CVS-81071 Hardcode documentation links to 2022.1 for OV/OVMS/OMZ/MO (#…

…1222)

* Replace develop links to main
* Update OMZ links
* Update example catalog-source
* Freeze absolute links to release/2022/1
* Update OpenVINO sphinx doc links to 2022.1
* Update OVMS sphinx doc links from nightly to 2022.1
* Fix 2021.4 links
* update model optimizer/downloader/converter command line CVS-82624
* add info about paddlepaddle support
Co-authored-by: Dariusz Trawinski <dariusz.trawinski@intel.com>

README.md

@@ -12,23 +12,23 @@ Google Cloud Storage (GCS), Amazon S3, or Azure Blob Storage.
 
 Read [release notes](https://github.com/openvinotoolkit/model_server/releases) to find out what’s new.
 
-Review the [Architecture concept](https://docs.openvino.ai/nightly/ovms_docs_architecture.html) document for more details.
+Review the [Architecture concept](https://docs.openvino.ai/2022.1/ovms_docs_architecture.html) document for more details.
 
 Key features: 
-- support for multiple frameworks, such as Caffe, TensorFlow, MXNet, and ONNX
-- online deployment of new [model versions](https://docs.openvino.ai/nightly/ovms_docs_model_version_policy.html)
-- [configuration updates in runtime](https://docs.openvino.ai/nightly/ovms_docs_online_config_changes.html)
+- support for multiple frameworks, such as Caffe, TensorFlow, MXNet, PaddlePaddle and ONNX
+- online deployment of new [model versions](https://docs.openvino.ai/2022.1/ovms_docs_model_version_policy.html)
+- [configuration updates in runtime](https://docs.openvino.ai/2022.1/ovms_docs_online_config_changes.html)
 - support for AI accelerators, such as 
-[Intel Movidius Myriad VPUs](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_VPU.html), 
-[GPU](https://docs.openvino.ai/latest/openvino_docs_IE_DG_supported_plugins_GPU.html), and 
-[HDDL](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_HDDL.html) 
-- works with [Bare Metal Hosts](docs/host.md) as well as [Docker containers](https://docs.openvino.ai/nightly/ovms_docs_docker_container.html) 
-- [model reshaping](https://docs.openvino.ai/nightly/ovms_docs_shape_batch_layout.html) in runtime
-- [directed Acyclic Graph Scheduler](https://docs.openvino.ai/nightly/ovms_docs_dag.html) - connecting multiple models to deploy complex processing solutions and reducing data transfer overhead
-- [custom nodes in DAG pipelines](https://docs.openvino.ai/nightly/ovms_docs_custom_node_development.html) - allowing model inference and data transformations to be implemented with a custom node C/C++ dynamic library
-- [serving stateful models](https://docs.openvino.ai/nightly/ovms_docs_stateful_models.html) - models that operate on sequences of data and maintain their state between inference requests
-- [binary format of the input data](https://docs.openvino.ai/nightly/ovms_docs_binary_input.html) - data can be sent in JPEG or PNG formats to reduce traffic and offload the client applications
-- [model caching](https://docs.openvino.ai/nightly/ovms_docs_model_cache.html) - cache the models on first load and re-use models from cache on subsequent loads
+[Intel Movidius Myriad VPUs](https://docs.openvino.ai/2022.1/openvino_docs_OV_UG_supported_plugins_MYRIAD.html), 
+[GPU](https://docs.openvino.ai/2022.1/openvino_docs_OV_UG_supported_plugins_GPU.html), and 
+[HDDL](https://docs.openvino.ai/2022.1/openvino_docs_OV_UG_supported_plugins_HDDL.html) 
+- works with [Bare Metal Hosts](docs/host.md) as well as [Docker containers](https://docs.openvino.ai/2022.1/ovms_docs_docker_container.html) 
+- [model reshaping](https://docs.openvino.ai/2022.1/ovms_docs_shape_batch_layout.html) in runtime
+- [directed Acyclic Graph Scheduler](https://docs.openvino.ai/2022.1/ovms_docs_dag.html) - connecting multiple models to deploy complex processing solutions and reducing data transfer overhead
+- [custom nodes in DAG pipelines](https://docs.openvino.ai/2022.1/ovms_docs_custom_node_development.html) - allowing model inference and data transformations to be implemented with a custom node C/C++ dynamic library
+- [serving stateful models](https://docs.openvino.ai/2022.1/ovms_docs_stateful_models.html) - models that operate on sequences of data and maintain their state between inference requests
+- [binary format of the input data](https://docs.openvino.ai/2022.1/ovms_docs_binary_input.html) - data can be sent in JPEG or PNG formats to reduce traffic and offload the client applications
+- [model caching](https://docs.openvino.ai/2022.1/ovms_docs_model_cache.html) - cache the models on first load and re-use models from cache on subsequent loads
 
 
 **Note:** OVMS has been tested on RedHat, CentOS, and Ubuntu. The latest publicly released docker images are based on Ubuntu and UBI.
@@ -39,28 +39,28 @@ 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/2022.1/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/2022.1/ovms_docs_models_repository.html)
 
-* [Using a docker container](https://docs.openvino.ai/nightly/ovms_docs_docker_container.html)
+* [Using a docker container](https://docs.openvino.ai/2022.1/ovms_docs_docker_container.html)
 
-* [Landing on bare metal or virtual machine](https://docs.openvino.ai/nightly/ovms_docs_baremetal.html)
+* [Landing on bare metal or virtual machine](https://docs.openvino.ai/2022.1/ovms_docs_baremetal.html)
 
-* [Performance tuning](https://docs.openvino.ai/nightly/ovms_docs_performance_tuning.html)
+* [Performance tuning](https://docs.openvino.ai/2022.1/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/2022.1/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/2022.1/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/2022.1/ovms_docs_stateful_models.html)
 
-* [Deploy using a Kubernetes Helm Chart](https://docs.openvino.ai/nightly/ovms_deploy_helm_chart.html)
+* [Deploy using a Kubernetes Helm Chart](https://docs.openvino.ai/2022.1/ovms_deploy_helm_chart.html)
 
 * [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/2022.1/ovms_docs_binary_input.html)
 
 
 
@@ -74,7 +74,7 @@ For more information on using Model Server in various scenarios you can check th
 
 * [RESTful API](https://restfulapi.net/)
 
-* [Benchmarking results](https://docs.openvinotoolkit.org/latest/openvino_docs_performance_benchmarks_ovms.html)
+* [Benchmarking results](https://docs.openvino.ai/2022.1/openvino_docs_performance_benchmarks_ovms.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/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/develop/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/2022/1/client/python/ovmsclient/lib/docs/README.md) for details on what the library provides.
 
 
 ## Installation
@@ -82,7 +82,7 @@ model_status = client.get_model_status(model_name="model")
 model_metadata = client.get_model_metadata(model_name="model")
 
 # Exemplary model_metadata. Values for model:
-# https://docs.openvino.ai/latest/omz_models_model_resnet_50_tf.html
+# https://docs.openvino.ai/2022.1/omz_models_model_resnet_50_tf.html
 #
 #{
 #   "model_version": 1,
@@ -105,7 +105,7 @@ model_metadata = client.get_model_metadata(model_name="model")
 **Create and send predict request with binary input data:**
 ```python
 # Assuming requesting model with inputs and outputs as in:
-# https://docs.openvino.ai/latest/omz_models_model_resnet_50_tf.html
+# https://docs.openvino.ai/2022.1/omz_models_model_resnet_50_tf.html
 
 with open(<path_to_img>, 'rb') as f:
     img = f.read()
@@ -118,4 +118,4 @@ results = client.predict(inputs=inputs, model_name="model")
 #
 ```
 
-For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/ovmsclient/lib/docs/README.md)
+For more details on `ovmsclient` see [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2022/1/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/develop/client/python/ovmsclient/lib/docs/README.md) for usage details.
+See [API reference](https://github.com/openvinotoolkit/model_server/blob/releases/2022/1/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/develop/client/python/ovmsclient/lib).
+Learn more on `ovmsclient` [documentation site](https://github.com/openvinotoolkit/model_server/tree/releases/2022/1/client/python/ovmsclient/lib).

client/python/ovmsclient/lib/setup.py

@@ -56,7 +56,7 @@ def run(self):
      description="Python client for OpenVINO Model Server",
      long_description=long_description,
      long_description_content_type="text/markdown",
-     url="https://github.com/openvinotoolkit/model_server/tree/main/client/python/lib",
+     url="https://github.com/openvinotoolkit/model_server/tree/releases/2022/1/client/python/ovmsclient/lib",
      cmdclass={
         "build_apis": BuildApis,
      },

client/python/ovmsclient/samples/README.md

@@ -24,11 +24,11 @@ Install samples dependencies:
 pip3 install -r requirements.txt
 ```
 
-Download [Resnet50-tf Model](https://docs.openvinotoolkit.org/latest/omz_models_model_resnet_50_tf.html) and convert it into Intermediate Representation format:
+Download [Resnet50-tf Model](https://docs.openvino.ai/2022.1/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/ubuntu18_dev:latest deployment_tools/open_model_zoo/tools/downloader/downloader.py --name resnet-50-tf --output_dir /models
-docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models:rw openvino/ubuntu18_dev:latest deployment_tools/open_model_zoo/tools/downloader/converter.py --name resnet-50-tf --download_dir /models --output_dir /models --precisions FP32
+docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models openvino/ubuntu18_dev:latest omz_downloader --name resnet-50-tf --output_dir /models
+docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models:rw openvino/ubuntu18_dev:latest omz_converter --name resnet-50-tf --download_dir /models --output_dir /models --precisions FP32
 mv ${PWD}/models/public/resnet-50-tf/FP32 ${PWD}/models/public/resnet-50-tf/1
 ```
 

demos/README.md

@@ -31,21 +31,21 @@ OpenVINO Model Server demos have been created to showcase the usage of the model
 | Demo | Description |
 |---|---|
 |[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/develop/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/develop/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/2022/1/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/2022/1/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 recogition 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 recogition 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/develop/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 recogition 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/2022/1/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. |
 |[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/develop/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/2022/1/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.|
-|[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/develop/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/2022/1/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. |
 |[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. |
 |[Speech Recognition on Kaldi Model](speech_recognition_with_kaldi_model/python/README.md)|Run inference on a speech sample and use Kaldi model to perform speech recognition via gRPC API. This demo uses [stateful model](../docs/stateful_models.md). |
 |[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/develop/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/2022/1/src/custom_nodes/face_blur). |
 
 ## C++
 | Demo | Description |