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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 - preview](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 - preview](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/2024/2/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/2024/2/client/python/ovmsclient/lib/docs/README.md)
Copy file name to clipboardexpand all lines: client/python/ovmsclient/lib/docs/pypi_overview.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/2024/2/client/python/ovmsclient/lib/docs/README.md) for usage details.
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