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docs: add documentation for running profile capi yolov8 ensemble
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Signed-off-by: Jim Wang <[email protected]>
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# OpenVINO OVMS C-API Yolov8 Ensemble Pipeline Run

OpenVINO Model Server has [many ways to run inferencing pipeline](https://docs.openvino.ai/2023.1/ovms_docs_server_api.html):
TensorFlow Serving gRPC API, KServe gRPC API, TensorFlow Serving REST API, KServe REST API and OVMS C API through OpenVINO model server (OVMS). Here we are demonstrating for using OVMS C API method to run inferencing pipeline yolov5s ensemble models in following steps:

1. Add new section to model configuration file for model server
2. Add pipeline specific files
3. Add environment variable file dependency
4. Add a profile launcher pipeline configuration file
5. Build and run
6. Clean up


## Add New Section To Model Config File for Model Server

The model template configuration file has been updated with model configs of yolov8, efficientnetb0_FP32INT8 and custom configurations, please view [`configs/opencv-ovms/models/2022/config_template.json`](https://github.com/intel-retail/automated-self-checkout/blob/main/configs/opencv-ovms/models/2022/config_template.json) for detail.

!!! Note
The capi yolov8 ensemble uses custom node library, which is different from capi yolov5 ensemble, and the configuration of JSON file is integrated by the profile-launcher on the fly.

## Add pipeline specific files

The pre-processing and post-processing work files are added in directory of [`configs/opencv-ovms/gst_capi/pipelines/capi_yolov8_ensemble/`](https://github.com/intel-retail/automated-self-checkout/blob/main/configs/opencv-ovms/gst_capi/pipelines/capi_yolov8_ensemble/), please view directory for details.

## Add Environment Variable File

You can add multiple environment variable files to `configs/opencv-ovms/envs/` directory for your pipeline, we've added `capi_yolov8_ensemble.env` for yolov8 ensemble pipeline run. Below is a list of explanation for all environment variables and current default values we set, this list can be extended for any future modification.

| EV Name |Default Value | Description |
| --------------------------|-----------------------------------------------------|-------------------------------------------------------|
| RENDER_PORTRAIT_MODE | 1 | rendering in portrait mode, value: 0 or 1 |
| GST_DEBUG | 1 | running GStreamer in debug mode, value: 0 or 1 |
| USE_ONEVPL | 1 | using OneVPL CPU & GPU Support, value: 0 or 1 |
| PIPELINE_EXEC_PATH | pipelines/capi_yolov8_ensemble/capi_yolov8_ensemble | pipeline execution path inside container |
| GST_VAAPI_DRM_DEVICE | /dev/dri/renderD128 | GStreamer VAAPI DRM device input |
| TARGET_GPU_DEVICE | --privileged | allow using GPU devices if any |
| LOG_LEVEL | 0 | [GST_DEBUG log level](https://gstreamer.freedesktop.org/documentation/tutorials/basic/debugging-tools.html?gi-language=c#the-debug-log) to be set when running gst pipeline |
| RENDER_MODE | 1 | option to display the input source video stream with the inferencing results, value: 0 or 1 |
| cl_cache_dir | /home/intel/gst-ovms/.cl-cache | cache directory in container |
| WINDOW_WIDTH | 1280 | display window width |
| WINDOW_HEIGHT | 720 | display window height |
| DETECTION_THRESHOLD | 0.7 | detection threshold value in floating point that needs to be between 0.0 to 1.0 |
| INPUT_TYPE | RTSP_H264 | video input type to us, value: RTSP_H264, FILE_H264, RTSP_H265, or FILE_H265 |


details of yolov8s pipeline environment variable file can be viewed in [`configs/opencv-ovms/envs/capi_yolov8_ensemble.env`](https://github.com/intel-retail/automated-self-checkout/blob/main/configs/opencv-ovms/envs/capi_yolov8_ensemble.env).

## Add A Profile Launcher Configuration File

The details about Profile Launcher configuration can be found [here](./profileLauncherConfigs.md), details for capi yolov8 ensemble pipeline profile launcher configuration can be viewed in [`configs/opencv-ovms/cmd_client/res/capi_yolov8_ensemble/configuration.yaml`](https://github.com/intel-retail/automated-self-checkout/tree/main/configs/opencv-ovms/cmd_client/res/capi_yolov8_ensemble/configuration.yaml)

## Build and Run

Here are the quick start steps to build and run capi yolov5 pipeline profile :

1. Build docker image with profile-launcher: `make build-capi_yolov8_ensemble`
2. Download sample video files: `cd benchmark-scripts/ && ./download_sample_videos.sh && cd ..`
3. Start simulator camera: `make run-camera-simulator`
4. To start the pipeline run: `PIPELINE_PROFILE="capi_yolov8_ensemble" RENDER_MODE=1 sudo -E ./run.sh --platform core --inputsrc rtsp://127.0.0.1:8554/camera_0`
!!! Note
The pipeline will automatically download the OpenVINO model files listed in [`configs/opencv-ovms/models/2022/config_template.json`](https://github.com/intel-retail/automated-self-checkout/blob/main/configs/opencv-ovms/models/2022/config_template.json)

# Clean Up

To stop existing container: `make clean-capi_yolov8_ensemble`
To stop all running containers including camera simulator and remove all log files: `make clean-all`

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