Website Visual Updates & Fixing Broken Links (#14430)

css/custom.css

@@ -144,7 +144,7 @@ a.link:active .link-arrow{
     transition:all 200ms ease-in-out;
 }
 .get-started-section h2{
-    font-size:34px;
+    font-size:30px;
 }
 .tab-wrapper{
     margin: 3rem auto;
@@ -314,7 +314,7 @@ transition:all 200ms ease-in-out;
     background-color: #333333;
 }
 .footer h2{
-    font-size: 34px;
+    font-size: 30px;
 }
 .footer .footer-heading{
     color:#ffffff;
@@ -337,6 +337,9 @@ transition:all 200ms ease-in-out;
 .footer a:active{
     color:#a0dcf9;
 }
+.footer a .link-content {
+    box-shadow: 0 2px  #1fa2ff;
+}
 .footer a:hover .link-content,
 .footer a:focus .link-content,
 .footer a:active .link-content{
@@ -712,7 +715,7 @@ a .abbr[data-original-title], a abbr[title]{
     cursor: pointer;
 }
 .section-heading{
-    font-size: 34px;
+    font-size: 30px;
 }
 .blue-title-columns h3{
     font-size: 24px;

docs/ecosystem/index.md

@@ -29,9 +29,6 @@ ONNX Runtime functions as part of an ecosystem of tools and platforms to deliver
 * [Export a Custom Vision model to ONNX format](https://docs.microsoft.com/en-us/samples/azure-samples/cognitive-services-onnx-customvision-sample/cognitive-services-onnx-customvision-sample/){:target="_blank"}
 * [Use a Custom Vision model with Windows Machine Learning](https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/custom-vision-onnx-windows-ml){:target="_blank"}
 
-## Azure Live Video Analytics
-* [Azure Video Analytics: YOLOv3 and TinyYOLOv3](https://github.com/Azure/live-video-analytics/tree/master/utilities/video-analysis/yolov3-onnx){:target="_blank"}
-
 ## Azure SQL Edge
 * [ML predictions in Azure SQL Edge and Azure SQL Managed Instance](https://docs.microsoft.com/en-us/azure/azure-sql-edge/deploy-onnx){:target="_blank"}
 
@@ -44,4 +41,4 @@ ONNX Runtime functions as part of an ecosystem of tools and platforms to deliver
 * [Inference: Object detection](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx){:target="_blank"}
 
 ## NVIDIA Triton Inference Server
-* [ONNX Runtime backend for Triton](https://github.com/triton-inference-server/onnxruntime_backend){:target="_blank"}
+* [ONNX Runtime backend for Triton](https://github.com/triton-inference-server/onnxruntime_backend){:target="_blank"}

docs/execution-providers/OpenVINO-ExecutionProvider.md

@@ -31,7 +31,7 @@ ONNX Runtime OpenVINO™ Execution Provider is compatible with three lastest rel
 
 |ONNX Runtime|OpenVINO™|Notes|
 |---|---|---|
-|1.13.0|2022.3|[Details](https://github.com/intel/onnxruntime/releases/tag/v4.3)|
+|1.13.0|2022.3|[Coming soon](https://github.com/intel/onnxruntime/releases)|
 |1.13.0|2022.2|[Details](https://github.com/intel/onnxruntime/releases/tag/v4.2)|
 |1.11.0|2022.1|[Details](https://github.com/intel/onnxruntime/releases/tag/v4.0)|
 |1.10.0|2021.4.2|[Details](https://github.com/intel/onnxruntime/releases/tag/v3.4)|

docs/get-started/with-c.md

@@ -87,7 +87,7 @@ chooses to override this by setting ```session_state.use_env_allocators``` to "0
 * **Share initializer(s) and their ORT pre-processed version(s) between sessions:**
    * *Description*: This feature allows a user to share the same instance of an initializer (and their ORT "pre-processed" versions)across multiple sessions.
    * *Scenario*: You've several models that use the same set of initializers except the last few layers of the model and you load these models in the same process. When every model (session) creates a separate instance of the same initializer, it leads to excessive and wasteful memory usage since in this case it's the same initializer. You want to optimize memory usage while having the flexibility to allocate the initializers (possibly even store them in shared memory). 
-   * *Usage*: Use the ```AddInitializer``` API to add a pre-allocated initializer to session options before calling ```CreateSession```. Use the same instance of session options to create several sessions allowing the initializer(s) to be shared between the sessions. See [C API sample usage (TestSharingOfInitializerAndItsPrepackedVersion)](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/shared_lib/test_inference.cc) and [C# API sample usage (TestSharingOfInitializerAndItsPrepackedVersion)](https://github.com/microsoft/onnxruntime/blob/main/csharp/test/Microsoft.ML.OnnxRuntime.Tests/InferenceTest.cs).
+   * *Usage*: Use the ```AddInitializer``` API to add a pre-allocated initializer to session options before calling ```CreateSession```. Use the same instance of session options to create several sessions allowing the initializer(s) to be shared between the sessions. See [C API sample usage (TestSharingOfInitializerAndItsPrepackedVersion)](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/shared_lib/test_inference.cc) and [C# API sample usage (TestSharingOfInitializerAndItsPrepackedVersion)](https://github.com/microsoft/onnxruntime/blob/main/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs).
    * In some ORT operator implementations, initializers are pre-processed when the model is loaded (a process called "pre-packing") to promote optimal operator inferencing on some platforms. By default, these pre-processed versions of initializers are maintained on a per-session basis (i.e.) they are not shared between sessions. To enable sharing these between sessions, create a container (using ```CreatePrepackedWeightsContainer```) and pass this at session creation time so that the sharing of pre-packed versions of shared initializers between sessions take place and these are not duplicated in memory. The same tests referenced above in C and C# shows sample usage of this feature as well.
       NOTE: Any kernel developer wishing to implement pre-packing MUST write a test that triggers pre-packing of all weights that can be possibly pre-packed using the kernel and must test sharing of these pre-packed weights between sessions. See [kernel test (SharedPrepackedWeights)](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/providers/cpu/math/gemm_test.cc).
    

docs/performance/tune-performance.md

@@ -356,7 +356,7 @@ Refer to the [Python API docs](https://onnxruntime.ai/docs/api/python).
 
 * C#
 
-Refer to [OrtIoBindingAllocationTest.cs](https://github.com/microsoft/onnxruntime/blob/main/csharp/test/Microsoft.ML.OnnxRuntime.Tests/OrtIoBindingAllocationTest.cs).
+Refer to [OrtIoBindingAllocationTest.cs](https://github.com/microsoft/onnxruntime/blob/main/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/OrtIoBindingAllocationTest.cs).
 
 ### Convolution-heavy models and the CUDA EP
 
@@ -621,4 +621,4 @@ residual_of_N / (dynamic_block_base * num_of_threads)
 whenever it is ready to run. So over a period of time, threads in the pool are likely to be better load balanced, thereby lowering the latency variance.
 
 Due to the same reason, the dynamic cost model may also improve the performance for cases when threads are more likely be preempted.
-Per our tests, by far the best configuration for dynamic_block_base is 4, which lowers the variance while keeping good performance.
+Per our tests, by far the best configuration for dynamic_block_base is 4, which lowers the variance while keeping good performance.

docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md

@@ -9,7 +9,7 @@ nav_exclude: true
 
 2. The sample involves presenting an image to the ONNX Runtime (RT), which uses the OpenVINO Execution Provider for ONNX RT to run inference on various Intel hardware devices like Intel CPU, GPU, VPU and more. The sample uses OpenCV for image processing and ONNX Runtime OpenVINO EP for inference. After the sample image is inferred, the terminal will output the predicted label classes in order of their confidence.
 
-The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/OpenVINO_EP/squeezenet_classification).
+The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/OpenVINO_EP/Linux/squeezenet_classification).
 
 # How to build
 

docs/tutorials/accelerate-pytorch/ort-training.md

@@ -7,14 +7,4 @@ nav_order: 2
 # ORTModule Examples
 * [Use ORTModule with HuggingFace Models](https://github.com/microsoft/onnxruntime-training-examples/tree/master/huggingface)
 
-# (deprecated) ORTTrainer Examples
-{: .no_toc }
 
-* [Get Started: Basic PyTorch transformer model](https://github.com/microsoft/onnxruntime-training-examples/tree/master/orttrainer/getting-started)
-
-* [Accelerate pre-training of large BERT model](https://github.com/microsoft/onnxruntime-training-examples/tree/master/orttrainer/nvidia-bert)
-
-* [Accelerate fine tuning of Huggingface GPT2 model](https://github.com/microsoft/onnxruntime-training-examples/tree/master/orttrainer/huggingface-gpt2)
-
-
-* *More coming soon!*

docs/tutorials/csharp/yolov3_object_detection_csharp.md

@@ -74,6 +74,6 @@ dotnet run [path-to-model] [path-to-image] [path-to-output-image]
 ```
 
 ## References:
-[fasterrcnn_csharp](https://github.com/microsoft/onnxruntime/blob/gh-pages/docs/tutorials/tutorials/fasterrcnn_csharp.md)
+[fasterrcnn_csharp](https://github.com/microsoft/onnxruntime/blob/gh-pages/docs/tutorials/csharp/fasterrcnn_csharp.md)
 
-[resnet50_csharp](https://github.com/microsoft/onnxruntime/blob/gh-pages/docs/tutorials/tutorials/resnet50_csharp.md)
+[resnet50_csharp](https://github.com/microsoft/onnxruntime/blob/gh-pages/docs/tutorials/csharp/resnet50_csharp.md)