diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt index b3bcbcdba49a4b..cdf601602795d7 100644 --- a/docs/CMakeLists.txt +++ b/docs/CMakeLists.txt @@ -84,7 +84,7 @@ function(build_docs) list(APPEND commands COMMAND ${Python3_EXECUTABLE} ${FILE_HELPER_SCRIPT} --filetype=md --input_dir=${OVMS_DOCS_DIR} - --output_dir=${SPHINX_SOURCE_DIR}/openvino-workflow/model-server + --output_dir=${SPHINX_SOURCE_DIR}/model-server --exclude_dir=${SPHINX_SOURCE_DIR}) list(APPEND commands COMMAND ${CMAKE_COMMAND} -E cmake_echo_color --green "FINISHED preprocessing OVMS") endif() diff --git a/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project.rst b/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project.rst index 894b017174bed3..463edf22c31f8d 100644 --- a/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project.rst +++ b/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project.rst @@ -38,7 +38,7 @@ and TensorFlow models during training. | **OpenVINO Model Server** | :bdg-link-dark:`GitHub ` - :bdg-link-success:`User Guide ` + :bdg-link-success:`User Guide ` A high-performance system that can be used to access the host models via request to the model server. diff --git a/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project/openvino-security-add-on.rst b/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project/openvino-security-add-on.rst index 001942195cbf16..c89d5584b634f0 100644 --- a/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project/openvino-security-add-on.rst +++ b/docs/articles_en/about-openvino/openvino-ecosystem/openvino-project/openvino-security-add-on.rst @@ -17,7 +17,7 @@ In this release, one person performs the role of both the Model Developer and th Overview ######## -The OpenVINO™ Security Add-on works with the :doc:`OpenVINO™ Model Server <../../../openvino-workflow/model-server/ovms_what_is_openvino_model_server>` on Intel® architecture. Together, the OpenVINO™ Security Add-on and the OpenVINO™ Model Server provide a way for Model Developers and Independent Software Vendors to use secure packaging and secure model execution to enable access control to the OpenVINO™ models, and for model Users to run inference within assigned limits. +The OpenVINO™ Security Add-on works with the :doc:`OpenVINO™ Model Server <../../../../model-server/ovms_what_is_openvino_model_server>` on Intel® architecture. Together, the OpenVINO™ Security Add-on and the OpenVINO™ Model Server provide a way for Model Developers and Independent Software Vendors to use secure packaging and secure model execution to enable access control to the OpenVINO™ models, and for model Users to run inference within assigned limits. The OpenVINO™ Security Add-on consists of three components that run in Kernel-based Virtual Machines (KVMs). These components provide a way to run security-sensitive operations in an isolated environment. A brief description of the three components are as follows. Click each triangled line for more information about each. diff --git a/docs/articles_en/about-openvino/performance-benchmarks.rst b/docs/articles_en/about-openvino/performance-benchmarks.rst index 5d414ab4909ecb..7522aae508456f 100644 --- a/docs/articles_en/about-openvino/performance-benchmarks.rst +++ b/docs/articles_en/about-openvino/performance-benchmarks.rst @@ -18,7 +18,7 @@ Performance Benchmarks This page presents benchmark results for the `Intel® Distribution of OpenVINO™ toolkit `__ -and :doc:`OpenVINO Model Server <../openvino-workflow/model-server/ovms_what_is_openvino_model_server>`, for a representative +and :doc:`OpenVINO Model Server <../../model-server/ovms_what_is_openvino_model_server>`, for a representative selection of public neural networks and Intel® devices. The results may help you decide which hardware to use in your applications or plan AI workload for the hardware you have already implemented in your solutions. Click the buttons below to see the chosen benchmark data. diff --git a/docs/articles_en/openvino-workflow-generative.rst b/docs/articles_en/openvino-workflow-generative.rst index bb1e6acf0e56c5..4931092e38c2cc 100644 --- a/docs/articles_en/openvino-workflow-generative.rst +++ b/docs/articles_en/openvino-workflow-generative.rst @@ -55,15 +55,19 @@ options: as well as conversion on the fly. For integration with the final product it may offer lower performance, though. - .. tab-item:: Base OpenVINO (not recommended) + .. tab-item:: OpenVINO™ Model Server - Note that the base version of OpenVINO may also be used to run generative AI. Although it may - offer a simpler environment, with fewer dependencies, it has significant limitations and a more - demanding implementation process. - - To learn more, refer to the article for the 2024.6 OpenVINO version: - `Generative AI with Base OpenVINO `__ + | - Easy and quick deployment of models to edge or cloud. + | - Includes endpoints for serving generative AI models. + | - Available in both Python and C++. + | - Allows client applications in any programming language that supports REST or gRPC. + Deploy deep learning models remotely, using + :doc:`OpenVINO™ Model Server ` + - a high-performance solution, developed in C++ for scalability and optimized for + Intel architectures. The deployment is straightforward, as you simply connect + your application via gRPC or REST endpoints to a server, where OpenVINO's logic for + inference tasks is applied. The advantages of using OpenVINO for generative model deployment: @@ -94,10 +98,13 @@ The advantages of using OpenVINO for generative model deployment: better performance than Python-based runtimes. +You can run Generative AI models, using native OpenVINO API, although it is not recommended. +If you want to learn how to do it, refer to +`the 24.6 documentation `__. + + Proceed to guides on: * :doc:`OpenVINO GenAI <./openvino-workflow-generative/inference-with-genai>` * :doc:`Hugging Face and Optimum Intel <./openvino-workflow-generative/inference-with-optimum-intel>` * `Generative AI with Base OpenVINO `__ - - diff --git a/docs/articles_en/openvino-workflow.rst b/docs/articles_en/openvino-workflow.rst index 942d6ed4b13a96..27a9e243412334 100644 --- a/docs/articles_en/openvino-workflow.rst +++ b/docs/articles_en/openvino-workflow.rst @@ -13,8 +13,7 @@ OpenVINO Workflow Model Preparation openvino-workflow/model-optimization Running Inference - Deployment on a Local System - Deployment on a Model Server + Deployment on a Local System openvino-workflow/torch-compile @@ -86,11 +85,11 @@ OpenVINO uses the following functions for reading, converting, and saving models and the quickest way of running a deep learning model. | :doc:`Deployment Option 1. Using OpenVINO Runtime ` -| Deploy a model locally, reading the file directly from your application and utilizing about-openvino/additional-resources available to the system. +| Deploy a model locally, reading the file directly from your application and utilizing resources available to the system. | Deployment on a local system uses the steps described in the section on running inference. -| :doc:`Deployment Option 2. Using Model Server ` -| Deploy a model remotely, connecting your application to an inference server and utilizing external about-openvino/additional-resources, with no impact on the app's performance. +| :doc:`Deployment Option 2. Using Model Server <../model-server/ovms_what_is_openvino_model_server>` +| Deploy a model remotely, connecting your application to an inference server and utilizing external resources, with no impact on the app's performance. | Deployment on OpenVINO Model Server is quick and does not require any additional steps described in the section on running inference. | :doc:`Deployment Option 3. Using torch.compile for PyTorch 2.0 ` diff --git a/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst b/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst index 55626d485c412d..9597de75ffe73d 100644 --- a/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst +++ b/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst @@ -140,4 +140,4 @@ sequences. You can find more examples demonstrating how to work with states in other articles: * `LLaVA-NeXT Multimodal Chatbot notebook <../../notebooks/llava-next-multimodal-chatbot-with-output.html>`__ -* :doc:`Serving Stateful Models with OpenVINO Model Server <../../openvino-workflow/model-server/ovms_docs_stateful_models>` +* :doc:`Serving Stateful Models with OpenVINO Model Server <../../model-server/ovms_docs_stateful_models>` diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-C_bHXmvJ.js b/docs/sphinx_setup/_static/selector-tool/assets/selector-C_bHXmvJ.js index b9f82cb3968062..0fffc0579a0480 100644 --- a/docs/sphinx_setup/_static/selector-tool/assets/selector-C_bHXmvJ.js +++ b/docs/sphinx_setup/_static/selector-tool/assets/selector-C_bHXmvJ.js @@ -56,4 +56,4 @@ enabled=1 gpgcheck=1 repo_gpgcheck=1 gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB -EOF`,getMoveRepoFileCommand:e=>`sudo mv /tmp/openvino-${e.metadata.yumYear}.repo ${ec}`,verifyRepoCommand:"yum repolist | grep -i openvino",getInstallCommand:e=>`sudo yum install openvino-${e.metadata.yumVersion}`};class Df extends Ae{constructor(t){super({level:T.DISTRIBUTION,key:A.ZYPPER,metadata:{title:"ZYPPER",subtitle:me("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Pv}}}const Pv={addRepo:"sudo zypper addrepo https://download.opensuse.org/repositories/science/openSUSE_Tumbleweed/science.repo",refresh:"sudo zypper refresh",getInstallCommand:({metadata:e})=>`sudo zypper install openvino-devel-${e.zypperVersion} openvino-sample-${e.zypperVersion}`};class ca extends ti{constructor(t,n,r){super({level:T.OP_SYSTEM,key:t,metadata:n},r),this._setDefaultOS()}_setDefaultOS(){const t=this._detectOS()||Qe.WINDOWS;this.key===t&&this.default()}_detectOS(){const{userAgent:t}=navigator,n={windows:/(Windows|Win)/g,macOS:/(Macintosh|Mac)/g,linux:/(Linux|X11)/g};return 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${bf?"idz-page":""}`,children:m.jsxs($f.Provider,{value:t,children:[m.jsx(Ay,{state:e}),m.jsx(jy,{state:e}),m.jsx(Ly,{state:e}),m.jsx(Iy,{})]})})}hs.createRoot(document.getElementById("root")).render(m.jsx(op.StrictMode,{children:m.jsx(Dy,{})})); diff --git a/docs/sphinx_setup/index.rst b/docs/sphinx_setup/index.rst index 979cc98caa219c..89a6b220886c00 100644 --- a/docs/sphinx_setup/index.rst +++ b/docs/sphinx_setup/index.rst @@ -38,7 +38,7 @@ hardware and environments, on-premises and on-device, in the browser or in the c

  • Improved model serving

    OpenVINO Model Server has improved parallel inference!

    - Learn more + Learn more

  • OpenVINO via PyTorch 2.0 torch.compile()

    @@ -124,7 +124,7 @@ Places to Begin Cloud-ready deployments for microservice applications. - .. button-link:: openvino-workflow/model-server/ovms_what_is_openvino_model_server.html + .. button-link:: model-server/ovms_what_is_openvino_model_server.html :color: primary :outline: @@ -195,5 +195,6 @@ Key Features GET STARTED HOW TO USE - MAIN WORKFLOW HOW TO USE - GENERATIVE AI WORKFLOW + HOW TO USE - MODEL SERVING REFERENCE DOCUMENTATION ABOUT OPENVINO \ No newline at end of file