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Copy file name to clipboardexpand all lines: docs/articles_en/about-openvino.rst
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Along with the primary components of model optimization and runtime, the toolkit also includes:
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* `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ - a tool for enhanced OpenVINO™ inference to get performance boost with minimal accuracy drop.
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* :doc:`Openvino Notebooks <learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
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* :doc:`Openvino Notebooks <get-started/learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
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* `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ - a server that enables scalability via a serving microservice.
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* :doc:`OpenVINO Training Extensions <documentation/openvino-ecosystem/openvino-training-extensions>` – a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
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* :doc:`Dataset Management Framework (Datumaro) <documentation/openvino-ecosystem/datumaro>` - a tool to build, transform, and analyze datasets.
Copy file name to clipboardexpand all lines: docs/articles_en/about-openvino/performance-benchmarks/getting-performance-numbers.rst
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When comparing OpenVINO Runtime performance with the framework or reference code,
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make sure that both versions are as similar as possible:
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* Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../learn-openvino/openvino-samples/benchmark-tool>`).
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* Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`).
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* Do not include model loading time.
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* Ensure that the inputs are identical for OpenVINO Runtime and the framework. For example, watch out for random values that can be used to populate the inputs.
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* In situations when any user-side pre-processing should be tracked separately, consider :doc:`image pre-processing and conversion <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing>`.
Copy file name to clipboardexpand all lines: docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst
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Inference
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The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../learn-openvino/openvino-samples/benchmark-tool>`.
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The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
Copy file name to clipboardexpand all lines: docs/articles_en/get-started.rst
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:hidden:
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Install OpenVINO <get-started/install-openvino>
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Learn OpenVINO <get-started/learn-openvino>
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System Requirements <./about-openvino/release-notes-openvino/system-requirements>
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Interactive Tutorials - Jupyter Notebooks
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-----------------------------------------
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Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
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Start with :doc:`interactive Python <get-started/learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
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* `Hello Image Classification <notebooks/hello-world-with-output.html>`__ - Load an image classification model in OpenVINO and use it to apply a label to an image
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* `OpenVINO Runtime API Tutorial <notebooks/openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
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OpenVINO Code Samples
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---------------------
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View :doc:`sample code <learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
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View :doc:`sample code <get-started/learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <get-started/learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
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.. _integrate-openvino:
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* :doc:`Automatic Batching <openvino-workflow/running-inference/inference-devices-and-modes/automatic-batching>` performs on-the-fly grouping of inference requests to maximize utilization of the target hardware’s memory and processing cores.
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* :doc:`Performance Hints <openvino-workflow/running-inference/optimize-inference/high-level-performance-hints>` automatically adjust runtime parameters to prioritize for low latency or high throughput
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* :doc:`Dynamic Shapes <openvino-workflow/running-inference/dynamic-shapes>` reshapes models to accept arbitrarily-sized inputs, increasing flexibility for applications that encounter different data shapes
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* :doc:`Benchmark Tool <learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
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* :doc:`Benchmark Tool <get-started/learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
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Visit the :doc:`Tutorials <../../../get-started/learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
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* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
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* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
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.. tab-item:: Get started with C++
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:sync: get-started-cpp
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Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions
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Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions
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on building and running a basic image classification C++ application.
Visit the :doc:`Samples <../../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
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Visit the :doc:`Samples <../../../get-started/learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
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Visit the :doc:`Tutorials <../../../get-started/learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
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* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
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* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
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.. tab-item:: Get started with C++
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Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
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Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
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