[DOCS] Small fixes in articles for master (#20947)
* Fixes * Update deployment_intro.md * Update docs/articles_en/openvino_workflow/deployment_intro.md Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com> --------- Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
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@ -28,8 +28,7 @@ Local Deployment Options
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- using PIP package manager on PyPI - the default approach for Python-based applications;
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- using Docker images - if the application should be deployed as a Docker image, use a pre-built OpenVINO™ Runtime Docker image as a base image in the Dockerfile for the application container image. For more information about OpenVINO Docker images, refer to :doc:`Installing OpenVINO from Docker <openvino_docs_install_guides_installing_openvino_docker>`
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Furthermore, to customize your OpenVINO Docker image, use the `Docker CI Framework <https://github.com/openvinotoolkit/docker_ci>`__ to generate a Dockerfile and built the image.
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- Furthermore, to customize your OpenVINO Docker image, use the `Docker CI Framework <https://github.com/openvinotoolkit/docker_ci>`__ to generate a Dockerfile and build the image.
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- Grab a necessary functionality of OpenVINO together with your application, also called "local distribution":
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- using :doc:`OpenVINO Deployment Manager <openvino_docs_install_guides_deployment_manager_tool>` - providing a convenient way for creating a distribution package;
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@ -45,7 +44,7 @@ The table below shows which distribution type can be used for what target operat
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- Operating systems
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* - Debian packages
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- Ubuntu 18.04 long-term support (LTS), 64-bit; Ubuntu 20.04 long-term support (LTS), 64-bit
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* - RMP packages
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* - RPM packages
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- Red Hat Enterprise Linux 8, 64-bit
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* - Docker images
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- Ubuntu 22.04 long-term support (LTS), 64-bit; Ubuntu 20.04 long-term support (LTS), 64-bit; Red Hat Enterprise Linux 8, 64-bit
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@ -76,7 +76,7 @@ Quantization is the process of converting the weights and activation values in a
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Quantization-aware training inserts nodes into the neural network during training that simulate the effect of lower precision. This allows the training algorithm to consider quantization errors as part of the overall training loss that gets minimized during training. The network is then able to achieve enhanced accuracy when quantized.
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The officially supported method of quantization in NNCF is uniform 8-bit quantization. This means all the weights and activation functions in the neural network are converted to 8-bit values. See the :doc:`Quantization-ware Training guide <qat_introduction>` to learn more.
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The officially supported method of quantization in NNCF is uniform 8-bit quantization. This means all the weights and activation functions in the neural network are converted to 8-bit values. See the :doc:`Quantization-aware Training guide <qat_introduction>` to learn more.
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Filter pruning
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--------------------
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