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Copy file name to clipboardExpand all lines: README.md
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The models used by the server need to be stored locally or hosted remotely by object storage services. For more details, refer to [Preparing Model Repository](https://docs.openvino.ai/nightly/ovms_docs_models_repository.html) documentation. Model server works inside [Docker containers](https://docs.openvino.ai/nightly/ovms_docs_deploying_server.html#deploying-model-server-in-docker-container), on [Bare Metal](https://docs.openvino.ai/nightly/ovms_docs_deploying_server.html#deploying-model-server-on-baremetal-without-container), and in [Kubernetes environment](https://docs.openvino.ai/nightly/ovms_docs_deploying_server.html#deploying-model-server-in-kubernetes).
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Start using OpenVINO Model Server with a fast-forward serving example from the [Quickstart guide](https://docs.openvino.ai/nightly/ovms_docs_quick_start_guide.html) or explore [Model Server features](https://docs.openvino.ai/nightly/ovms_docs_features.html).
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The models used by the server need to be stored locally or hosted remotely by object storage services. For more details, refer to [Preparing Model Repository](https://docs.openvino.ai/2024/ovms_docs_models_repository.html) documentation. Model server works inside [Docker containers](https://docs.openvino.ai/2024/ovms_docs_deploying_server.html#deploying-model-server-in-docker-container), on [Bare Metal](https://docs.openvino.ai/2024/ovms_docs_deploying_server.html#deploying-model-server-on-baremetal-without-container), and in [Kubernetes environment](https://docs.openvino.ai/2024/ovms_docs_deploying_server.html#deploying-model-server-in-kubernetes).
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Start using OpenVINO Model Server with a fast-forward serving example from the [Quickstart guide](https://docs.openvino.ai/2024/ovms_docs_quick_start_guide.html) or explore [Model Server features](https://docs.openvino.ai/2024/ovms_docs_features.html).
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Read [release notes](https://github.com/openvinotoolkit/model_server/releases) to find out what’s new.
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### Key features:
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-**[NEW]**[Efficient Text Generation via OpenAI API](https://docs.openvino.ai/nightly/ovms_docs_llm_reference.html)
- Model management - including [model versioning](https://docs.openvino.ai/nightly/ovms_docs_model_version_policy.html) and [model updates in runtime](https://docs.openvino.ai/nightly/ovms_docs_online_config_changes.html)
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-[Dynamic model inputs](https://docs.openvino.ai/nightly/ovms_docs_shape_batch_layout.html)
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-[Directed Acyclic Graph Scheduler](https://docs.openvino.ai/nightly/ovms_docs_dag.html) along with [custom nodes in DAG pipelines](https://docs.openvino.ai/nightly/ovms_docs_custom_node_development.html)
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-[Metrics](https://docs.openvino.ai/nightly/ovms_docs_metrics.html) - metrics compatible with Prometheus standard
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-**[NEW]**[Efficient Text Generation via OpenAI API](https://docs.openvino.ai/2024/ovms_docs_llm_reference.html)
- Model management - including [model versioning](https://docs.openvino.ai/2024/ovms_docs_model_version_policy.html) and [model updates in runtime](https://docs.openvino.ai/2024/ovms_docs_online_config_changes.html)
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-[Dynamic model inputs](https://docs.openvino.ai/2024/ovms_docs_shape_batch_layout.html)
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-[Directed Acyclic Graph Scheduler](https://docs.openvino.ai/2024/ovms_docs_dag.html) along with [custom nodes in DAG pipelines](https://docs.openvino.ai/2024/ovms_docs_custom_node_development.html)
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-[Metrics](https://docs.openvino.ai/2024/ovms_docs_metrics.html) - metrics compatible with Prometheus standard
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- Support for multiple frameworks, such as TensorFlow, PaddlePaddle and ONNX
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- Support for [AI accelerators](https://docs.openvino.ai/nightly/about-openvino/compatibility-and-support/supported-devices.html)
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- Support for [AI accelerators](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)
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**Note:** OVMS has been tested on RedHat, and Ubuntu. The latest publicly released docker images are based on Ubuntu and UBI.
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They are stored in:
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## Run OpenVINO Model Server
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A demonstration on how to use OpenVINO Model Server can be found in [our quick-start guide](https://docs.openvino.ai/nightly/ovms_docs_quick_start_guide.html).
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A demonstration on how to use OpenVINO Model Server can be found in [our quick-start guide](https://docs.openvino.ai/2024/ovms_docs_quick_start_guide.html).
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For more information on using Model Server in various scenarios you can check the following guides:
*[Speed and Scale AI Inference Operations Across Multiple Architectures](https://techdecoded.intel.io/essentials/speed-and-scale-ai-inference-operations-across-multiple-architectures/?elq_cid=3646480_ts1607680426276&erpm_id=6470692_ts1607680426276) - webinar recording
Copy file name to clipboardExpand all lines: client/python/ovmsclient/lib/README.md
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As OpenVINO Model Server API is compatible with TensorFlow Serving, it's possible to use `ovmsclient` with TensorFlow Serving instances on: Predict, GetModelMetadata and GetModelStatus endpoints.
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See [API documentation](https://github.com/openvinotoolkit/model_server/blob/main/client/python/ovmsclient/lib/docs/README.md) for details on what the library provides.
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See [API documentation](https://github.com/openvinotoolkit/model_server/blob/releases/2024/4/client/python/ovmsclient/lib/docs/README.md) for details on what the library provides.
For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/main/client/python/ovmsclient/lib/docs/README.md)
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For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2024/4/client/python/ovmsclient/lib/docs/README.md)
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The `ovmsclient` can replace `tensorflow-serving-api` package with reduced footprint and simplified interface.
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See [API reference](https://github.com/openvinotoolkit/model_server/blob/main/client/python/ovmsclient/lib/docs/README.md) for usage details.
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See [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2024/4/client/python/ovmsclient/lib/docs/README.md) for usage details.
Learn more on `ovmsclient`[documentation site](https://github.com/openvinotoolkit/model_server/tree/main/client/python/ovmsclient/lib).
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Learn more on `ovmsclient`[documentation site](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/client/python/ovmsclient/lib).
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OpenVINO Model Server demos have been created to showcase the usage of the model server as well as demonstrate it’s capabilities.
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### Check Out New Generative AI Demos
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-[Text Generation with continuous batching](continuous_batching/README.md)
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-[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)
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-[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/releases/2024/4/demos/continuous_batching/rag/rag_demo.ipynb)
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Check out the list below to see complete step-by-step examples of using OpenVINO Model Server with real world use cases:
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|[CLIP image classification](python_demos/clip_image_classification/README.md)| Classify image according to provided labels using CLIP model embedded in a multi-node MediaPipe graph.|
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|[Seq2seq translation](python_demos/seq2seq_translation/README.md)| Translate text using seq2seq model via gRPC API.|
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|[Age gender recognition](age_gender_recognition/python/README.md)| Run prediction on a JPEG image using age gender recognition model via gRPC API.|
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|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md)| Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md)| Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md)| Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md)| Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Face Detection](face_detection/python/README.md)|Run prediction on a JPEG image using face detection model via gRPC API.|
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|[Single Face Analysis Pipeline](single_face_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a simple pipeline of age-gender recognition and emotion recognition models via gRPC API to analyze image with a single face. This demo uses [pipeline](../docs/dag_scheduler.md)|
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|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md)|
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|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md)|
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|[Model Ensemble Pipeline](model_ensemble/python/README.md)|Combine multiple image classification models into one [pipeline](../docs/dag_scheduler.md) and aggregate results to improve classification accuracy. |
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|[Image Classification](image_classification/python/README.md)|Run prediction on a JPEG image using image classification model via gRPC API.|
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|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/image_transformation). |
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|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/image_transformation). |
|[Person, Vehicle, Bike Detection](person_vehicle_bike_detection/python/README.md)|Run prediction on a video file or camera stream using person, vehicle, bike detection model via gRPC API.|
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|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection). |
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|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/model_zoo_intel_object_detection). |
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|[Real Time Stream Analysis](real_time_stream_analysis/python/README.md)| Analyze RTSP video stream in real time with generic application template for custom pre and post processing routines as well as simple results visualizer for displaying predictions in the browser. |
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|[Classification with PaddlePaddle](classification_using_paddlepaddle_model/python/README.md)| Perform classification on an image with a PaddlePaddle model. |
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|[Natural Language Processing with BERT](bert_question_answering/python/README.md)|Provide a knowledge source and a query and use BERT model for question answering use case via gRPC API. This demo uses dynamic shape feature. |
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|[Using inputs data in string format with universal-sentence-encoder model](universal-sentence-encoder/README.md)| Handling AI model with text as the model input. |
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|[Benchmark App](benchmark/python/README.md)|Generate traffic and measure performance of the model served in OpenVINO Model Server.|
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|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/face_blur). |
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|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2024/4/src/custom_nodes/face_blur). |
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```bash
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pip3 install -r requirements.txt
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```
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Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
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Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/releases/2024/4/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
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