CLIP (Contrastive Language–Image Pre-training) is a machine learning model developed by OpenAI. It is versatile and excels in tasks like zero-shot learning, image classification, and image-text matching without needing specific training for each task.

This is a BentoML example project, demonstrating how to build a CLIP inference API server, using the clip-vit-base-patch32 model. See here for a full list of BentoML example projects.

git clone https://github.com/bentoml/BentoClip.git
cd BentoClip

# 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 .

2024-01-08T09:07:28 0000 [INFO] [cli] Prometheus metrics for HTTP BentoServer from "service:CLIPService" can be accessed at http://localhost:3000/metrics.
2024-01-08T09:07:28 0000 [INFO] [cli] Starting production HTTP BentoServer from "service:CLIPService" listening on http://localhost:3000 (Press CTRL C to quit)
Model clip 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/encode_image

Python client

import bentoml
from pathlib import Path

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
    result = client.encode_image(
        items=[
            Path("demo.jpg"),
        ],
    )

For detailed explanations of the Service code, see CLIP embeddings.

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.