A containerized REST API around OpenAI's CLIP model.

Last update: Nov 06, 2022

Overview

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:

[
    {
        "url": "https://images.unsplash.com/photo-1597308680537-1ba44407ffc0?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=1834&q=80", 
        "labels": [
            "woman with black shirt and a bag", 
            "woman with a bag", 
            "person with a bag", 
            "bag", "person"
        ], 
        "probs": [1.0, 1.7488513970320696e-09, 1.1663764917350243e-19, 4.179975909038141e-30, 3.77612043676229e-30]
    }, 
    {
        "url": "https://images.unsplash.com/photo-1589270216117-7972b3082c7d?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=1834&q=80", 
        "labels": [
            "person with a bag", 
            "woman with black shirt and a bag", 
            "bag", 
            "woman with a bag", 
            "person"
        ], 
        "probs": [1.0, 2.4879632576357835e-08, 2.065714813830402e-13, 7.658033346455602e-15, 1.1307645811408335e-23]
    }
]

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.

A containerized REST API around OpenAI's CLIP model.

Related tags

Overview

OpenAI's CLIP — REST API

Running the container locally

Sending requests:

SageMaker Integration

Owner

Santiago Valdarrama

Clockwork Convnets for Video Semantic Segmentation

A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

A repository built on the Flow software package to explore cyber-security attacks on intelligent transportation systems.

Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

Video Autoencoder: self-supervised disentanglement of 3D structure and motion

Addon and nodes for working with structural biology and molecular data in Blender.

Rank1 Conversation Emotion Detection Task

FLVIS: Feedback Loop Based Visual Initial SLAM

A Real-World Benchmark for Reinforcement Learning based Recommender System

Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.

PyTorch reimplementation of Diffusion Models

Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

Face-Recognition-based-Attendance-System - An implementation of Attendance System in python.

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

LibMTL: A PyTorch Library for Multi-Task Learning

face_recognization (FaceNet) + TFHE (HNP) + hand_face_detection (Mediapipe)

Benchmarks for Model-Based Optimization