Revert "chore!: remove OVMSAdapter (#425)"

This reverts commit 6b54085.

Author: tybulewicz

.github/workflows/pre_commit.yml

@@ -121,3 +121,4 @@ jobs:
       - name: Some tests failed
         if: ${{ contains(needs.*.result, 'failure') }}
         run: exit 1
+

.gitignore

@@ -145,3 +145,4 @@ docs/source/_build/
 .vscode/
 
 data/
+ovms_models/

README.md

@@ -11,7 +11,7 @@
 
 ## Introduction
 
-Model API is a set of wrapper classes for particular tasks and model architectures, simplifying data preprocess and postprocess as well as routine procedures (model loading, asynchronous execution, etc.). It is aimed at simplifying end-to-end model inference. The Model API is based on the OpenVINO inference API.
+Model API is a set of wrapper classes for particular tasks and model architectures, simplifying data preprocess and postprocess as well as routine procedures (model loading, asynchronous execution, etc.). It is aimed at simplifying end-to-end model inference for different deployment scenarios, including local execution and serving. The Model API is based on the OpenVINO inference API.
 
 ## How it works
 
@@ -29,6 +29,7 @@ Training Extensions embed all the metadata required for inference into model fil
 
 - Python API
 - Synchronous and asynchronous inference
+- Local inference and serving through the rest API
 - Model preprocessing embedding for faster inference
 
 ## Installation
@@ -41,6 +42,7 @@ Training Extensions embed all the metadata required for inference into model fil
 from model_api.models import Model
 
 # Create a model wrapper from a compatible model generated by OpenVINO Training Extensions
+# Use URL to work with OVMS-served model, e.g. "localhost:9000/models/ssdlite_mobilenet_v2"
 model = Model.create_model("model.xml")
 
 # Run synchronous inference locally
@@ -52,7 +54,7 @@ print(f"Inference result: {result}")
 
 ## Prepare a model for `InferenceAdapter`
 
-There are usecases when it is not possible to modify an internal `ov::Model` and it is hidden behind `InferenceAdapter`. `create_model()` can construct a model from a given `InferenceAdapter`. That approach assumes that the model in `InferenceAdapter` was already configured by `create_model()` called with a string (a path or a model name). It is possible to prepare such model:
+There are usecases when it is not possible to modify an internal `ov::Model` and it is hidden behind `InferenceAdapter`. For example the model can be served using [OVMS](https://github.com/openvinotoolkit/model_server). `create_model()` can construct a model from a given `InferenceAdapter`. That approach assumes that the model in `InferenceAdapter` was already configured by `create_model()` called with a string (a path or a model name). It is possible to prepare such model:
 
 ```python
 model = DetectionModel.create_model("~/.cache/omz/public/ssdlite_mobilenet_v2/FP16/ssdlite_mobilenet_v2.xml")

docs/source/adapters/index.md

@@ -11,6 +11,10 @@
 [todo]
 :::
 
+:::{grid-item-card} Ovms Adapter
+:link: ./ovms_adapter
+:link-type: doc
+
 [todo]
 :::
 :::{grid-item-card} Onnx Adapter
@@ -41,5 +45,6 @@
 ./inference_adapter
 ./onnx_adapter
 ./openvino_adapter
+./ovms_adapter
 ./utils
 ```

docs/source/adapters/ovms_adapter.md

@@ -0,0 +1,8 @@
+# Ovms Adapter
+
+```{eval-rst}
+.. automodule:: model_api.adapters.ovms_adapter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```

examples/serving_api/README.md

@@ -0,0 +1,40 @@
+# Serving API example
+
+This example demonstrates how to use a Python API of OpenVINO Model API for a remote inference of models hosted with [OpenVINO Model Server](https://docs.openvino.ai/latest/ovms_what_is_openvino_model_server.html). This tutorial assumes that you are familiar with Docker subsystem and includes the following steps:
+
+- Run Docker image with
+- Instantiate a model
+- Run inference
+- Process results
+
+## Prerequisites
+
+- Install Model API from source. Please refer to the main [README](../../../README.md) for details.
+- Install Docker. Please refer to the [official documentation](https://docs.docker.com/get-docker/) for details.
+- Install OVMS client into the Python environment:
+
+  ```bash
+  pip install ovmsclient
+  ```
+
+- Download a model by running a Python code with Model API, see Python [exaple](../../synchronous_api/README.md) and resave a configured model at OVMS friendly folder layout:
+
+  ```python
+  from model_api.models import DetectionModel
+
+  DetectionModel.create_model("ssd_mobilenet_v1_fpn_coco").save("/home/user/models/ssd_mobilenet_v1_fpn_coco/1/ssd_mobilenet_v1_fpn_coco.xml")
+  ```
+
+- Run docker with OVMS server:
+
+  ```bash
+  docker run -d -v /home/user/models:/models -p 8000:8000 openvino/model_server:latest --model_path /models/ssd_mobilenet_v1_fpn_coco --model_name ssd_mobilenet_v1_fpn_coco --rest_port 8000 --nireq 4 --target_device CPU
+  ```
+
+## Run example
+
+To run the example, please execute the following command:
+
+```bash
+python run.py <path_to_image>
+```

examples/serving_api/run.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+#
+# Copyright (C) 2020-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import sys
+
+import cv2
+
+from model_api.models import DetectionModel
+
+
+def main():
+    if len(sys.argv) != 2:
+        usage_message = f"Usage: {sys.argv[0]} <path_to_image>"
+        raise RuntimeError(usage_message)
+
+    image = cv2.cvtColor(cv2.imread(sys.argv[1]), cv2.COLOR_BGR2RGB)
+    if image is None:
+        error_message = f"Failed to read the image: {sys.argv[1]}"
+        raise RuntimeError(error_message)
+
+    # Create Object Detection model specifying the OVMS server URL
+    model = DetectionModel.create_model(
+        "localhost:8000/v2/models/ssd_mobilenet_v1_fpn_coco",
+        model_type="ssd",
+    )
+    detections = model(image)
+    print(f"Detection results: {detections}")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -31,6 +31,9 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
+ovms = [
+  "tritonclient[http]<2.59",
+]
 tests = [
     "httpx",
     "pytest",

src/README.md

@@ -79,13 +79,25 @@ The following tasks can be solved with wrappers usage:
 
 Model API wrappers are executor-agnostic, meaning it does not implement the specific model inference or model loading, instead it can be used with different executors having the implementation of common interface methods in adapter class respectively.
 
-Currently, `OpenvinoAdapter` and `ONNXRuntimeAdapter` are supported.
+Currently, `OpenvinoAdapter` and `OVMSAdapter` are supported.
 
 ### OpenVINO Adapter
 
 `OpenvinoAdapter` hides the OpenVINO™ toolkit API, which allows Model API wrappers launching with models represented in Intermediate Representation (IR) format.
 It accepts a path to either `xml` model file or `onnx` model file.
 
+### OpenVINO Model Server Adapter
+
+`OVMSAdapter` hides the OpenVINO Model Server python client API, which allows Model API wrappers launching with models served by OVMS.
+
+Refer to **[`OVMSAdapter`](adapters/ovms_adapter.md)** to learn about running demos with OVMS.
+
+For using OpenVINO Model Server Adapter you need to install the package with extra module:
+
+```sh
+pip install <omz_dir>/demos/common/python[ovms]
+```
+
 ### ONNXRuntime Adapter
 
 `ONNXRuntimeAdapter` hides the ONNXRuntime, which Model API wrappers launching with models represented in ONNX format.

src/model_api/adapters/__init__.py

@@ -5,13 +5,15 @@
 
 from .onnx_adapter import ONNXRuntimeAdapter
 from .openvino_adapter import OpenvinoAdapter, create_core, get_user_config
+from .ovms_adapter import OVMSAdapter
 from .utils import INTERPOLATION_TYPES, RESIZE_TYPES, InputTransform, Layout
 
 __all__ = [
     "create_core",
     "get_user_config",
     "Layout",
     "OpenvinoAdapter",
+    "OVMSAdapter",
     "ONNXRuntimeAdapter",
     "RESIZE_TYPES",
     "InputTransform",