://grpc.io/docs/tutorials/).
 
 ## Advanced Topics
 
@@ -91,7 +91,7 @@ For easy tracking of requests, we provide the following header fields:
 
 ### rsyslog Support
 
-If you prefer using an ONNX Runtime Server with [rsyslog](https://www.rsyslog.com/) support([build instruction](https://github.com/microsoft/onnxruntime/blob/master/BUILD.md#build-onnx-runtime-server-on-linux)), you should be able to see the log in `/var/log/syslog` after the ONNX Runtime Server runs. For detail about how to use rsyslog, please reference [here](https://www.rsyslog.com/category/guides-for-rsyslog/).
+If you prefer using an ONNX Runtime Server with [rsyslog](https://www.rsyslog.com/) support([build instruction](../BUILD.md#build-onnx-runtime-server-on-linux)), you should be able to see the log in `/var/log/syslog` after the ONNX Runtime Server runs. For detail about how to use rsyslog, please reference [here](https://www.rsyslog.com/category/guides-for-rsyslog/).
 
 ## Report Issues
 
diff --git a/docs/execution_providers/MKL-DNN-ExecutionProvider.md b/docs/execution_providers/MKL-DNN-ExecutionProvider.md
@@ -26,10 +26,10 @@ InferenceSession session_object{so};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime:: MKLDNNExecutionProvider >());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](https://github.com/Microsoft/onnxruntime/blob/master/docs/C_API.md#c-api).
+The C API details are [here](../C_API.md#c-api).
 
 ## Python
-When using the python wheel from the ONNX Runtime built with MKL-DNN execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](https://github.com/Microsoft/onnxruntime/blob/master/docs/python/api_summary.rst#api-summary).
+When using the python wheel from the ONNX Runtime built with MKL-DNN execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](https://aka.ms/onnxruntime-python).
 
-## Using onnxruntime_perf_test and onnx_test_runner
-You can test the performance of your ONNX Model with the MKL-DNN execution provider. Use the flag -e mkldnn in [onnxruntime_perf_test](https://github.com/Microsoft/onnxruntime/tree/master/onnxruntime/test/perftest#onnxruntime-performance-test) and [onnx_test_runner](https://github.com/Microsoft/onnxruntime/tree/master/onnxruntime/test/onnx/README.txt)..
+## Performance Tuning
+For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../ONNX_Runtime_Perf_Tuning.md)
diff --git a/docs/execution_providers/TensorRT-ExecutionProvider.md b/docs/execution_providers/TensorRT-ExecutionProvider.md
diff --git a/docs/execution_providers/nGraph-ExecutionProvider.md b/docs/execution_providers/nGraph-ExecutionProvider.md
