Welcome to CLIP-as-service!#

CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions.

โšก Fast: Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks.

๐Ÿซ Elastic: Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing.

๐Ÿฅ Easy-to-use: No learning curve, minimalist design on client and server. Intuitive and consistent API for image and sentence embedding.

๐Ÿ‘’ Modern: Async client support. Easily switch between gRPC, HTTP, WebSocket protocols with TLS and compression.

๐Ÿฑ Integration: Smooth integration with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solutions in no time.

[*] with default config (single replica, PyTorch no JIT) on GeForce RTX 3090.

Try it!#

An always-online server api.clip.jina.ai loaded with ViT-L-14-336::openai is there for you to play & test. Before you start, make sure you have created an access token from our console website, or via CLI as described in this guide.

jina auth token create <name of PAT> -e <expiration days>

Then, you need to configure the access token in the parameter credential of the client in python or set it in the HTTP request header Authorization as <your access token>.

pip install clip-client
from clip_client import Client

c = Client(
    credential={'Authorization': '<your access token>'}

r = c.encode(
        'First do it',
        'then do it right',
        'then do it better',
curl \
-X POST https://api.clip.jina.ai:8443/post \
-H 'Content-Type: application/json' \
-H 'Authorization: <your access token>' \
-d '{"data":[{"text": "First do it"}, 
    {"text": "then do it right"}, 
    {"text": "then do it better"}, 
    {"uri": "https://picsum.photos/200"}], 


PyPI is the latest version.

Make sure you are using Python 3.7+. You can install the client and server independently. It is not required to install both: e.g. you can install clip_server on a GPU machine and clip_client on a local laptop.

pip install clip-client
pip install clip-server
pip install "clip_server[onnx]"
pip install nvidia-pyindex 
pip install "clip_server[tensorrt]"

Quick check#

After installing, you can run the following commands for a quick connectivity check.

Start the server#

python -m clip_server
python -m clip_server onnx-flow.yml
python -m clip_server tensorrt-flow.yml

At the first time starting the server, it will download the default pretrained model, which may take a while depending on your network speed. Then you will get the address information similar to the following:

โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ ๐Ÿ”— Endpoint โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
โ”‚  ๐Ÿ”—     Protocol                   GRPC  โ”‚
โ”‚  ๐Ÿ         Local  โ”‚
โ”‚  ๐Ÿ”’      Private  โ”‚
|  ๐ŸŒ       Public  |

This means the server is ready to serve. Note down the three addresses shown above, you will need them later.

Connect from client#


Depending on the location of the client and server. You may use different IP addresses:

  • Client and server are on the same machine: use local address, e.g.

  • Client and server are connected to the same router: use private network address, e.g.

  • Server is in public network: use public network address, e.g.

Run the following Python script:

from clip_client import Client

c = Client('grpc://')

will give you:

 Roundtrip  16ms  100%
โ”œโ”€โ”€  Client-server network  8ms  49%
โ””โ”€โ”€  Server  8ms  51%
    โ”œโ”€โ”€  Gateway-CLIP network  2ms  25%
    โ””โ”€โ”€  CLIP model  6ms  75%
{'Roundtrip': 15.684750003856607, 'Client-server network': 7.684750003856607, 'Server': 8, 'Gateway-CLIP network': 2, 'CLIP model': 6}

It means the client and the server are now connected. Well done!


Join Us#

CLIP-as-service is backed by Jina AI and licensed under Apache-2.0. We are actively hiring AI engineers, solution engineers to build the next neural search ecosystem in open-source.

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