Links 2025.1 (openvinotoolkit#3193)

* main->releases/2025/1

* 404s

* /releases/2025/0/->/releases/2025/1/

* Karol's fixes


CVS-164825
CVS-165419

Author: dkalinowski

README.md

@@ -51,13 +51,13 @@ A demonstration on how to use OpenVINO Model Server can be found in our [quick-s
 
 Check also other instructions:
 
-[Preparing model repository](https://docs.openvino.ai/nightly/model-server/ovms_docs_models_repository.html)
+[Preparing model repository](https://docs.openvino.ai/2025/model-server/ovms_docs_models_repository.html)
 
-[Deployment](https://docs.openvino.ai/nightly/model-server/ovms_docs_deploying_server.html)
+[Deployment](https://docs.openvino.ai/2025/model-server/ovms_docs_deploying_server.html)
 
-[Writing client code](https://docs.openvino.ai/nightly/model-server/ovms_docs_server_app.html)
+[Writing client code](https://docs.openvino.ai/2025/model-server/ovms_docs_server_app.html)
 
-[Demos](https://docs.openvino.ai/nightly/model-server/ovms_docs_demos.html)
+[Demos](https://docs.openvino.ai/2025/model-server/ovms_docs_demos.html)
 
 
 
@@ -73,7 +73,7 @@ Check also other instructions:
 
 * [Inference Scaling with OpenVINO™ Model Server in Kubernetes and OpenShift Clusters](https://www.intel.com/content/www/us/en/developer/articles/technical/deploy-openvino-in-openshift-and-kubernetes.html)
 
-* [Benchmarking results](https://docs.openvino.ai/nightly/about-openvino/performance-benchmarks.html)
+* [Benchmarking results](https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html)
 
 
 ## Contact

client/go/kserve-api/Dockerfile

@@ -26,7 +26,7 @@ RUN go install google.golang.org/protobuf/cmd/[email protected]
 RUN go install google.golang.org/grpc/cmd/[email protected]
 
 # 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/2025/1/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/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/2025/1/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/2025/1/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/2025/1/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/2025/1/client/python/ovmsclient/lib).

demos/README.md

@@ -48,15 +48,15 @@ OpenVINO Model Server demos have been created to showcase the usage of the model
  - [VLM Text Generation with continuous batching](continuous_batching/vlm/README.md)
  - [OpenAI API text embeddings ](embeddings/README.md)
  - [Reranking with Cohere API](rerank/README.md)
- - [RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)
+ - [RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/continuous_batching/rag/rag_demo.ipynb)
 
 Check out the list below to see complete step-by-step examples of using OpenVINO Model Server with real world use cases:
 
 ## With Traditional Models
 | Demo | Description |
 |---|---|
 |[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/2025/1/src/custom_nodes/image_transformation). |
 |[Using TensorFlow Model](image_classification_using_tf_model/python/README.md)|Run image classification using directly imported TensorFlow model. |
 |[Age gender recognition](age_gender_recognition/python/README.md) | Run prediction on a JPEG image using age gender recognition model via gRPC API.|
 |[Face Detection](face_detection/python/README.md)|Run prediction on a JPEG image using face detection model via gRPC API.|
@@ -86,13 +86,13 @@ Check out the list below to see complete step-by-step examples of using OpenVINO
 ## With DAG Pipelines
 | Demo | Description |
 |---|---|
-|[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/2025/1/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/2025/1/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
 |[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/2025/1/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. |
-|[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). |
-|[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). |
+|[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/2025/1/src/custom_nodes/face_blur). |
+|[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/2025/1/src/custom_nodes/model_zoo_intel_object_detection). |
 
 ## With C++ Client
 | Demo | Description |

demos/age_gender_recognition/python/README.md

@@ -53,7 +53,7 @@ Install python dependencies:
 ```console
 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/2025/1/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
 ```console
 python age_gender_recognition.py --image_input_path age-gender-recognition-retail-0001.jpg --rest_port 8000
 ```

demos/benchmark/python/README.md

@@ -379,4 +379,4 @@ docker run -v ${PWD}/workspace:/workspace --network host benchmark_client -a loc
 ```
 
 Many other client options together with benchmarking examples are presented in
-[an additional PDF document](https://github.com/openvinotoolkit/model_server/blob/main/docs/python-benchmarking-client-16feb.pdf).
+[an additional PDF document](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/docs/python-benchmarking-client-16feb.pdf).

demos/bert_question_answering/python/README.md

@@ -4,7 +4,7 @@
 
 This document demonstrates how to run inference requests for [BERT model](https://github.com/openvinotoolkit/open_model_zoo/tree/2022.1.0/models/intel/bert-small-uncased-whole-word-masking-squad-int8-0002) with OpenVINO Model Server. It provides questions answering functionality.
 
-In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
+In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
 
 NOTE: With `min_request_token_num` parameter you can specify the minimum size of the request. If the paragraph has too short, it is concatenated with the next one until it has required length. When there is no paragraphs left to concatenate request is created with the remaining content.
 

demos/continuous_batching/README.md

@@ -33,8 +33,8 @@ LLM engine parameters will be defined inside the `graph.pbtxt` file.
 
 Download export script, install it's dependencies and create directory for the models:
 ```console
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/0/demos/common/export_models/export_model.py -o export_model.py
-pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/0/demos/common/export_models/requirements.txt
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/1/demos/common/export_models/export_model.py -o export_model.py
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/1/demos/common/export_models/requirements.txt
 mkdir models
 ```
 
@@ -321,16 +321,16 @@ P99 TPOT (ms):                           246.48
 
 The service deployed above can be used in RAG chain using `langchain` library with OpenAI endpoint as the LLM engine.
 
-Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)
+Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/continuous_batching/rag/rag_demo.ipynb)
 
 ## Scaling the Model Server
 
-Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/scaling/README.md).
+Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/continuous_batching/scaling/README.md).
 
 
 ## Testing the model accuracy over serving API
 
-Check the [guide of using lm-evaluation-harness](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/accuracy/README.md)
+Check the [guide of using lm-evaluation-harness](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/continuous_batching/accuracy/README.md)
 
 ## Use Speculative Decoding
 

demos/continuous_batching/rag/README.md

@@ -4,8 +4,8 @@
 ## Creating models repository for all the endpoints
 
 ```console
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/0/demos/common/export_models/export_model.py -o export_model.py
-pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/0/demos/common/export_models/requirements.txt
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/1/demos/common/export_models/export_model.py -o export_model.py
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/1/demos/common/export_models/requirements.txt
 
 mkdir models
 python export_model.py text_generation --source_model meta-llama/Meta-Llama-3-8B-Instruct --weight-format int8 --kv_cache_precision u8 --config_file_path models/config_all.json --model_repository_path models 
@@ -28,4 +28,4 @@ ovms --rest_port 8000 --config_path ./models/config_all.json
 
 ## Using RAG
 
-When the model server is deployed and serving all 3 endpoints, run the [jupyter notebook](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb) to use RAG chain with a fully remote execution.
+When the model server is deployed and serving all 3 endpoints, run the [jupyter notebook](https://github.com/openvinotoolkit/model_server/blob/releases/2025/1/demos/continuous_batching/rag/rag_demo.ipynb) to use RAG chain with a fully remote execution.