Docs menu recreate structure step2 port2master (#14552)
model optimization deploy locally run inference remove OVSA from security (it was duplicated)
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Karol Blaszczak
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# Introduction to OpenVINO™ Deployment {#openvino_docs_deployment_guide_introduction}
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Once you have a model that meets both OpenVINO™ and your requirements, you can choose among several ways of deploying it with your application:
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* [Deploy your application locally](../OV_Runtime_UG/deployment/deployment_intro.md).
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* [Deploy your model with OpenVINO Model Server](@ref ovms_what_is_openvino_model_server).
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* [Deploy your application for the TensorFlow framework with OpenVINO Integration](./openvino_ecosystem_ovtf.md).
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> **NOTE**: Note that [running inference in OpenVINO Runtime](../OV_Runtime_UG/openvino_intro.md) is the most basic form of deployment. Before moving forward, make sure you know how to create a proper Inference configuration.
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# Introduction to Model Processing {#openvino_docs_model_processing_introduction}
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# Model Preparation {#openvino_docs_model_processing_introduction}
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@sphinxdirective
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.. toctree::
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:maxdepth: 1
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:hidden:
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Supported_Model_Formats
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openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide
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omz_tools_downloader
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@endsphinxdirective
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Every deep learning workflow begins with obtaining a model. You can choose to prepare a custom one, use a ready-made solution and adjust it to your needs, or even download and run a pre-trained network from an online database, such as OpenVINO's [Open Model Zoo](../model_zoo.md).
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@endsphinxdirective
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Once the [OpenVINO™ application development](../integrate_with_your_application.md) has been finished, application developers usually need to deploy their applications to end users. There are several ways to achieve that:
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Once [OpenVINO™ application development](../integrate_with_your_application.md) has been finished, application developers usually need to deploy their applications to end users. There are several ways to achieve that. This section will explain how you can deploy locally, using OpenVINO Runtime.
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> **NOTE**: Note that [running inference in OpenVINO Runtime](../openvino_intro.md) is the most basic form of deployment. Before moving forward, make sure you know how to create a proper Inference configuration.
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## Local Deployment Options
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- Set a dependency on the existing prebuilt packages, also called "centralized distribution":
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- using Debian / RPM packages - a recommended way for Linux operating systems;
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:hidden:
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openvino_2_0_transition_guide
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API Reference <api/api_reference>
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Model Preparation <openvino_docs_model_processing_introduction>
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Model Optimization and Compression <openvino_docs_model_optimization_guide>
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Run Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide>
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Deploy Locally <openvino_deployment_guide>
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Tool Ecosystem <openvino_ecosystem>
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OpenVINO Extensibility <openvino_docs_Extensibility_UG_Intro>
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Media Processing and CV Libraries <media_processing_cv_libraries>
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OpenVINO™ Security <openvino_docs_security_guide_introduction>
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.. toctree::
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:maxdepth: 1
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:caption: Model preparation
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:hidden:
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openvino_docs_model_processing_introduction
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Supported_Model_Formats
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openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide
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omz_tools_downloader
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.. toctree::
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:maxdepth: 1
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:caption: Running Inference
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:hidden:
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openvino_docs_OV_UG_OV_Runtime_User_Guide
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openvino_inference_engine_tools_compile_tool_README
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openvino_docs_optimization_guide_dldt_optimization_guide
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openvino_docs_MO_DG_Getting_Performance_Numbers
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openvino_docs_model_optimization_guide
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openvino_docs_deployment_optimization_guide_dldt_optimization_guide
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openvino_docs_tuning_utilities
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openvino_docs_performance_benchmarks
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.. toctree::
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:maxdepth: 1
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:caption: Deploying Inference
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:hidden:
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openvino_docs_deployment_guide_introduction
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openvino_deployment_guide
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@endsphinxdirective
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@ -11,6 +11,9 @@ OpenVINO™ Documentation
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.. raw:: html
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<div class="section" id="welcome-to-openvino-toolkit-s-documentation">
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<link rel="stylesheet" type="text/css" href="_static/css/homepage_style.css">
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@ -120,7 +123,6 @@ OpenVINO™ Documentation
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GET STARTED <get_started>
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LEARN OPENVINO <learn_openvino>
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DOCUMENTATION <documentation>
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API REFERENCE <api/api_reference>
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MODEL ZOO <model_zoo>
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RESOURCES <resources>
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RELEASE NOTES <https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html>
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@ -8,7 +8,6 @@
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openvino_docs_security_guide_workbench
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openvino_docs_OV_UG_protecting_model_guide
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ovsa_get_started
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@endsphinxdirective
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@ -18,3 +17,4 @@ Trained models are often valuable intellectual property and you may choose to pr
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Actual security and privacy requirements depend on your unique deployment scenario.
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This section provides general guidance on using OpenVINO tools and libraries securely.
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The main security measure for OpenVINO is its [Security Add-on](../ovsa/ovsa_get_started.md). You can find its description in the Ecosystem section.
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