Fixed all accessibility colors. Again. (#23055)

Should address the last 300+ accessibility color issues.

.github/workflows/check-website-links.yml

@@ -1,4 +1,5 @@
 name: CheckLinks
+
 on:
   push:
     branches:
@@ -12,20 +13,21 @@ jobs:
   checklinks:
     name: Check website links
     runs-on: ubuntu-latest
+
     steps:
       - uses: actions/checkout@v2
       - name: Ruby
         uses: ruby/setup-ruby@v1
         with:
-          ruby-version: 2.6
+          ruby-version: 3.3
           bundler-cache: true
       - name: Build jekyll website with drafts
         run: bundle exec jekyll build --drafts
 
       - name: Use Node.js
         uses: actions/setup-node@v3
         with:
-          node-version: 19.x
+          node-version: 22.x
 
       - name: Install dependencies
         run: npm install
@@ -37,7 +39,28 @@ jobs:
         run: |
           sudo mv ./build/* ./_site
           rm -rf ./_site/src
-
       - name: Check for broken links
         run: |
-          bundle exec htmlproofer --assume_extension --checks_to_ignore ImageCheck,ScriptCheck --only_4xx --http_status_ignore 429,403 --allow_hash_href --url_ignore "https://onnxruntime.ai/docs/reference/api/c-api.html,https://www.onnxruntime.ai/docs/reference/execution-providers/TensorRT-ExecutionProvider.html#c-api-example,https://www.onnxruntime.ai/docs/resources/graph-optimizations.html,onnxruntime/capi/onnxruntime_pybind11_state.html,https://github.com/microsoft/onnx-converters-private/issues/new/choose,https://aka.ms/onnx/exportissue,https://aka.ms/onnx/board" --log-level :info ./_site
+          bundle exec htmlproofer ./_site \
+            --only_4xx \
+            --ignore-status-codes 429,403 \
+            --allow_hash_href \
+            --allow_missing_href \
+            --ignore_urls "/.*linkedin\.com.*/,https://onnxruntime.ai/docs/reference/api/c-api.html,https://www.onnxruntime.ai/docs/reference/execution-providers/TensorRT-ExecutionProvider.html#c-api-example,https://www.onnxruntime.ai/docs/resources/graph-optimizations.html,onnxruntime/capi/onnxruntime_pybind11_state.html,https://github.com/microsoft/onnx-converters-private/issues/new/choose,https://aka.ms/onnx/exportissue,https://aka.ms/onnx/board" \
+            --no-check-external-hash
+      # - name: Check for broken links
+      #   uses: chabad360/htmlproofer@master
+      #   with:
+      #     directory: "./_site"
+      #     # The directory to scan
+      #     arguments: |
+      #       --no-check_external_hash 
+      #       --assume_extension 
+      #       --only_4xx 
+      #       --ignore_status_codes 429,403,999 
+      #       --allow_missing_href 
+      #       --allow_hash_href 
+      #       --checks 'Links' 
+      #       --log-level :info 
+      #       --ignore_urls "^https://linkedin.com,https://onnxruntime.ai/docs/reference/api/c-api.html,https://www.onnxruntime.ai/docs/reference/execution-providers/TensorRT-ExecutionProvider.html#c-api-example,https://www.onnxruntime.ai/docs/resources/graph-optimizations.html,onnxruntime/capi/onnxruntime_pybind11_state.html,https://github.com/microsoft/onnx-converters-private/issues/new/choose,https://aka.ms/onnx/exportissue,https://aka.ms/onnx/board" 
+      #     # The arguments to pass to HTMLProofer

_sass/color_schemes/onnxruntime.scss

@@ -13,6 +13,7 @@ $btn-primary-color: #226aca;
 // }
 // 2024 December Accessibility changes
 .highlight .s { color: #3c7a3b ;}
+.highlight .py {color: #a25f00;}
 // Initial Theme
 .highlight .hll { background-color: #ffffcc; }
 .highlight { background: #ffffff; }
@@ -22,7 +23,7 @@ $btn-primary-color: #226aca;
 .highlight .o { color: #333333; }
 .highlight .ch { color: #707070 ; }
 .highlight .cm { color: #707070 ; }
-.highlight .cp { color: #557799; }
+.highlight .cp { color: #507191; }
 .highlight .cpf { color: #707070 ; }
 .highlight .c1 { color: #707070 ; }
 .highlight .cs { color: #cc0000; font-weight: bold; }
@@ -52,7 +53,7 @@ $btn-primary-color: #226aca;
 .highlight .ni { color: #880000; font-weight: bold; }
 .highlight .ne { font-weight: bold; color: #eb0000; }
 .highlight .nf { color: #0066BB; font-weight: bold; }
-.highlight .nl { font-weight: bold; color: #8f6f00; }
+.highlight .nl { font-weight: bold; color: #876900; }
 .highlight .nn { font-weight: bold; color: #0d77a2 ; }
 .highlight .nt { color: #007700; }
 .highlight .nv { color: #996633; }
@@ -68,7 +69,7 @@ $btn-primary-color: #226aca;
 .highlight .sc { color: #0044DD; }
 .highlight .dl { background-color: #fff0f0; }
 .highlight .sd { color: #d54220; }
-.highlight .s2 { background-color: #fff0f0; }
+.highlight .s2 { color: #3c7a3b ; background-color: #fff0f0; }
 .highlight .se { color: #666666; font-weight: bold; background-color: #fff0f0; }
 .highlight .sh { background-color: #fff0f0; }
 .highlight .si { background-color: #eeeeee; }

docs/build/eps.md

@@ -271,7 +271,7 @@ See more information on the OpenVINO™ Execution Provider [here](../execution-p
   *2024.3 is the current recommended OpenVINO™ version. [OpenVINO™ 2023.3](https://docs.openvino.ai/2023.3/home.html) is minimal OpenVINO™ version requirement.*
 
 2. Configure the target hardware with specific follow on instructions:
-   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvino.ai/latest/openvino_docs_install_guides_configurations_for_intel_gpu.html#gpu-guide-windows), [Linux](https://docs.openvino.ai/latest/openvino_docs_install_guides_configurations_for_intel_gpu.html#linux)
+   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html#windows), [Linux](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html#linux)
 
 
 3. Initialize the OpenVINO™ environment by running the setupvars script as shown below. This is a required step:
@@ -306,7 +306,7 @@ See more information on the OpenVINO™ Execution Provider [here](../execution-p
 * `--use_openvino` builds the OpenVINO™ Execution Provider in ONNX Runtime.
 * `<hardware_option>`: Specifies the default hardware target for building OpenVINO™ Execution Provider. This can be overriden dynamically at runtime with another option (refer to [OpenVINO™-ExecutionProvider](../execution-providers/OpenVINO-ExecutionProvider.md#summary-of-options) for more details on dynamic device selection). Below are the options for different Intel target devices.
 
-Refer to [Intel GPU device naming convention](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_GPU.html#device-naming-convention) for specifying the correct hardware target in cases where both integrated and discrete GPU's co-exist.
+Refer to [Intel GPU device naming convention](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#device-naming-convention) for specifying the correct hardware target in cases where both integrated and discrete GPU's co-exist.
 
 | Hardware Option | Target Device |
 | --------------- | ------------------------|

docs/build/inferencing.md

@@ -29,7 +29,7 @@ Basic CPU build
    cd onnxruntime
    ```
 
-* Install [Python 3.x](http://python.org/).
+* Install [Python 3.x](https://python.org/).
 
 * Install [cmake-3.27](https://cmake.org/download/) or higher.
 
@@ -394,7 +394,7 @@ This option is very fast and allows the package to be built in minutes, but is c
 
     TLDR; Go to https://www.linaro.org/downloads/, get "64-bit Armv8 Cortex-A, little-endian" and "Linux Targeted", not "Bare-Metal Targeted". Extract it to your build machine and add the bin folder to your $PATH env. Then skip this part.
 
-    You can use [GCC](https://gcc.gnu.org/) or [Clang](http://clang.llvm.org/). Both work, but instructions here are based on GCC.
+    You can use [GCC](https://gcc.gnu.org/) or [Clang](https://clang.llvm.org/). Both work, but instructions here are based on GCC.
 
     In GCC terms:
     * "build" describes the type of system on which GCC is being configured and compiled.
@@ -412,7 +412,7 @@ This option is very fast and allows the package to be built in minutes, but is c
     COLLECT_GCC=/usr/bin/aarch64-linux-gnu-gcc
     COLLECT_LTO_WRAPPER=/usr/libexec/gcc/aarch64-linux-gnu/9/lto-wrapper
     Target: aarch64-linux-gnu
-    Configured with: ../gcc-9.2.1-20190827/configure --bindir=/usr/bin --build=x86_64-redhat-linux-gnu --datadir=/usr/share --disable-decimal-float --disable-dependency-tracking --disable-gold --disable-libgcj --disable-libgomp --disable-libmpx --disable-libquadmath --disable-libssp --disable-libunwind-exceptions --disable-shared --disable-silent-rules --disable-sjlj-exceptions --disable-threads --with-ld=/usr/bin/aarch64-linux-gnu-ld --enable-__cxa_atexit --enable-checking=release --enable-gnu-unique-object --enable-initfini-array --enable-languages=c,c++ --enable-linker-build-id --enable-lto --enable-nls --enable-obsolete --enable-plugin --enable-targets=all --exec-prefix=/usr --host=x86_64-redhat-linux-gnu --includedir=/usr/include --infodir=/usr/share/info --libexecdir=/usr/libexec --localstatedir=/var --mandir=/usr/share/man --prefix=/usr --program-prefix=aarch64-linux-gnu- --sbindir=/usr/sbin --sharedstatedir=/var/lib --sysconfdir=/etc --target=aarch64-linux-gnu --with-bugurl=http://bugzilla.redhat.com/bugzilla/ --with-gcc-major-version-only --with-isl --with-newlib --with-plugin-ld=/usr/bin/aarch64-linux-gnu-ld --with-sysroot=/usr/aarch64-linux-gnu/sys-root --with-system-libunwind --with-system-zlib --without-headers --enable-gnu-indirect-function --with-linker-hash-style=gnu
+    Configured with: ../gcc-9.2.1-20190827/configure --bindir=/usr/bin --build=x86_64-redhat-linux-gnu --datadir=/usr/share --disable-decimal-float --disable-dependency-tracking --disable-gold --disable-libgcj --disable-libgomp --disable-libmpx --disable-libquadmath --disable-libssp --disable-libunwind-exceptions --disable-shared --disable-silent-rules --disable-sjlj-exceptions --disable-threads --with-ld=/usr/bin/aarch64-linux-gnu-ld --enable-__cxa_atexit --enable-checking=release --enable-gnu-unique-object --enable-initfini-array --enable-languages=c,c++ --enable-linker-build-id --enable-lto --enable-nls --enable-obsolete --enable-plugin --enable-targets=all --exec-prefix=/usr --host=x86_64-redhat-linux-gnu --includedir=/usr/include --infodir=/usr/share/info --libexecdir=/usr/libexec --localstatedir=/var --mandir=/usr/share/man --prefix=/usr --program-prefix=aarch64-linux-gnu- --sbindir=/usr/sbin --sharedstatedir=/var/lib --sysconfdir=/etc --target=aarch64-linux-gnu --with-bugurl=https://bugzilla.redhat.com/bugzilla/ --with-gcc-major-version-only --with-isl --with-newlib --with-plugin-ld=/usr/bin/aarch64-linux-gnu-ld --with-sysroot=/usr/aarch64-linux-gnu/sys-root --with-system-libunwind --with-system-zlib --without-headers --enable-gnu-indirect-function --with-linker-hash-style=gnu
     Thread model: single
     gcc version 9.2.1 20190827 (Red Hat Cross 9.2.1-3) (GCC)
     ```

docs/execution-providers/OpenVINO-ExecutionProvider.md

@@ -90,7 +90,7 @@ To use csharp api for openvino execution provider create a custom nuget package.
 
 ### OpenCL queue throttling for GPU devices
 
-Enables [OpenCL queue throttling](https://docs.openvino.ai/latest/groupov_runtime_ocl_gpu_prop_cpp_api.html?highlight=throttling) for GPU devices. Reduces CPU utilization when using GPUs with OpenVINO EP.
+Enables [OpenCL queue throttling](https://docs.openvino.ai/2024/api/c_cpp_api/group__ov__runtime__ocl__gpu__prop__cpp__api.html) for GPU devices. Reduces CPU utilization when using GPUs with OpenVINO EP.
 
 ### Model caching
 
@@ -118,7 +118,7 @@ Int8 models are supported on CPU, GPU and NPU.
 
 OpenVINO™ Execution Provider now  supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations.
 
-See the [OpenVINO™ ONNX Support documentation](https://docs.openvino.ai/latest/classov_1_1Core.html).
+See the [OpenVINO™ ONNX Support documentation](https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-onnx.html).
 
 Converting and Saving an ONNX Model to External Data:
 Use the ONNX API's.[documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md#converting-and-saving-an-onnx-model-to-external-data).
@@ -177,7 +177,7 @@ Use `AUTO:<device 1><device 2>..` as the device name to delegate selection of an
 From the application point of view, this is just another device that handles all accelerators in full system.
 
 For more information on Auto-Device plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Auto Device Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_AUTO.html).
+[Intel OpenVINO™ Auto Device Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#automatic-device-selection).
 
 ### Heterogeneous Execution for OpenVINO™ Execution Provider
 
@@ -186,7 +186,7 @@ The heterogeneous execution enables computing for inference on one network on se
 * To utilize accelerator's power and calculate the heaviest parts of the network on the accelerator and execute unsupported layers on fallback devices like the CPU to utilize all available hardware more efficiently during one inference.
 
 For more information on Heterogeneous plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Heterogeneous Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Hetero_execution.html).
+[Intel OpenVINO™ Heterogeneous Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.html).
 
 ### Multi-Device Execution for OpenVINO EP
 
@@ -196,7 +196,7 @@ Multi-Device plugin automatically assigns inference requests to available comput
 * More consistent performance, since the devices can now share the inference burden (so that if one device is becoming too busy, another device can take more of the load)
 
 For more information on Multi-Device plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Multi Device Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Running_on_multiple_devices.html).
+[Intel OpenVINO™ Multi Device Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#multi-stream-execution).
 
 ### Export OpenVINO Compiled Blob 
 Export the OpenVINO compiled blob as an ONNX model. Using this ONNX model for subsequent inferences avoids model recompilation and could have a positive impact on Session creation time. This feature is currently enabled for fully supported models only. It complies with the ORT session config keys

docs/extensions/add-op.md

@@ -70,7 +70,7 @@ the custom operator kernel C++ code example can be found [operators](https://git
 * the third libraries API docs integrated in ONNXRuntime Extensions the can be used in C++ code
     - OpenCV API docs https://docs.opencv.org/4.x/
     - Google SentencePiece Library docs https://github.com/google/sentencepiece/blob/master/doc/api.md
-    - dlib(matrix and ML library) C++ API docs http://dlib.net/algorithms.html
+    - dlib(matrix and ML library) C++ API docs https://dlib.net/algorithms.html
     - BlingFire Library https://github.com/microsoft/BlingFire
     - Google RE2 Library https://github.com/google/re2/wiki/CplusplusAPI
     - JSON library https://json.nlohmann.me/api/basic_json/

docs/genai/tutorials/finetune.md

@@ -65,7 +65,7 @@ Olive generates models and adapters in ONNX format. These models and adapters ca
 
    Note: this operations requires a system with an NVIDIA GPU, with CUDA installed
 
-   Use the `olive fine-tune` command: https://microsoft.github.io/Olive/features/cli.html#finetune
+   Use the `olive fine-tune` command: https://microsoft.github.io/Olive/how-to/cli/cli-finetune.html
 
    Here is an example usage of the command:
 
@@ -75,12 +75,12 @@ Olive generates models and adapters in ONNX format. These models and adapters ca
 
 2. Optionally, quantize your model
 
-   Use the `olive quantize` command: https://microsoft.github.io/Olive/features/cli.html#quantize
+   Use the `olive quantize` command: https://microsoft.github.io/Olive/how-to/cli/cli-quantize.html
 
 
 3. Generate the ONNX model and adapter using the quantized model
 
-   Use the `olive auto-opt` command for this step: https://microsoft.github.io/Olive/features/cli.html#auto-opt
+   Use the `olive auto-opt` command for this step: https://microsoft.github.io/Olive/how-to/cli/cli-auto-opt.html
 
    The `--adapter path` can either be a HuggingFace adapter reference, or a path to the adapter you fine-tuned above.
 
@@ -162,4 +162,4 @@ python app.py -m <model folder> -a <.onnx_adapter files> -t <prompt template> -s
 ## References
 
 * [Python API docs](../api/python.md#adapter-class)
-* [Olive CLI docs](https://microsoft.github.io/Olive/features/cli.html)
+* [Olive CLI docs](https://microsoft.github.io/Olive/how-to/index.html#working-with-the-cli)

docs/get-started/with-c.md

@@ -61,7 +61,7 @@ is as follows
    * Call ```Run()``` as usual
 * **Share allocator(s) between sessions:**
    * *Description*: This feature allows multiple sessions in the same process to use the same allocator(s).
-   * *Scenario*: You've several sessions in the same process and see high memory usage. One of the reasons for this is as follows. Each session creates its own CPU allocator which is arena based by default. [ORT implements](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/framework/bfc_arena.h) a simplified version of an arena allocator that is based on [Doug Lea's best-first with coalescing algorithm](http://gee.cs.oswego.edu/dl/html/malloc.html). Each allocator lives in its own session. It allocates a large region of memory during init time and thereafter it chunks, coalesces and extends this initial region as per allocation/deallocation demands. Overtime the arena ends up with unused chunks of memory per session. Moreover, the memory allocated by the arena is never returned to the system; once allocated it always remains allocated. All these factors add up when using multiple sessions (each with its own arena) thereby increasing the overall memory consumption of the process. Hence it becomes important to share the arena allocator between sessions.
+   * *Scenario*: You've several sessions in the same process and see high memory usage. One of the reasons for this is as follows. Each session creates its own CPU allocator which is arena based by default. [ORT implements](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/framework/bfc_arena.h) a simplified version of an arena allocator that is based on [Doug Lea's best-first with coalescing algorithm](https://gee.cs.oswego.edu/dl/html/malloc.html). Each allocator lives in its own session. It allocates a large region of memory during init time and thereafter it chunks, coalesces and extends this initial region as per allocation/deallocation demands. Overtime the arena ends up with unused chunks of memory per session. Moreover, the memory allocated by the arena is never returned to the system; once allocated it always remains allocated. All these factors add up when using multiple sessions (each with its own arena) thereby increasing the overall memory consumption of the process. Hence it becomes important to share the arena allocator between sessions.
    * *Usage*:
       * Create and register a shared allocator with the env using the ```CreateAndRegisterAllocator``` API. This allocator is then reused by all sessions that use the same env instance unless a session
 chooses to override this by setting ```session_state.use_env_allocators``` to "0".

docs/get-started/with-python.md

@@ -281,4 +281,4 @@ For Python compiler version notes, see [this page](https://github.com/microsoft/
 - [Python Tutorials](../tutorials/api-basics)
 * [TensorFlow with ONNX Runtime](../tutorials/tf-get-started.md)
 * [PyTorch with ONNX Runtime](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html)
-* [scikit-learn with ONNX Runtime](http://onnx.ai/sklearn-onnx/index_tutorial.html)
+* [scikit-learn with ONNX Runtime](https://onnx.ai/sklearn-onnx/index_tutorial.html)