Fixing broken links

Signed-off-by: Sebastian Golebiewski <[email protected]>
openvinotoolkit · Nov 13, 2024 · 63bdc3e · 63bdc3e
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diff --git a/...rticles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst b/...rticles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
@@ -58,11 +58,11 @@ Performance Information F.A.Q.
         - Hugginface
         - Causal Decoder-only
         - 2048
-      * - `Llama-2-7b-chat <https://ai.meta.com/llama/>`__
+      * - `Llama-2-7b-chat <https://www.llama.com/>`__
         - Meta AI
         - Auto regressive language
         - 4096
-      * - `Llama-3-8b <https://ai.meta.com/llama/>`__
+      * - `Llama-3-8b <https://www.llama.com/>`__
         - Meta AI
         - Auto regressive language
         - 8192
@@ -74,7 +74,7 @@ Performance Information F.A.Q.
         - Huggingface
         - Auto regressive language
         - 4096
-      * - `Stable-Diffusion-V1-5 <https://https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5>`__
+      * - `Stable-Diffusion-V1-5 <https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5>`__
         - Hugginface
         - Latent Diffusion Model
         - 77
@@ -118,7 +118,7 @@ Performance Information F.A.Q.
         - YOLO V5 Medium
         - object detection
         - 640x640
-      * - `yolov8n <https://https://github.com/ultralytics/ultralytics>`__
+      * - `yolov8n <https://github.com/ultralytics/ultralytics>`__
         - Yolov8nano
         - object detection
         - 608x608

diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst
diff --git a/docs/articles_en/documentation/legacy-features.rst b/docs/articles_en/documentation/legacy-features.rst
@@ -96,7 +96,7 @@ Discontinued:
 
    |   *New solution:* API 2.0 launched in OpenVINO 2022.1
    |   *Old solution:* discontinued with OpenVINO 2024.0
-   |      `The last version supporting API 1.0 <https://docs.openvino.ai/2023.2/openvino_2_0_transition_guide.html>`__
+   |      `2023.2 is the last version supporting API 1.0 <https://docs.openvino.ai/archives/index.html#:~:text=2023.2,Release%20Notes>`__
 
 .. dropdown:: Compile tool
 

diff --git a/...egacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst b/...egacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats.rst
@@ -120,7 +120,7 @@ Here are code examples of how to use these methods with different model formats:
 
               For more details on conversion, refer to the
               :doc:`guide <[legacy]-supported-model-formats/[legacy]-convert-tensorflow>`
-              and an example `tutorial <https://docs.openvino.ai/2024/notebooks/tensorflow-to-openvino-with-output.html>`__
+              and an example `tutorial <https://docs.openvino.ai/2024/notebooks/tensorflow-classification-to-openvino-with-output.html>`__
               on this topic.
 
             * The ``read_model()`` and ``compile_model()`` methods:
@@ -592,7 +592,7 @@ to OpenVINO IR or ONNX before running inference should be considered the default
    OpenVINO versions of 2023 are mostly compatible with the old instructions,
    through a deprecated MO tool, installed with the deprecated OpenVINO Developer Tools package.
 
-   `OpenVINO 2023.0 <https://docs.openvino.ai/archive/2023.0/Supported_Model_Formats.html>`__ is the last
+   `OpenVINO 2023.0 <https://docs.openvino.ai/archives/index.html>`__ is the last
    release officially supporting the MO conversion process for the legacy formats.
 
 
diff --git a/...orted-model-formats/[legacy]-conversion-tutorials/convert-onnx-faster-r-cnn.rst b/...orted-model-formats/[legacy]-conversion-tutorials/convert-onnx-faster-r-cnn.rst
@@ -14,7 +14,7 @@ Converting an ONNX Faster R-CNN Model
 
 The instructions below are applicable **only** to the Faster R-CNN model converted to the ONNX file format from the `maskrcnn-benchmark model <https://github.com/facebookresearch/maskrcnn-benchmark>`__:
 
-1. Download the pretrained model file from `onnx/models <https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn>`__ (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
+1. Download the pretrained model file from `onnx/models <https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/faster-rcnn>`__ (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
 
 2. Generate the Intermediate Representation of the model, by changing your current working directory to the model conversion API installation directory, and running model conversion with the following parameters:
 

diff --git a/...y]-supported-model-formats/[legacy]-conversion-tutorials/convert-onnx-gpt-2.rst b/...y]-supported-model-formats/[legacy]-conversion-tutorials/convert-onnx-gpt-2.rst
@@ -12,7 +12,7 @@ Converting an ONNX GPT-2 Model
 
    This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <../../../../../../learn-openvino/interactive-tutorials-python>`.
 
-`Public pre-trained GPT-2 model <https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2>`__ is a large
+`Public pre-trained GPT-2 model <https://github.com/onnx/models/tree/main/validated/text/machine_comprehension/gpt-2>`__ is a large
 transformer-based language model with a simple objective: predict the next word, given all of the previous words within some text.
 
 Downloading the Pre-Trained Base GPT-2 Model

diff --git a/...rted-model-formats/[legacy]-conversion-tutorials/convert-pytorch-quartz-net.rst b/...rted-model-formats/[legacy]-conversion-tutorials/convert-pytorch-quartz-net.rst
@@ -20,7 +20,7 @@ Downloading the Pre-trained QuartzNet Model
 To download the pre-trained model, refer to the `NeMo Speech Models Catalog <https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels>`__.
 Here are the instructions on how to obtain QuartzNet in ONNX format.
 
-1. Install the NeMo toolkit, using the `instructions <https://github.com/NVIDIA/NeMo/tree/main#installation>`__.
+1. Install the NeMo toolkit, using the `instructions <https://github.com/NVIDIA/NeMo/blob/main/README.md#install-nemo-framework>`__.
 
 2. Run the following code:
 

diff --git a/...supported-model-formats/[legacy]-conversion-tutorials/convert-pytorch-rnn-t.rst b/...supported-model-formats/[legacy]-conversion-tutorials/convert-pytorch-rnn-t.rst
@@ -44,7 +44,7 @@ For UNIX-like systems, you can use ``wget``:
 
 
 The link was taken from ``setup.sh`` in the ``speech_recoginitin/rnnt`` subfolder. You will get exactly the same weights as
-if you were following the `guide <https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt>`__.
+if you were following the `guide <https://github.com/mlcommons/inference/tree/master/retired_benchmarks/speech_recognition/rnnt>`__.
 
 **Step 4**. Install required Python packages:
 

diff --git a/...conversion-api/[legacy]-supported-model-formats/[legacy]-convert-tensorflow.rst b/...conversion-api/[legacy]-supported-model-formats/[legacy]-convert-tensorflow.rst
@@ -59,7 +59,7 @@ To convert such TensorFlow model, run the `mo` script with a path to the MetaGra
 
 
 3. **SavedModel format**. In this case, a model consists of a special directory with a ``.pb`` file
-and several subfolders: ``variables``, ``assets``, and ``assets.extra``. For more information about the SavedModel directory, refer to the `README <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/saved_model#components>`__ file in the TensorFlow repository.
+and several subfolders: ``variables``, ``assets``, and ``assets.extra``. For more information about the SavedModel directory, refer to the `README <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/saved_model>`__ file in the TensorFlow repository.
 To convert such TensorFlow model, run the ``mo`` script with a path to the SavedModel directory:
 
 .. code-block:: sh

diff --git a/...tures/transition-legacy-conversion-api/legacy-model-optimizer-extensibility.rst b/...tures/transition-legacy-conversion-api/legacy-model-optimizer-extensibility.rst
@@ -160,7 +160,7 @@ It is important to mention that sometimes it seems like transformation cannot be
 because the actual values of inputs or shapes are needed. In fact, manipulations of shapes or values can be implemented
 using operations that are added to the graph. Consider the
 ``extensions/front/onnx/flattenONNX_to_reshape.py`` transformation, which replaces an ONNX
-`Flatten <https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten>`__ operation with a sub-graph of operations performing
+`Flatten <https://github.com/onnx/onnx/blob/main/docs/Operators.md#Flatten>`__ operation with a sub-graph of operations performing
 the following (when ``axis`` is not equal to 0 and 1):
 
 1. Calculate a shape of the ``Flatten`` input tensor, using the :doc:`ShapeOf <../../openvino-ir-format/operation-sets/operation-specs/shape/shape-of-3>` operation.

diff --git a/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst b/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst
@@ -580,7 +580,7 @@ Building OpenVINO™ Security Add-on depends on OpenVINO™ Model Server docker
 
 1. Download the `OpenVINO™ Model Server software <https://github.com/openvinotoolkit/model_server>`__
 
-2. Build the `OpenVINO™ Model Server Docker images <https://github.com/openvinotoolkit/model_server/blob/main/docs/docker_container.md>`__
+2. Build the `OpenVINO™ Model Server Docker images <https://github.com/openvinotoolkit/model_server/blob/main/docs/developer_guide.md#step-1-compile-source-code>`__
 
    .. code-block:: sh
 

diff --git a/docs/articles_en/documentation/openvino-ecosystem/openvino-training-extensions.rst b/docs/articles_en/documentation/openvino-ecosystem/openvino-training-extensions.rst
@@ -32,9 +32,9 @@ If the results are unsatisfactory, add datasets and perform the same steps, star
 OpenVINO Training Extensions Components
 #######################################
 
-* `OpenVINO Training Extensions API <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/api>`__
+* `OpenVINO Training Extensions API <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/>`__
 * `OpenVINO Training Extensions CLI <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/cli>`__
-* `OpenVINO Training Extensions Algorithms <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/algorithms>`__
+* `OpenVINO Training Extensions Algorithms <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/algo>`__
 
 Tutorials
 #########

diff --git a/...ility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst b/...ility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst
@@ -35,7 +35,7 @@ The goal of Low Precision Transformations (LPT) is to transform a quantized mode
 
 As result, operation input tensor precisions will be changed from original to low precision and operations can be inferred by OpenVINO™ plugin in low precision.
 
-For a more detailed description on how to quantize a model, see the `Low precision tools <#low-precision-tools>`__ section below. For more information about model quantization, refer to **Brief History of Lower Precision in Deep Learning** section in `this whitepaper <https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training>`__.
+For a more detailed description on how to quantize a model, see the `Low precision tools <#low-precision-tools>`__ section below. For more information about model quantization, refer to **Brief History of Lower Precision in Deep Learning** section in `this whitepaper <https://www.intel.com/content/dam/develop/external/us/en/documents/lower-numerical-precision-deep-learning-jan2018-754765.pdf>`__.
 
 Input model requirements
 ########################

diff --git a/...ion/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst b/...ion/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst
@@ -11,7 +11,7 @@ Loop
 **Category**: *Infrastructure*
 
 **Short description**: *Loop* operation performs recurrent execution of the network, which is described in the ``body``, iterating through the data.
-The operation has similar semantic to the ONNX Loop `operation <https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Loop-13>`__.
+The operation has similar semantic to the ONNX Loop `operation <https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Loop-13>`__.
 
 **Detailed description**
 
@@ -73,7 +73,7 @@ Loop operation description in the IR also has several special sections: ``body``
 1. The body operation getting an input from the main graph should have an entry in the ``port_map`` section of the Loop operation. These edges connect input ports of the Loop with the body ``Parameter``\ s.
 2. Input tensors to the Loop can be sliced along a specified axis, the Loop can iterates over all sliced parts. The corresponding ``input`` entry in the ``port_map`` should have ``axis`` attribute specifying the axis to slice. Therefore, inputs to the Loop operation corresponding to ``input`` entries in the ``port_map`` without ``axis`` attribute are used "as is" (without slicing).
 3. The body operation producing tensor to be used in the subsequent iterations (like in RNN models) should have a back edge described in the ``back_edges`` section of the operation. The back edge connects the respective body ``Parameter`` and ``Result`` operations. For such a case the Loop operation node provides input for the first iteration, while corresponding Loop operation output produces the tensor computed during the last iteration.
-4. Output tensors produced by a particular body operation across all iterations can be concatenated and returned as a Loop operation output (this is a "scan output" according to the ONNX* Loop operation `specification <https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Loop-13>`__ ). The corresponding ``output`` entry in the ``port_map`` should have ``axis`` attribute specifying the axis to concatenate. Therefore, outputs from operations corresponding to ``output`` entries in the ``port_map`` without ``axis`` attribute are returned "as is" (without concatenation).
+4. Output tensors produced by a particular body operation across all iterations can be concatenated and returned as a Loop operation output (this is a "scan output" according to the ONNX* Loop operation `specification <https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Loop-13>`__ ). The corresponding ``output`` entry in the ``port_map`` should have ``axis`` attribute specifying the axis to concatenate. Therefore, outputs from operations corresponding to ``output`` entries in the ``port_map`` without ``axis`` attribute are returned "as is" (without concatenation).
 5. There is one body ``Parameter`` operation not connected through the ``port_map``. This is a "current iteration" input. The Loop operation is responsible for providing the appropriate value for each iteration.
 6. Connection of nodes inside the Loop body with the main graph should be done through ``Parameter`` and ``Result`` body operations. No other ways to connect graphs are allowed.
 

diff --git a/...ation/openvino-ir-format/operation-sets/operation-specs/sequence/gru-cell-3.rst b/...ation/openvino-ir-format/operation-sets/operation-specs/sequence/gru-cell-3.rst
@@ -64,7 +64,7 @@ GRUCell
 * *linear_before_reset*
 
   * **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification
-    of *GRUCell* described in the formula in the `ONNX documentation <https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU>`__.
+    of *GRUCell* described in the formula in the `ONNX documentation <https://github.com/onnx/onnx/blob/main/docs/Operators.md#GRU>`__.
   * **Range of values**: true or false
   * **Type**: ``boolean``
   * **Default value**: false

diff --git a/...n/openvino-ir-format/operation-sets/operation-specs/sequence/gru-sequence-5.rst b/...n/openvino-ir-format/operation-sets/operation-specs/sequence/gru-sequence-5.rst
@@ -19,7 +19,7 @@ represents a sequence of GRU cells. The sequence can be connected differently de
 ``direction`` attribute that specifies the direction of traversing of input data along sequence
 dimension or specifies whether it should be a bidirectional sequence. The most of the attributes
 are in sync with the specification of ONNX GRU operator defined
-`GRUCell <https://github.com/onnx/onnx/blob/master/docs/Operators.md#gru>`__
+`GRUCell <https://github.com/onnx/onnx/blob/main/docs/Operators.md#gru>`__
 
 
 **Attributes**
@@ -69,7 +69,7 @@ are in sync with the specification of ONNX GRU operator defined
 * *linear_before_reset*
 
   * **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification
-    of *GRUCell* described in the formula in the `ONNX documentation <https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU>`__.
+    of *GRUCell* described in the formula in the `ONNX documentation <https://github.com/onnx/onnx/blob/main/docs/Operators.md#GRU>`__.
   * **Range of values**: True or False
   * **Type**: ``boolean``
   * **Default value**: False

diff --git a/.../openvino-ir-format/operation-sets/operation-specs/sequence/lstm-sequence-5.rst b/.../openvino-ir-format/operation-sets/operation-specs/sequence/lstm-sequence-5.rst
@@ -14,7 +14,7 @@ LSTMSequence
 
 **Detailed description**
 
-A single cell in the sequence is implemented in the same way as in :doc:`LSTM Cell <lstm-cell-1>` operation. *LSTMSequence* represents a sequence of LSTM cells. The sequence can be connected differently depending on ``direction`` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX LSTM operator defined `LSTMCell <https://github.com/onnx/onnx/blob/master/docs/Operators.md#lstm>`__ .
+A single cell in the sequence is implemented in the same way as in :doc:`LSTM Cell <lstm-cell-1>` operation. *LSTMSequence* represents a sequence of LSTM cells. The sequence can be connected differently depending on ``direction`` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX LSTM operator defined `LSTMCell <https://github.com/onnx/onnx/blob/main/docs/Operators.md#lstm>`__ .
 
 
 **Attributes**

diff --git a/...n/openvino-ir-format/operation-sets/operation-specs/sequence/rnn-sequence-5.rst b/...n/openvino-ir-format/operation-sets/operation-specs/sequence/rnn-sequence-5.rst
@@ -14,7 +14,7 @@ RNNSequence
 
 **Detailed description**
 
-A single cell in the sequence is implemented in the same way as in :doc:`RNNCell <rnn-cell-3>` operation. *RNNSequence* represents a sequence of RNN cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX RNN operator defined `RNNCell <https://github.com/onnx/onnx/blob/master/docs/Operators.md#rnn>`__.
+A single cell in the sequence is implemented in the same way as in :doc:`RNNCell <rnn-cell-3>` operation. *RNNSequence* represents a sequence of RNN cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX RNN operator defined `RNNCell <https://github.com/onnx/onnx/blob/main/docs/Operators.md#rnn>`__.
 
 
 **Attributes**

diff --git a/docs/articles_en/get-started/configurations/configurations-intel-gpu.rst b/docs/articles_en/get-started/configurations/configurations-intel-gpu.rst
@@ -37,7 +37,7 @@ Below are the instructions on how to install the OpenCL packages on supported Li
       and install the apt package `ocl-icd-libopencl1` with the OpenCl ICD loader.
 
       Alternatively, you can add the apt repository by following the
-      `installation guide <https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps>`__.
+      `installation guide <https://dgpu-docs.intel.com/driver/installation.html#ubuntu>`__.
       Then install the `ocl-icd-libopencl1`, `intel-opencl-icd`, `intel-level-zero-gpu` and `level-zero`
       apt packages:
 

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst b/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst
@@ -108,6 +108,6 @@ Additional Resources
 - `Official Yocto Project documentation <https://docs.yoctoproject.org/>`__
 - `BitBake Tool <https://docs.yoctoproject.org/bitbake/>`__
 - `Poky <https://git.yoctoproject.org/poky>`__
-- `Meta-intel <https://git.yoctoproject.org/meta-intel/tree/README>`__
+- `Meta-intel <https://git.yoctoproject.org/meta-intel/tree/README.md>`__
 - `Meta-openembedded <http://cgit.openembedded.org/meta-openembedded/tree/README.md>`__
 - `Meta-clang <https://github.com/kraj/meta-clang/tree/master/#readme>`__
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst
@@ -31,8 +31,8 @@ some examples of popular Generative AI scenarios:
 
 To write such pipelines, you can follow the examples provided as part of OpenVINO:
 
-* `OpenVINO Latent Consistency Model C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/master/image_generation/lcm_dreamshaper_v7/cpp>`__
-* `OpenVINO Stable Diffusion (with LoRA) C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/master/image_generation/stable_diffusion_1_5/cpp>`__
+* `OpenVINO Latent Consistency Model C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/releases/2024/4/image_generation/lcm_dreamshaper_v7/cpp>`__
+* `OpenVINO Stable Diffusion (with LoRA) C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/releases/2024/4/image_generation/stable_diffusion_1_5/cpp>`__
 
 To perform inference, models must be first converted to OpenVINO IR format using
 Hugging Face Optimum-Intel API.

diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst
@@ -336,7 +336,7 @@ Additional Resources
 
 * `OpenVINO Tokenizers repo <https://github.com/openvinotoolkit/openvino_tokenizers>`__
 * `OpenVINO Tokenizers Notebook <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/openvino-tokenizers>`__
-* `Text generation C++ samples that support most popular models like LLaMA 2 <https://github.com/openvinotoolkit/openvino.genai/tree/master/text_generation/causal_lm/cpp>`__
+* `Text generation C++ samples that support most popular models like LLaMA 3 <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/greedy_causal_lm>`__
 * `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__