diff --git a/docs/accelerators.md b/docs/accelerators.md index 945934d1f6..1c755a55c6 100644 --- a/docs/accelerators.md +++ b/docs/accelerators.md @@ -112,7 +112,7 @@ docker run --rm -it --device=/dev/dxg --volume /usr/lib/wsl:/usr/lib/wsl --grou > **NOTE**: > The public docker image includes the OpenCL drivers for GPU in version 22.28 (RedHat) and 22.35 (Ubuntu). -If you need to build the OpenVINO Model Server with different driver version, refer to the [building from sources](https://github.com/openvinotoolkit/model_server/blob/develop/docs/build_from_source.md) +If you need to build the OpenVINO Model Server with different driver version, refer to the [building from sources](https://github.com/openvinotoolkit/model_server/blob/v2022.3/docs/build_from_source.md) ## Using Multi-Device Plugin @@ -232,7 +232,7 @@ The docker image of OpenVINO Model Server including support for NVIDIA can be bu cd model_server make docker_build NVIDIA=1 OV_USE_BINARY=0 OV_SOURCE_BRANCH=releases/2022/3 OV_CONTRIB_BRANCH=releases/2022/3 ``` -Check also [building from sources](https://github.com/openvinotoolkit/model_server/blob/develop/docs/build_from_source.md). +Check also [building from sources](https://github.com/openvinotoolkit/model_server/blob/v2022.3/docs/build_from_source.md). Example command to run container with NVIDIA support: diff --git a/docs/binary_input_kfs.md b/docs/binary_input_kfs.md index 20889d8aca..e68170d190 100644 --- a/docs/binary_input_kfs.md +++ b/docs/binary_input_kfs.md @@ -203,7 +203,7 @@ pip install -r requirements.txt ``` ### Run the gRPC client sending JPEG images -([see the code](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/kserve-api/samples/grpc_infer_binary_resnet.py)) +([see the code](https://github.com/openvinotoolkit/model_server/blob/v2022.3/client/python/kserve-api/samples/grpc_infer_binary_resnet.py)) ```bash python3 ./grpc_infer_binary_resnet.py --grpc_port 9000 --images_list resnet_input_images.txt --labels_numpy_path ../../lbs.npy --input_name 0 --output_name 1463 --model_name resnet Start processing: @@ -253,7 +253,7 @@ Classification accuracy: 100.00 ### Run the REST client sending JPEG images -([see the code](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/kserve-api/samples/http_infer_binary_resnet.py)) +([see the code](https://github.com/openvinotoolkit/model_server/blob/v2022.3/client/python/kserve-api/samples/http_infer_binary_resnet.py)) ```bash python3 ./http_infer_binary_resnet.py --http_port 8000 --images_list resnet_input_images.txt --labels_numpy_path ../../lbs.npy --input_name 0 --output_name 1463 --model_name resnet Start processing: @@ -302,7 +302,7 @@ Classification accuracy: 100.00 ``` ### Run the REST client with raw data sent in binary representation -([see the code](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/kserve-api/samples/http_infer_resnet.py)) +([see the code](https://github.com/openvinotoolkit/model_server/blob/v2022.3/client/python/kserve-api/samples/http_infer_resnet.py)) ```bash python3 ./http_infer_resnet.py --http_port 8000 --images_numpy_path ../../imgs_nhwc.npy --labels_numpy_path ../../lbs.npy --input_name 0 --output_name 1463 --model_name resnet --transpose_input False --binary_data Image data range: 0.0 : 255.0 diff --git a/docs/binary_input_tfs.md b/docs/binary_input_tfs.md index b7710f4cbd..3a9c22cfe7 100644 --- a/docs/binary_input_tfs.md +++ b/docs/binary_input_tfs.md @@ -100,7 +100,7 @@ pip install -r requirements.txt ``` ### Run the gRPC client sending the binary input -([see the code](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/ovmsclient/samples/grpc_predict_binary_resnet.py)) +([see the code](https://github.com/openvinotoolkit/model_server/blob/v2022.3/client/python/ovmsclient/samples/grpc_predict_binary_resnet.py)) ```bash python grpc_predict_binary_resnet.py --images_dir ../../../../demos/common/static/images --model_name resnet --service_url localhost:9000 Image ../../../../demos/common/static/images/magnetic_compass.jpeg has been classified as magnetic compass @@ -117,7 +117,7 @@ Image ../../../../demos/common/static/images/golden_retriever.jpeg has been clas ### Run the REST client sending the binary input -([see the code](https://github.com/openvinotoolkit/model_server/blob/develop/client/python/ovmsclient/samples/http_predict_binary_resnet.py)) +([see the code](https://github.com/openvinotoolkit/model_server/blob/v2022.3/client/python/ovmsclient/samples/http_predict_binary_resnet.py)) ```bash python http_predict_binary_resnet.py --images_dir ../../../../demos/common/static/images --model_name resnet --service_url localhost:8000 Image ../../../../demos/common/static/images/magnetic_compass.jpeg has been classified as magnetic compass diff --git a/docs/build_from_source.md b/docs/build_from_source.md index 196f6b4007..0337cc89dc 100644 --- a/docs/build_from_source.md +++ b/docs/build_from_source.md @@ -126,4 +126,4 @@ Example: make docker_build OV_USE_BINARY=0 OV_SOURCE_BRANCH= ``` -Read more detailed usage in [developer guide](https://github.com/openvinotoolkit/model_server/blob/develop/docs/developer_guide.md). +Read more detailed usage in [developer guide](https://github.com/openvinotoolkit/model_server/blob/v2022.3/docs/developer_guide.md). diff --git a/docs/clients_kfs.md b/docs/clients_kfs.md index 36b01b9e40..b8c437457e 100644 --- a/docs/clients_kfs.md +++ b/docs/clients_kfs.md @@ -84,7 +84,7 @@ When creating a Python-based client application, you can use Triton client libra @endsphinxdirective -For complete usage examples see [Kserve samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples). +For complete usage examples see [Kserve samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples). ## C++ Client @@ -95,4 +95,4 @@ For complete usage examples see [Kserve samples](https://github.com/openvinotool Creating a client application in C++ follows the same principles as Python. When creating a C++-based client application, you can use Triton client library - [tritonclient](https://github.com/triton-inference-server/client). -See our [C++ samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/cpp/kserve-api/README.md) to learn how to build a sample C++ client application. +See our [C++ samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/cpp/kserve-api/README.md) to learn how to build a sample C++ client application. diff --git a/docs/metrics.md b/docs/metrics.md index de9a372a94..e8f8a3b524 100644 --- a/docs/metrics.md +++ b/docs/metrics.md @@ -221,7 +221,7 @@ It means that each request to the DAG pipeline will update also the metrics for With server metrics being scraped by [Prometheus](https://prometheus.io/) it is possible to integrate [Grafana](https://grafana.com/) to visualize them on the dashboards. Once you have Grafana configured with Prometheus as a data source, you can create your own dashboard or import one. -In OpenVINO Model Server repository you can find [grafana_dashboard.json](https://github.com/openvinotoolkit/model_server/blob/develop/extras/grafana_dashboard.json) file that can be used to visualize per model metrics like: +In OpenVINO Model Server repository you can find [grafana_dashboard.json](https://github.com/openvinotoolkit/model_server/blob/v2022.3/extras/grafana_dashboard.json) file that can be used to visualize per model metrics like: - Throughput [RPS] - number of requests being processed by the model per second. - Mean Latency [ms] - latency averaged across all requests processed by the model in a certain timeframe. - Latency Quantile [ms] - value of latency for quantiles [0.75, 0.90, 0.99], meaning the latency that has NOT been exceeded by 75%, 90% and 99% of the requests. diff --git a/docs/model_server_c_api.md b/docs/model_server_c_api.md index d0cccd8419..5705c6f341 100644 --- a/docs/model_server_c_api.md +++ b/docs/model_server_c_api.md @@ -6,7 +6,7 @@ This document describes OpenVINO Model Server (OVMS) C API that allows OVMS to b ## API Description -Server functionalities are encapsulated in shared library built from OVMS source. To include OVMS you need to link this library with your application and use C API defined in [header file](https://github.com/openvinotoolkit/model_server/blob/develop/src/ovms.h). +Server functionalities are encapsulated in shared library built from OVMS source. To include OVMS you need to link this library with your application and use C API defined in [header file](https://github.com/openvinotoolkit/model_server/blob/v2022.3/src/ovms.h). To start model serving you need to spawn process that will keep OVMS alive. Then you can schedule inference both directly from app using C API and gRPC/HTTP endpoints. diff --git a/docs/model_server_grpc_api_kfs.md b/docs/model_server_grpc_api_kfs.md index 87a182488f..716c066b83 100644 --- a/docs/model_server_grpc_api_kfs.md +++ b/docs/model_server_grpc_api_kfs.md @@ -12,7 +12,7 @@ The API includes following endpoints: * Model Metadata API * Inference API -> **NOTE**: Examples of using each of above endpoints can be found in [KServe samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples/README.md). +> **NOTE**: Examples of using each of above endpoints can be found in [KServe samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples/README.md). ## Server Live API @@ -51,7 +51,7 @@ Check [how binary data is handled in OpenVINO Model Server](./binary_input.md) ## See Also -- [Example client code](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples/README.md) shows how to use GRPC API and REST API. +- [Example client code](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples/README.md) shows how to use GRPC API and REST API. - [KServe API](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2) - [gRPC](https://grpc.io/) diff --git a/docs/model_server_rest_api_kfs.md b/docs/model_server_rest_api_kfs.md index b51775812f..6ab8cc8aa2 100644 --- a/docs/model_server_rest_api_kfs.md +++ b/docs/model_server_rest_api_kfs.md @@ -36,7 +36,7 @@ Date: Tue, 09 Aug 2022 09:20:24 GMT Content-Length: 2 ``` -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for getting server liveness with KServe API on HTTP Server Live endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for getting server liveness with KServe API on HTTP Server Live endpoint. ## Server Ready API **Description** @@ -63,7 +63,7 @@ Date: Tue, 09 Aug 2022 09:22:14 GMT Content-Length: 2 ``` -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for getting server readiness with KServe API on HTTP Server Ready endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for getting server readiness with KServe API on HTTP Server Ready endpoint. ## Server Metadata API **Description** @@ -103,7 +103,7 @@ $ curl http://localhost:5000/v2 For detailed description of the response contents see [KServe API docs](https://github.com/kserve/kserve/blob/master/docs/predict-api/v2/required_api.md#server-metadata). -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for getting server metadata with KServe API on HTTP Server Metadata endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for getting server metadata with KServe API on HTTP Server Metadata endpoint. ## Model Ready API **Description** @@ -130,7 +130,7 @@ Date: Tue, 09 Aug 2022 09:25:31 GMT Content-Length: 2 ``` -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for getting model readiness with KServe API on HTTP Model Ready endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for getting model readiness with KServe API on HTTP Model Ready endpoint. @@ -185,7 +185,7 @@ $ curl http://localhost:8000/v2/models/resnet For detailed description of the response contents see [KServe API docs](https://github.com/kserve/kserve/blob/master/docs/predict-api/v2/required_api.md#model-metadata). -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for running getting model metadata with KServe API on HTTP Model Metadata endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for running getting model metadata with KServe API on HTTP Model Metadata endpoint. ## Inference API **Description** @@ -285,4 +285,4 @@ For detailed description of request and response contents see [KServe API docs]( > Note: More efficient way of running inference via REST is sending data in a binary format outside of the JSON object, by using [binary data extension](./binary_input_kfs.md). -See also [code samples](https://github.com/openvinotoolkit/model_server/tree/develop/client/python/kserve-api/samples) for running inference with KServe API on HTTP Inference endpoint. +See also [code samples](https://github.com/openvinotoolkit/model_server/tree/v2022.3/client/python/kserve-api/samples) for running inference with KServe API on HTTP Inference endpoint. diff --git a/src/example/SampleCpuExtension/README.md b/src/example/SampleCpuExtension/README.md index 941048e477..f6f0edfbde 100644 --- a/src/example/SampleCpuExtension/README.md +++ b/src/example/SampleCpuExtension/README.md @@ -8,7 +8,7 @@ custom extension execution. ## Creating cpu_extension library -Compile the library by running `make cpu_extension BASE_OS=ubuntu` in root directory of [Model Server repository](https://github.com/openvinotoolkit/model_server/tree/develop). The implementation of this library slightly differs from the template in OpenVINO™ repository and can be found in [SampleCpuExtension directory](https://github.com/openvinotoolkit/model_server/tree/develop/src/example/SampleCpuExtension). +Compile the library by running `make cpu_extension BASE_OS=ubuntu` in root directory of [Model Server repository](https://github.com/openvinotoolkit/model_server/tree/v2022.3). The implementation of this library slightly differs from the template in OpenVINO™ repository and can be found in [SampleCpuExtension directory](https://github.com/openvinotoolkit/model_server/tree/v2022.3/src/example/SampleCpuExtension). Shared library will be generated in the `lib` folder. Such library can be used to run Model Server, using `--cpu_extension` argument.