Fix broken links #3

docs/how-to/add-custom-op.md

@@ -13,26 +13,29 @@ nav_order: 2
 * TOC placeholder
 {:toc}
 
-## A new op can be written and registered with ONNXRuntime in the following 3 ways
+A new op can be written and registered with ONNXRuntime in the following 3 ways
 
-### 1. Using the experimental custom op API in the C API (onnxruntime_c_api.h)
+1. Using the experimental custom op API in the C API (onnxruntime_c_api.h)
 
 Note: These APIs are experimental and will change in the next release. They're released now for feedback and experimentation.
+
 * Create an OrtCustomOpDomain with the domain name used by the custom ops
 * Create an OrtCustomOp structure for each op and add them to the OrtCustomOpDomain with OrtCustomOpDomain_Add
 * Call OrtAddCustomOpDomain to add the custom domain of ops to the session options
 See [this](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/test/shared_lib/test_inference.cc) for an example called MyCustomOp that uses the C++ helper API (onnxruntime_cxx_api.h).
-Currently, the only supported Execution Providers (EPs) for custom ops registered via this approach are the `CUDA` and the `CPU` EPs. 
+Currently, the only supported Execution Providers (EPs) for custom ops registered via this approach are the `CUDA` and the `CPU` EPs.
+
+2. Using RegisterCustomRegistry API
 
-### 2. Using RegisterCustomRegistry API
 * Implement your kernel and schema (if required) using the OpKernel and OpSchema APIs (headers are in the include folder).
 * Create a CustomRegistry object and register your kernel and schema with this registry.
 * Register the custom registry with ONNXRuntime using RegisterCustomRegistry API.
 
 See
 [this](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/test/framework/local_kernel_registry_test.cc) for an example.
 
-### 3. Contributing the op to ONNXRuntime
+3. Contributing the op to ONNXRuntime
+
 This is mostly meant for ops that are in the process of being proposed to ONNX. This way you don't have to wait for an approval from the ONNX team
 if the op is required in production today.
 See [this](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/contrib_ops) for an example.

docs/how-to/custom-python-operator.md

@@ -17,7 +17,7 @@ The Python Operator provides the capability to easily invoke any custom Python c
 
 ## Design Overview
 
-The feature can be found under [onnxruntime/core/language_interop_ops](../onnxruntime/core/language_interop_ops).
+The feature can be in [https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/core/language_interop_ops](onnxruntime/core/language_interop_ops).
 
 Here is a chart of calling sequence:
 

docs/reference/execution-providers/ACL-ExecutionProvider.md

@@ -33,10 +33,10 @@ session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::ACLExec
 status = session_object.Load(model_file_name);
 ```
 
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 ## Performance Tuning
 
 For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../../how-to/tune-performance.md)
 
-When/if using [onnxruntime_perf_test](../../onnxruntime/test/perftest), use the flag -e acl
+When/if using [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest), use the flag -e acl

docs/reference/execution-providers/ArmNN-ExecutionProvider.md

@@ -28,9 +28,9 @@ InferenceSession session_object{so, env};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::ArmNNExecutionProvider>());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 ### Performance Tuning
 For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../../how-to/tune-performance.md)
 
-When/if using [onnxruntime_perf_test](../../onnxruntime/test/perftest), use the flag -e armnn
+When/if using [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest), use the flag -e armnn

docs/reference/execution-providers/DNNL-ExecutionProvider.md

@@ -47,7 +47,7 @@ InferenceSession session_object{so,env};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime:: DNNLExecutionProvider >());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 ### Python
 When using the python wheel from the ONNX Runtime built with DNNL execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](https://aka.ms/onnxruntime-python).

docs/reference/execution-providers/MIGraphX-ExecutionProvider.md

@@ -37,7 +37,7 @@ status = session_object.Load(model_file_name);
 ```
 You can check [here](https://github.com/scxiao/ort_test/tree/master/char_rnn) for a specific c/c++ program.
 
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 ### Python
 When using the Python wheel from the ONNX Runtime build with MIGraphX execution provider, it will be automatically
@@ -50,7 +50,7 @@ model on either the CPU or MIGraphX Execution Provider.
 ## Performance Tuning
 For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../../how-to/tune-performance.md)
 
-When/if using [onnxruntime_perf_test](../../onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e migraphx` 
+When/if using [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e migraphx` 
 
 ## Configuring environment variables
 MIGraphX providers an environment variable ORT_MIGRAPHX_FP16_ENABLE to enable the FP16 mode.

docs/reference/execution-providers/NNAPI-ExecutionProvider.md

@@ -41,4 +41,4 @@ InferenceSession session_object{so,env};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::NnapiExecutionProvider>());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).

docs/reference/execution-providers/Nuphar-ExecutionProvider.md

@@ -23,13 +23,13 @@ For build instructions, please see the [BUILD page](../../how-to/build.md#nuphar
 
 ## Using the Nuphar execution provider
 ### C/C++
-The Nuphar execution provider needs to be registered with ONNX Runtime to enable in the inference session. The C API details are [here](../api/c-api.md.md).
+The Nuphar execution provider needs to be registered with ONNX Runtime to enable in the inference session. The C API details are [here](../api/c-api.md).
 
 ### Python
 You can use the Nuphar execution provider via the python wheel from the ONNX Runtime build. The Nuphar execution provider will be automatically prioritized over the default CPU execution providers, thus no need to separately register the execution provider. Python APIs details are [here](../python/api_summary.rst#api-summary).
 
 ## Performance and Accuracy Testing
-You can test your ONNX model's performance with [onnxruntime_perf_test](../../onnxruntime/test/perftest/README.md), or test accuracy with [onnx_test_runner](../../onnxruntime/test/onnx/README.txt). To run these tools with the Nuphar execution provider, please pass `-e nuphar` in command line options.
+You can test your ONNX model's performance with [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest/README.md), or test accuracy with [onnx_test_runner](../../onnxruntime/test/onnx/README.txt). To run these tools with the Nuphar execution provider, please pass `-e nuphar` in command line options.
 
 Please note that Nuphar uses TVM thread pool and parallel schedule for multi-thread inference performance. When building with OpenMP or MKLML, TVM thread pool would use gomp or iomp as its implementation; otherwise, TVM creates its own thread pool. Because of this, the current default parallel schedule policy is:
 - Default to on for USE_OPENMP or USE_MKLML. User can use OMP_NUM_THREADS/MKL_NUM_THREADS to control TVM thread pool, as well as TVM_NUM_THREADS

docs/reference/execution-providers/RKNPU-ExecutionProvider.md

@@ -41,7 +41,7 @@ InferenceSession session_object{so,env};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::RknpuExecutionProvider>());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 
 ## Supported Operators

docs/reference/execution-providers/TensorRT-ExecutionProvider.md

@@ -40,7 +40,7 @@ InferenceSession session_object{so,env};
 session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::TensorrtExecutionProvider>());
 status = session_object.Load(model_file_name);
 ```
-The C API details are [here](../api/c-api.md.md).
+The C API details are [here](../api/c-api.md).
 
 #### Shape Inference for TensorRT Subgraphs
 If some operators in the model are not supported by TensorRT, ONNX Runtime will partition the graph and only send supported subgraphs to TensorRT execution provider. Because TensorRT requires that all inputs of the subgraphs have shape specified, ONNX Runtime will throw error if there is no input shape info. In this case please run shape inference for the entire model first by running script [here](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/providers/nuphar/scripts/symbolic_shape_infer.py).
@@ -69,7 +69,7 @@ Please see [this Notebook](../python/notebooks/onnx-inference-byoc-gpu-cpu-aks.i
 ## Performance Tuning
 For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../../how-to/tune-performance.md)
 
-When/if using [onnxruntime_perf_test](../../onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e tensorrt` 
+When/if using [onnxruntime_perf_test](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/test/perftest#onnxruntime-performance-test), use the flag `-e tensorrt` 
 
 ## Configuring environment variables
 There are four environment variables for TensorRT execution provider.

docs/reference/execution-providers/nGraph-ExecutionProvider.md

@@ -65,7 +65,7 @@ The C API details are [here](../api/c-api.md).
 
 ### Python
 
-When using the python wheel from the ONNX Runtime built with nGraph execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](../python/api_summary.rst#api-summary).
+When using the python wheel from the ONNX Runtime built with nGraph execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](/python/api_summary).
 
 ## Performance Tuning
 

docs/tutorials/resnet50_csharp.md

@@ -9,7 +9,7 @@ nav_order: 2
 
 The sample walks through how to run a pretrained ResNet50 v2 ONNX model using the Onnx Runtime C# API.
 
-The source code for this sample is available [here](Program.cs).
+The source code for this sample is available [here](https://github.com/microsoft/onnxruntime/tree/master/csharp/sample/Microsoft.ML.OnnxRuntime.ResNet50v2Sample).
 
 ## Contents
 {: .no_toc }
@@ -23,7 +23,7 @@ To run this sample, you'll need the following things:
 
 1. Install [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) or higher for you OS (Mac, Windows or Linux).
 2. Download the [ResNet50 v2](https://github.com/onnx/models/blob/master/vision/classification/resnet/model/resnet50-v2-7.onnx) ONNX model to your local system.
-3. Download [this picture of a dog](dog.jpeg) to test the model. You can also use any image you like.
+3. Download [this picture of a dog](/images/dog.jpeg) to test the model. You can also use any image you like.
 
 ## Getting Started