CLIP API Service

Discover the effortless integration of OpenAI's innovative CLIP model with our streamlined API service.
Powered by BentoML 🍱

📖 Introduction 📖

CLIP, or Contrastive Language-Image Pretraining, is a cutting-edge AI model that comprehends and connects text and images, revolutionizing how we interpret online data.

This library provides you with an instant, easy-to-use interface for CLIP, allowing you to harness its capabilities without any setup hassles. BentoML takes care of all the complexity of serving the model!

🔧 Installation 🔧

Ensure that you have Python 3.8 or newer and pip installed on your system. We highly recommend using a Virtual Environment to avoid any potential package conflicts.

To install the service, enter the following command:

pip install clip-api-service

Once the installation process is complete, you can start the service by running:

clip-api-service serve --model-name=ViT-B-32:openai

Your service is now running! Interact with it via the Swagger UI at localhost:3000

🎯 Use cases 🎯

Harness the capabilities of the CLIP API service across a range of applications:

Encode

Text and Image Embedding
- Use encode to transform text or images into meaningful embeddings. This makes it possible to perform tasks such as:
  1. Neural Search: Utilize encoded embeddings to power a search engine capable of understanding and indexing images based on their textual descriptions, and vice versa.
  2. Custom Ranking: Design a ranking system based on embeddings, providing unique ways to sort and categorize data according to your context.

Rank

Zero-Shot Image Classification
- Use rank to perform image classification without any training. For example:
  1. Given a set of images, classify an image as being "a picture of a dog" or "a picture of a cat".
  2. More complex classifications such as recognizing different breeds of dogs can also be performed, illustrating the versatility of the CLIP API service.
Visual Reasoning
- The rank function can also be used to provide reasoning about visual scenarios. For instance:

Visual Scenario	Candidates	Output
Counting Objects	This is a picture of 1 dog This is a picture of 2 dogs This is a picture of 3 dogs	Image matched with "3 dogs"
Identifying Colors	The car is red The car is blue The car is green	Image matched with "blue car"
Understanding Motion	The car is parked The car is moving The car is turning	Image matched with "parked car"
Recognizing Location	The car is in the suburb The car is on the highway The car is in the street	Image matched with "car in the street"
Relative Positioning	The big car is on the left, the small car is on the right The small car is on the left, the big car is on the right	Image matched with the provided description

🚀 Deploying to Production 🚀

Effortlessly transition your project into a production-ready application using BentoCloud, the production-ready platform for managing and deploying machine learning models.

Start by creating a BentoCloud account. Once you've signed up, log in to your BentoCloud account using the command:

bentoml cloud login --api-token <your-api-token> --endpoint <bento-cloud-endpoint>

Note: Replace <your-api-token> and <bento-cloud-endpoint> with your specific API token and the BentoCloud endpoint respectively.

Next, build your BentoML service using the build command:

clip-api-service build --model-name=ViT-B-32:openai

Then, push your freshly-built Bento service to BentoCloud using the push command:

bentoml push <name:version>

Lastly, deploy this application to BentoCloud with a single bentoml deployment create command following the deployment instructions.

BentoML offers a number of options for deploying and hosting online ML services into production, learn more at Deploying a Bento.

📚 Reference 📚

API reference

`/encode`

Accepts either:

img_uri : An Image URI, i.e https://hips.hearstapps.com/hmg-prod/images/dog-puppy-on-garden-royalty-free-image-1586966191.jpg
text : A string
img_blob : Base64 encoded string

Returns a vector of embeddings of length 768.

Example:

curl -X 'POST' \
  'http://localhost:3000/encode' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '[
  {
    "img_uri": "https://hips.hearstapps.com/hmg-prod/images/dog-puppy-on-garden-royalty-free-image-1586966191.jpg"
  },
  {
    "text": "picture of a dog"
  },
  {
    "img_blob": "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"
  }
]'

`/rank`

Accepts a list of queries and a list of candidates. Similar to above, queries and candidates are either:

img_uri : An Image URI, i.e https://hips.hearstapps.com/hmg-prod/images/dog-puppy-on-garden-royalty-free-image-1586966191.jpg
text : A string
img_blob : Base64 encoded string

Returns a list of probabilies and cosine similarities of each candidate with respect to the query.

Example:

curl -X 'POST' \
  'http://localhost:3000/rank' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "queries": [
    {
      "img_uri": "https://hips.hearstapps.com/hmg-prod/images/dog-puppy-on-garden-royalty-free-image-1586966191.jpg"
    }
  ],
  "candidates": [
    {
      "text": "picture of a dog"
    },
    {
      "text": "picture of a cat"
    },
    {
      "text": "picture of a bird"
    },
    {
      "text": "picture of a car"
    },
    {
      "text": "picture of a plane"
    },
    {
      "text": "picture of a boat"
    }
  ]
}'

And the response looks like:

{
  "probabilities": [
    [
      0.9958375692367554,
      0.0022114247549325228,
      0.001514736912213266,
      0.00011969256593147293,
      0.00019143625104334205,
      0.0001251235808013007
    ]
  ],
  "cosine_similarities": [
    [
      0.2297772467136383,
      0.16867777705192566,
      0.16489382088184357,
      0.13951312005519867,
      0.14420939981937408,
      0.13995687663555145
    ]
  ]
}

CLI reference

`serve`

Spins up a HTTP Server with the model of your choice.

Arguments:

--model-name : Name of the CLIP model. Use list_models to see the list of available model. Default: openai/clip-vit-large-patch14

`build`

Builds a Bento with the model of your choice

Arguments:

--model-name : Name of the CLIP model. Use list_models to see the list of available model. Default: openai/clip-vit-large-patch14

`list_models`

List all available CLIP models.

parano/CLIP-API-service