Selectively update links (#2303)

* 2023.2->2023.3

* 2023.1->2023.3

* /main/ -> /releases/2023/3/

* /nightly/ -> /2023.3/

* Fix 404s

README.md

@@ -15,21 +15,21 @@ OpenVINO™ Model Server (OVMS) is a high-performance system for serving mod
 
 ![OVMS picture](docs/ovms_high_level.png)
 
-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).
-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).
+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/2023.3/ovms_docs_models_repository.html) documentation. Model server works inside [Docker containers](https://docs.openvino.ai/2023.3/ovms_docs_deploying_server.html#deploying-model-server-in-docker-container), on [Bare Metal](https://docs.openvino.ai/2023.3/ovms_docs_deploying_server.html#deploying-model-server-on-baremetal-without-container), and in [Kubernetes environment](https://docs.openvino.ai/2023.3/ovms_docs_deploying_server.html#deploying-model-server-in-kubernetes).
+Start using OpenVINO Model Server with a fast-forward serving example from the [Quickstart guide](https://docs.openvino.ai/2023.3/ovms_docs_quick_start_guide.html) or explore [Model Server features](https://docs.openvino.ai/2023.3/ovms_docs_features.html).
 
 Read [release notes](https://github.com/openvinotoolkit/model_server/releases) to find out what’s new.
 
 ### Key features:
-- **[NEW]** [Python code execution](https://docs.openvino.ai/nightly/ovms_docs_python_support_reference.html)
-- **[NEW]** [gRPC streaming](https://docs.openvino.ai/nightly/ovms_docs_streaming_endpoints.html)
-- [MediaPipe graphs serving](https://docs.openvino.ai/nightly/ovms_docs_mediapipe.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)
-- [Dynamic model inputs](https://docs.openvino.ai/nightly/ovms_docs_shape_batch_layout.html)
-- [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)
-- [Metrics](https://docs.openvino.ai/nightly/ovms_docs_metrics.html) - metrics compatible with Prometheus standard
+- **[NEW]** [Python code execution](https://docs.openvino.ai/2023.3/ovms_docs_python_support_reference.html)
+- **[NEW]** [gRPC streaming](https://docs.openvino.ai/2023.3/ovms_docs_streaming_endpoints.html)
+- [MediaPipe graphs serving](https://docs.openvino.ai/2023.3/ovms_docs_mediapipe.html) 
+- Model management - including [model versioning](https://docs.openvino.ai/2023.3/ovms_docs_model_version_policy.html) and [model updates in runtime](https://docs.openvino.ai/2023.3/ovms_docs_online_config_changes.html)
+- [Dynamic model inputs](https://docs.openvino.ai/2023.3/ovms_docs_shape_batch_layout.html)
+- [Directed Acyclic Graph Scheduler](https://docs.openvino.ai/2023.3/ovms_docs_dag.html) along with [custom nodes in DAG pipelines](https://docs.openvino.ai/2023.3/ovms_docs_custom_node_development.html)
+- [Metrics](https://docs.openvino.ai/2023.3/ovms_docs_metrics.html) - metrics compatible with Prometheus standard
 - Support for multiple frameworks, such as TensorFlow, PaddlePaddle and ONNX
-- Support for [AI accelerators](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html)
+- Support for [AI accelerators](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html)
 
 **Note:** OVMS has been tested on RedHat, and Ubuntu. The latest publicly released docker images are based on Ubuntu and UBI.
 They are stored in:
@@ -39,26 +39,26 @@ They are stored in:
 
 ## Run OpenVINO Model Server
 
-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). 
+A demonstration on how to use OpenVINO Model Server can be found in [our quick-start guide](https://docs.openvino.ai/2023.3/ovms_docs_quick_start_guide.html). 
 For more information on using Model Server in various scenarios you can check the following guides:
 
-* [Model repository configuration](https://docs.openvino.ai/nightly/ovms_docs_models_repository.html)
+* [Model repository configuration](https://docs.openvino.ai/2023.3/ovms_docs_models_repository.html)
 
-* [Deployment options](https://docs.openvino.ai/nightly/ovms_docs_deploying_server.html)
+* [Deployment options](https://docs.openvino.ai/2023.3/ovms_docs_deploying_server.html)
 
-* [Performance tuning](https://docs.openvino.ai/nightly/ovms_docs_performance_tuning.html)
+* [Performance tuning](https://docs.openvino.ai/2023.3/ovms_docs_performance_tuning.html)
 
-* [Directed Acyclic Graph Scheduler](https://docs.openvino.ai/nightly/ovms_docs_dag.html)
+* [Directed Acyclic Graph Scheduler](https://docs.openvino.ai/2023.3/ovms_docs_dag.html)
 
-* [Custom nodes development](https://docs.openvino.ai/nightly/ovms_docs_custom_node_development.html)
+* [Custom nodes development](https://docs.openvino.ai/2023.3/ovms_docs_custom_node_development.html)
 
-* [Serving stateful models](https://docs.openvino.ai/nightly/ovms_docs_stateful_models.html)
+* [Serving stateful models](https://docs.openvino.ai/2023.3/ovms_docs_stateful_models.html)
 
 * [Deploy using a Kubernetes Helm Chart](https://github.com/openvinotoolkit/operator/tree/main/helm-charts/ovms)
 
 * [Deployment using Kubernetes Operator](https://operatorhub.io/operator/ovms-operator)
 
-* [Using binary input data](https://docs.openvino.ai/nightly/ovms_docs_binary_input.html)
+* [Using binary input data](https://docs.openvino.ai/2023.3/ovms_docs_binary_input.html)
 
 
 
@@ -72,7 +72,7 @@ For more information on using Model Server in various scenarios you can check th
 
 * [RESTful API](https://restfulapi.net/)
 
-* [Benchmarking results](https://docs.openvino.ai/nightly/openvino_docs_performance_benchmarks.html)
+* [Benchmarking results](https://docs.openvino.ai/2023.3/openvino_docs_performance_benchmarks.html)
 
 * [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
 

client/go/kserve-api/Dockerfile

@@ -26,7 +26,7 @@ RUN go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.28
 RUN go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.2
 
 # Compile API
-RUN wget https://raw.githubusercontent.com/openvinotoolkit/model_server/main/src/kfserving_api/grpc_predict_v2.proto
+RUN wget https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2023/3/src/kfserving_api/grpc_predict_v2.proto
 RUN echo 'option go_package = "./grpc-client";' >> grpc_predict_v2.proto
 RUN protoc --go_out="./" --go-grpc_out="./" ./grpc_predict_v2.proto
 

client/java/kserve-api/pom.xml

@@ -64,7 +64,7 @@
             </goals>
             <configuration>
               <url>
-                https://raw.githubusercontent.com/openvinotoolkit/model_server/main/src/kfserving_api/grpc_predict_v2.proto</url>
+                https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2023/3/src/kfserving_api/grpc_predict_v2.proto</url>
               <outputFileName>grpc_predict_v2.proto</outputFileName>
               <outputDirectory>src/main/proto</outputDirectory>
             </configuration>

client/python/ovmsclient/lib/README.md

@@ -6,7 +6,7 @@ OVMS client library contains only the necessary dependencies, so the whole packa
 
 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.
 
-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.
+See [API documentation](https://github.com/openvinotoolkit/model_server/blob/releases/2023/3/client/python/ovmsclient/lib/docs/README.md) for details on what the library provides.
 
 ```bash
 git clone https://github.com/openvinotoolkit/model_server.git
@@ -136,4 +136,4 @@ results = client.predict(inputs=inputs, model_name="model")
 #
 ```
 
-For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/main/client/python/ovmsclient/lib/docs/README.md)
+For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2023/3/client/python/ovmsclient/lib/docs/README.md)

client/python/ovmsclient/lib/docs/pypi_overview.md

@@ -9,7 +9,7 @@ The `ovmsclient` package works both with OpenVINO™ Model Server and Tensor
 The `ovmsclient` can replace `tensorflow-serving-api` package with reduced footprint and simplified interface.
 
 
-See [API reference](https://github.com/openvinotoolkit/model_server/blob/main/client/python/ovmsclient/lib/docs/README.md) for usage details.
+See [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2023/3/client/python/ovmsclient/lib/docs/README.md) for usage details.
 
 
 ## Usage example
@@ -38,4 +38,4 @@ results = client.predict(inputs=inputs, model_name="model")
 
 ```
 
-Learn more on `ovmsclient` [documentation site](https://github.com/openvinotoolkit/model_server/tree/main/client/python/ovmsclient/lib).
+Learn more on `ovmsclient` [documentation site](https://github.com/openvinotoolkit/model_server/tree/releases/2023/3/client/python/ovmsclient/lib).

client/python/ovmsclient/samples/README.md

@@ -31,7 +31,7 @@ Install samples dependencies:
 pip3 install -r requirements.txt
 ```
 
-Download [Resnet50-tf Model](https://docs.openvino.ai/2023.2/omz_models_model_resnet_50_tf.html) and convert it into Intermediate Representation format:
+Download [Resnet50-tf Model](https://docs.openvino.ai/2023.3/omz_models_model_resnet_50_tf.html) and convert it into Intermediate Representation format:
 ```bash
 mkdir models
 docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models openvino/ubuntu20_dev:latest omz_downloader --name resnet-50-tf --output_dir /models

demos/README.md

@@ -56,24 +56,24 @@ Check out the list below to see complete step-by-step examples of using OpenVINO
 |[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.|
 |[Seq2seq translation](python_demos/seq2seq_translation/README.md) | Translate text using seq2seq model via gRPC API.|
 |[Age gender recognition](age_gender_recognition/python/README.md) | Run prediction on a JPEG image using age gender recognition model via gRPC API.|
-|[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). |
-|[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). |
+|[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/2023/3/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
+|[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/2023/3/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
 |[Face Detection](face_detection/python/README.md)|Run prediction on a JPEG image using face detection model via gRPC API.|
 |[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) |
-|[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) |
+|[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/2023/3/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md) |
 |[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. |
 |[Image Classification](image_classification/python/README.md)|Run prediction on a JPEG image using image classification model via gRPC API.|
-|[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). |
+|[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/2023/3/src/custom_nodes/image_transformation). |
 |[Using TensorFlow Model](image_classification_using_tf_model/python/README.md)|Run image classification using directly imported TensorFlow model. |
 |[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.|
-|[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). |
+|[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/2023/3/src/custom_nodes/model_zoo_intel_object_detection). |
 |[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. |
 |[Segmentation with PaddlePaddle](segmentation_using_paddlepaddle_model/python/README.md)| Perform segmentation on an image with a PaddlePaddle model. |
 |[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. |
 |[GPT-J Causal Language Modeling](gptj_causal_lm/python/README.md)|Write start of the sentence and let GPT-J continue via gRPC API. This demo uses dynamic shape feature. |
 |[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. | 
 |[Benchmark App](benchmark/python/README.md)|Generate traffic and measure performance of the model served in OpenVINO Model Server.|
-|[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). |
+|[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/2023/3/src/custom_nodes/face_blur). |
 
 ## With C++ Client
 | Demo | Description |

demos/age_gender_recognition/python/README.md

@@ -35,7 +35,7 @@ Install python dependencies:
 ```bash
 pip3 install -r requirements.txt
 ```
-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:
+Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/releases/2023/3/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
 ```bash
 python3 age_gender_recognition.py --image_input_path age-gender-recognition-retail-0001.jpg --rest_port 8000
 ```