openvino/docs/Documentation/openvino_workflow.md

OpenVINO Workflow

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.. meta:: :description: OpenVINO toolkit workflow usually involves preparation, optimization, and compression of models, running inference and deploying deep learning applications.

.. toctree:: :maxdepth: 1 :hidden:

Model Preparation <openvino_docs_model_processing_introduction> Model Optimization and Compression <openvino_docs_model_optimization_guide> Running Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide> Deployment on a Local System <openvino_deployment_guide> Deployment on a Model Server <ovms_what_is_openvino_model_server> pytorch_2_0_torch_compile

| :doc:Model Preparation <openvino_docs_model_processing_introduction> | With model conversion API guide, you will learn to convert pre-trained models for use with OpenVINO™. You can use your own models or choose some from a broad selection in online databases, such as TensorFlow Hub <https://tfhub.dev/>, Hugging Face <https://huggingface.co/>, Torchvision models <https://pytorch.org/hub/>__..

| :doc:Model Optimization and Compression <openvino_docs_model_optimization_guide> | In this section you will find out how to optimize a model to achieve better inference performance. It describes multiple optimization methods for both the training and post-training stages.

| :doc:Running Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide> | This section explains describes how to run inference which is the most basic form of deployment and the quickest way of launching inference.

Once you have a model that meets both OpenVINO™ and your requirements, you can choose how to deploy it with your application.

| :doc:Option 1. Deployment via OpenVINO Runtime <openvino_deployment_guide> | Local deployment uses OpenVINO Runtime that is called from, and linked to, the application directly. | It utilizes resources available to the system and provides the quickest way of launching inference. | Deployment on a local system requires performing the steps from the running inference section.

| :doc:Option 2. Deployment via Model Server <ovms_what_is_openvino_model_server> | Deployment via OpenVINO Model Server allows the application to connect to the inference server set up remotely. | This way inference can use external resources instead of those available to the application itself. | Deployment on a model server can be done quickly and without performing any additional steps described in the running inference section.

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