- You have installed Python 3.11 and
pip
. See the Python downloads page to learn more. - You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
- (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.
git clone https://github.com/bentoml/BentoResnetTensorflow.git
cd BentoResnet
pip install -r requirements.txt
Run the following commands to download Resnet V2 Object detection model and import it into BentoML's model store
python import_model.py
# list models in BentoML's model store
bentoml models list
We have defined a BentoML Service in service.py
. Run bentoml serve
in your project directory to start the Service.
$ bentoml serve .
2024-01-08T09:07:28+0000 [INFO] [cli] Prometheus metrics for HTTP BentoServer from "service:Resnet" can be accessed at http://localhost:3000/metrics.
2024-01-08T09:07:28+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:Resnet" listening on http://localhost:3000 (Press CTRL+C to quit)
Model resnet loaded device: cuda
The Service is accessible at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways:
CURL
curl -s \
-X POST \
-F '[email protected]' \
http://localhost:3000/classify
Python client
import bentoml
from pathlib import Path
with bentoml.SyncHTTPClient("http://localhost:3000") as client:
result = client.classify(
images=[
Path("cat1.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.