clip-container's Introduction

OpenAI's CLIP — REST API

This is a container wrapping OpenAI's CLIP model in a RESTful interface.

Running the container locally

First, build the container:

docker build -t clip-container:latest .

Then, you can run it:

docker run -it -p 8080:8080 --name "clip-container" --rm clip-container:latest /opt/ml/code/serve

Sending requests:

The container exposes two different endpoints:

GET /ping: Returns 200 status if the container is working properly.
POST /invocations: Processes a list of images and returns the list of labels with their corresponding probabilities.

Here is an example request assuming the container is listening in port 8080:

curl --location --request POST 'http://localhost:8080/invocations' \
--header 'Content-Type: application/json' \
--data-raw '{
    "images": [
        "https://images.unsplash.com/photo-1597308680537-1ba44407ffc0?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=1834&q=80",
        "https://images.unsplash.com/photo-1589270216117-7972b3082c7d?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=1834&q=80"],
    "classes": ["person", "bag", "person with a bag", "woman riding a horse", "woman with a bag", "woman with black shirt and a bag"]
}'

The response looks like this:

[
    [
        [
            5,
            4,
            2
        ],
        [
            "woman with black shirt and a bag",
            "woman with a bag",
            "person with a bag"
        ],
        [
            0.8420771360397339,
            0.13471755385398865,
            0.020683499053120613
        ]
    ],
    [
        [
            2,
            5,
            4
        ],
        [
            "person with a bag",
            "woman with black shirt and a bag",
            "woman with a bag"
        ],
        [
            0.8793349266052246,
            0.06026121601462364,
            0.027413660660386086
        ]
    ]
]

SageMaker Integration

This container is compatible with SageMaker so you should be able to host it as a SageMaker endpoint with no modifications. The code supports GPU and CPU instances.

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