[DOCS] Port release docs to master

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

@@ -22,15 +22,13 @@ Install OpenVINO™ 2025.1
 
    <script type="module" crossorigin src="../_static/selector-tool/assets/index-Codcw3jz.js"></script>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-   <iframe id="selector" src="../_static/selector-tool/selector-73890fe.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
+   <iframe id="selector" src="../_static/selector-tool/selector-e4b375c.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
 
 OpenVINO 2025.1, described here, is not a Long-Term-Support version!
 All currently supported versions are:
 
 * 2025.1 (development)
 * 2024.6 (maintenance)
-* 2023.3 (LTS), it will be deprecated at the end of 2025.
-
 
 .. dropdown:: Effortless GenAI integration with OpenVINO GenAI
 

docs/articles_en/get-started/install-openvino/configurations/configurations-intel-gpu.rst

@@ -133,7 +133,7 @@ Additional Resources
 ####################
 
 * :doc:`GPU Device <../../../openvino-workflow/running-inference/inference-devices-and-modes/gpu-device>`
-* :doc:`Install Intel® Distribution of OpenVINO™ toolkit from a Docker Image <../install-openvino-archive-linux>`
+* :doc:`Install Intel® Distribution of OpenVINO™ toolkit from a Docker Image <../install-openvino-docker-linux>`
 * `Docker CI framework for Intel® Distribution of OpenVINO™ toolkit <https://github.com/openvinotoolkit/docker_ci/blob/master/README.md>`__
 * `Get Started with DockerHub CI for Intel® Distribution of OpenVINO™ toolkit <https://github.com/openvinotoolkit/docker_ci/blob/master/get-started.md>`__
 * `Dockerfiles with Intel® Distribution of OpenVINO™ toolkit <https://github.com/openvinotoolkit/docker_ci/blob/master/dockerfiles/README.md>`__

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

@@ -12,7 +12,7 @@ Install OpenVINO™ Runtime from Conda Forge
    Note that the Conda Forge distribution:
 
    * offers both C/C++ and Python APIs
-   * does not offer support for NPU inference
+   * supports NPU inference on Linux only
    * is dedicated to users of all major OSes: Windows, Linux, and macOS
      (all x86_64 / arm64 architectures)
 

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

@@ -24,10 +24,12 @@ You can get started easily with pre-built and published docker images, which are
 You can use the `available Dockerfiles on GitHub <https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles>`__
 or generate a Dockerfile with your settings via `DockerHub CI framework <https://github.com/openvinotoolkit/docker_ci/>`__,
 which can generate a Dockerfile, build, test, and deploy an image using the Intel® Distribution of OpenVINO™ toolkit.
+
 You can reuse available Dockerfiles, add your layer and customize the OpenVINO™ image to your needs.
-The Docker CI repository includes guides on how to
-`get started with docker images <https://github.com/openvinotoolkit/docker_ci/blob/master/get-started.md>`__ and how to use
-`OpenVINO™ Toolkit containers with GPU accelerators. <https://github.com/openvinotoolkit/docker_ci/blob/master/docs/accelerators.md>`__
+The Docker CI repository includes the following guides:
+
+* `Get started with docker images <https://github.com/openvinotoolkit/docker_ci/blob/master/get-started.md>`__ 
+*  How to use OpenVINO™ Toolkit containers with `GPU accelerators <https://github.com/openvinotoolkit/docker_ci/blob/master/docs/accelerators.md>`__ and `NPU accelerators <https://github.com/openvinotoolkit/docker_ci/blob/master/docs/npu_accelerator.md>`__.
 
 To start using Dockerfiles, install Docker Engine or a compatible container
 engine on your system:

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

@@ -11,7 +11,6 @@ Install OpenVINO™ Runtime on Linux From YUM Repository
    Note that the YUM distribution:
 
    * offers both C/C++ and Python APIs
-   * does not offer support for NPU inference
    * is dedicated to Linux users only
    * additionally includes code samples
 

docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes.rst

@@ -15,33 +15,31 @@ Inference Devices and Modes
    inference-devices-and-modes/query-device-properties
 
 
-The OpenVINO runtime offers multiple inference modes to enable the best hardware utilization under
-different conditions:
+The OpenVINO™ Runtime offers several inference modes to optimize hardware usage.
+You can run inference on a single device or use automated modes that manage multiple devices:
 
 | **single-device inference**
-|    Define just one device responsible for the entire inference workload. It supports a range of
-     processors by means of the following plugins embedded in the Runtime library:
+|    This mode runs all inference on one selected device. The OpenVINO Runtime includes 
+     built-in plugins that support the following devices:
 |    :doc:`CPU <inference-devices-and-modes/cpu-device>`
 |    :doc:`GPU <inference-devices-and-modes/gpu-device>`
 |    :doc:`NPU <inference-devices-and-modes/npu-device>`
 
 | **automated inference modes**
-|    Assume certain level of automation in selecting devices for inference. They may potentially
-     increase your deployed solution's performance and portability. The automated modes are:
+|    These modes automate device selection and workload distribution, potentially increasing 
+     performance and portability:
 |    :doc:`Automatic Device Selection (AUTO) <inference-devices-and-modes/auto-device-selection>`
-|    :doc:`Heterogeneous Execution (HETERO) <inference-devices-and-modes/hetero-execution>`
-|    :doc:`Automatic Batching Execution (Auto-batching) <inference-devices-and-modes/automatic-batching>`
+|    :doc:`Heterogeneous Execution (HETERO) <inference-devices-and-modes/hetero-execution>`  across different device types
+|    :doc:`Automatic Batching Execution (Auto-batching) <inference-devices-and-modes/automatic-batching>`: automatically groups inference requests to improve throughput
 
-To learn how to change the device configuration, read the :doc:`Query device properties article <inference-devices-and-modes/query-device-properties>`.
+Learn how to configure devices in the :doc:`Query device properties <inference-devices-and-modes/query-device-properties>` article.
 
 Enumerating Available Devices
 #######################################
 
-The OpenVINO Runtime API features dedicated methods of enumerating devices and their capabilities.
-Note that beyond the typical "CPU" or "GPU" device names, more qualified names are used when multiple
-instances of a device are available (iGPU is always GPU.0).
-The output you receive may look like this (truncated to device names only, two GPUs are listed
-as an example):
+The OpenVINO Runtime API provides methods to list available devices and their details.
+When there are multiple instances of a device, they get specific names like GPU.0 for iGPU.
+Here is an example of the output with device names, including two GPUs:
 
 .. code-block:: sh
 
@@ -54,9 +52,10 @@ as an example):
        Device: GPU.1
 
 
-You may see how to obtain this information in the :doc:`Hello Query Device Sample <../../get-started/learn-openvino/openvino-samples/hello-query-device>`.
-Here is an example of a simple programmatic way to enumerate the devices and use them with the
-multi-device mode:
+See the :doc:`Hello Query Device Sample <../../get-started/learn-openvino/openvino-samples/hello-query-device>`
+for more details.
+
+Below is an example showing how to list available devices and use them with multi-device mode:
 
 .. tab-set::
 
@@ -67,8 +66,8 @@ multi-device mode:
          :language: cpp
          :fragment: [part2]
 
-With two GPU devices used in one setup, the explicit configuration would be "MULTI:GPU.1,GPU.0".
-Accordingly, the code that loops over all available devices of the "GPU" type only is as follows:
+If you have two GPU devices, you can specify them explicitly as “MULTI:GPU.1,GPU.0”. 
+Here is how to list and use all available GPU devices:
 
 .. tab-set::