YOLO (You Only Look Once) is a series of popular convolutional neural network (CNN) models used for object detection tasks.
This is a BentoML example project, demonstrating how to build an object detection inference API server, using the YOLOv8 model. See here for a full list of BentoML example projects.
git clone https://github.com/bentoml/BentoYolo.git
cd BentoYolo
# Recommend Python 3.11
pip install -r requirements.txt
We have defined a BentoML Service in service.py
. Run bentoml serve
in your project directory to start the Service.
$ bentoml serve .
2024-03-19T10:02:15+0000 [WARNING] [cli] Converting 'YoloV8' to lowercase: 'yolov8'.
2024-03-19T10:02:16+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:YoloV8" listening on http://localhost:3000 (Press CTRL+C to quit)
The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.
CURL
curl -X 'POST' \
'http://localhost:3000/predict' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'image=@demo-image.jpg;type=image/jpeg'
Python client
import bentoml
from pathlib import Path
with bentoml.SyncHTTPClient("http://localhost:3000") as client:
result = client.predict(
image=Path("demo-image.jpg"),
)
After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.
Make sure you have logged in to BentoCloud, then run the following command to deploy it.
bentoml deploy .
Once the application is up and running on BentoCloud, you can access it via the exposed URL.
Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.