[Docs] Port release docs (#26689)

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Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
Co-authored-by: Alexander Suvorov <alexander.suvorov@intel.com>

docs/articles_en/about-openvino/key-features.rst

@@ -5,65 +5,65 @@ Easy Integration
 #########################
 
 | :doc:`Support for multiple frameworks <../openvino-workflow/model-preparation/convert-model-to-ir>`
-| Use deep learning models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle, and ONNX
- directly or convert them to the optimized OpenVINO IR format for improved performance.
+|  Use deep learning models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle, and ONNX
+  directly or convert them to the optimized OpenVINO IR format for improved performance.
 
 | :doc:`Close integration with PyTorch <../openvino-workflow/torch-compile>`
-| For PyTorch-based applications, specify OpenVINO as a backend using
- :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
- OpenVINO optimizations to your PyTorch models directly with a single line of code.
+|  For PyTorch-based applications, specify OpenVINO as a backend using
+  :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
+  OpenVINO optimizations to your PyTorch models directly with a single line of code.
 
 | :doc:`GenAI Out Of The Box <../learn-openvino/llm_inference_guide/genai-guide>`
-| With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
- Check out the GenAI guide for instructions on how to do it.
+|  With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
+  Check out the GenAI guide for instructions on how to do it.
 
 | `Python / C++ / C / NodeJS APIs <https://docs.openvino.ai/2024/api/api_reference.html>`__
-| OpenVINO offers the C++ API as a complete set of available methods. For less resource-critical
- solutions, the Python API provides almost full coverage, while C and NodeJS ones are limited
- to the methods most basic for their typical environments. The NodeJS API, is still in its
- early and active development.
+|  OpenVINO offers the C++ API as a complete set of available methods. For less resource-critical
+  solutions, the Python API provides almost full coverage, while C and NodeJS ones are limited
+  to the methods most basic for their typical environments. The NodeJS API, is still in its
+  early and active development.
 
 | :doc:`Open source and easy to extend <../about-openvino/contributing>`
-| If you need a particular feature or inference accelerator to be supported, you are free to file
- a feature request or develop new components specific to your projects yourself. As open source,
- OpenVINO may be used and modified freely. See the extensibility guide for more information on
- how to adapt it to your needs.
+|  If you need a particular feature or inference accelerator to be supported, you are free to file
+  a feature request or develop new components specific to your projects yourself. As open source,
+  OpenVINO may be used and modified freely. See the extensibility guide for more information on
+  how to adapt it to your needs.
 
 Deployment
 #########################
 
 | :doc:`Local or remote <../openvino-workflow>`
-| Integrate the OpenVINO runtime directly with your application to run inference locally or use
- `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ to shift the inference
- workload to a remote system, a separate server or a Kubernetes environment. For serving,
- OpenVINO is also integrated with `vLLM <https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html>`__
- and `Triton <https://github.com/triton-inference-server/openvino_backend>`__ services.
+|  Integrate the OpenVINO runtime directly with your application to run inference locally or use
+  `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ to shift the inference
+  workload to a remote system, a separate server or a Kubernetes environment. For serving,
+  OpenVINO is also integrated with `vLLM <https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html>`__
+  and `Triton <https://github.com/triton-inference-server/openvino_backend>`__ services.
 
 | :doc:`Scalable and portable <release-notes-openvino/system-requirements>`
-| Write an application once, deploy it anywhere, always making the most out of your hardware setup.
- The automatic device selection mode gives you the ultimate deployment flexibility on all major
- operating systems. Check out system requirements.
+|  Write an application once, deploy it anywhere, always making the most out of your hardware setup.
+  The automatic device selection mode gives you the ultimate deployment flexibility on all major
+  operating systems. Check out system requirements.
 
 | **Light-weight**
-| Designed with minimal external dependencies, OpenVINO does not bloat your application
- and simplifies installation and dependency management. The custom compilation for your specific
- model(s) may further reduce the final binary size.
+|  Designed with minimal external dependencies, OpenVINO does not bloat your application
+  and simplifies installation and dependency management. The custom compilation for your specific
+  model(s) may further reduce the final binary size.
 
 Performance
 #########################
 
 | :doc:`Model Optimization <../openvino-workflow/model-optimization>`
-| Optimize your deep learning models with NNCF, using various training-time and post-training
- compression methods, such as pruning, sparsity, quantization, and weight compression. Make
- your models take less space, run faster, and use less resources.
+|  Optimize your deep learning models with NNCF, using various training-time and post-training
+  compression methods, such as pruning, sparsity, quantization, and weight compression. Make
+  your models take less space, run faster, and use less resources.
 
 | :doc:`Top performance <../about-openvino/performance-benchmarks>`
-| OpenVINO is optimized to work with Intel hardware, delivering confirmed high performance for
- hundreds of models. Explore OpenVINO Performance Benchmarks to discover the optimal hardware
- configurations and plan your AI deployment based on verified data.
+|  OpenVINO is optimized to work with Intel hardware, delivering confirmed high performance for
+  hundreds of models. Explore OpenVINO Performance Benchmarks to discover the optimal hardware
+  configurations and plan your AI deployment based on verified data.
 
 | :doc:`Enhanced App Start-Up Time <../openvino-workflow/running-inference/optimize-inference>`
-| If you need your application to launch immediately, OpenVINO will reduce first-inference latency,
- running inference on CPU until a more suited device is ready to take over. Once a model
- is compiled for inference, it is also cached, improving the start-up time even more.
+|  If you need your application to launch immediately, OpenVINO will reduce first-inference latency,
+  running inference on CPU until a more suited device is ready to take over. Once a model
+  is compiled for inference, it is also cached, improving the start-up time even more.
 

docs/articles_en/about-openvino/release-notes-openvino.rst

@@ -15,7 +15,7 @@ OpenVINO Release Notes
 
 
 
-2024.4 - 18 September 2024
+2024.4 - 19 September 2024
 #############################
 
 :doc:`System Requirements <./release-notes-openvino/system-requirements>` | :doc:`Release policy <./release-notes-openvino/release-policy>` | :doc:`Installation Guides <./../get-started/install-openvino>`
@@ -203,15 +203,15 @@ OpenVINO Model Server
  * Ability to compress the KV Cache to a lower precision, reducing memory consumption without
  a significant loss of accuracy.
  * ``stop`` sampling parameters, to define a sequence that stops text generation.
- * ``logprobs`` sampling parameter, returning the probabilities to returned tokens, which can
- be used to calculate the model perplexity metric, among other things.
+ * ``logprobs`` sampling parameter, returning the probabilities to returned tokens.
  * Generic metrics related to execution of the MediaPipe graph that can be used for autoscaling
  based on the current load and the level of concurrency.
  * `Demo of text generation horizontal scalability <https://github.com/openvinotoolkit/model_server/tree/main/demos/continuous_batching/scaling>`__
  using basic docker containers and Kubernetes.
  * Automatic cancelling of text generation for disconnected clients.
  * Non-UTF-8 responses from the model can be now automatically changed to Unicode replacement
  characters, due to their configurable handling.
+ * Intel GPU with paged attention is now supported.
  * Support for Llama3.1 models.
 
 * The following has been improved:
@@ -301,6 +301,24 @@ Known Issues
 | OpenVINO.GenAI archive doesn't have debug libraries for OpenVINO Tokenizers and
  OpenVINO.GenAI.
 
+| **Component: ONNX for ARM**
+| ID: n/a
+| Description:
+| For ARM binaries, the `1.16 ONNX library <https://vcpkg.link/ports/onnx/versions>`__
+ is not yet available, while the current latest has shown two significant vulnerabilities:
+ `CVE-2024-27318 <https://nvd.nist.gov/vuln/detail/CVE-2024-27318>`__ and
+ `CVE-2024-27319 <https://nvd.nist.gov/vuln/detail/CVE-2024-27319>`__.
+ The vulnerabilities are less severe in the context of OpenVINO and will be fixed as soon as
+ the most recent version of the library is available for ARM, expected at the 2024.5 release.
+
+
+
+
+
+
+
+
+
 
 
 

docs/articles_en/get-started/install-openvino.rst

@@ -21,7 +21,7 @@ Install OpenVINO™ 2024.4
 
  <script type="module" crossorigin src="../_static/selector-tool/assets/index-f34d1fad.js"></script>
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <iframe id="selector" src="../_static/selector-tool/selector-9dbd5b1.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
+ <iframe id="selector" src="../_static/selector-tool/selector-8d4cf1d.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
 
 OpenVINO 2024.4, described here, is not a Long-Term-Support version!
 All currently supported versions are: