jarray 0.0.0

Jina is the cloud-native neural search framework for any kind of data

Cloud-Native Neural Search^? Framework for Any Kind of Data

Jina is a neural search framework that empowers anyone to build SOTA and scalable deep learning search applications in minutes.

⏱️ Save time - The design pattern of neural search systems. Native support for PyTorch/Keras/ONNX/Paddle. Build solutions in just minutes.

🌌 All data types - Process, index, query, and understand videos, images, long/short text, audio, source code, PDFs, etc.

🌩️ Local & cloud friendly - Distributed architecture, scalable & cloud-native from day one. Same developer experience on both local and cloud.

🍱 Own your stack - Keep end-to-end stack ownership of your solution. Avoid integration pitfalls you get with fragmented, multi-vendor, generic legacy tools.

Install

pip install -U jina

More install options including Conda, Docker, Windows can be found here.

Documentation

Get Started

We promise you can build a scalable ResNet-powered image search service in 20 minutes or less, from scratch. If not, you can forget about Jina.

Basic Concepts

Document, Executor, and Flow are three fundamental concepts in Jina.

• Document is the basic data type in Jina;
• Executor is how Jina processes Documents;
• Flow is how Jina streamlines and distributes Executors.

Leveraging these three components, let's build an app that find similar images using ResNet50.

ResNet50 Image Search in 20 Lines

^{💡 Preliminaries: download dataset, install PyTorch & Torchvision}

from jina import DocumentArray, Document

def preproc(d: Document):
    return (d.load_uri_to_image_blob()  # load
             .set_image_blob_normalization()  # normalize color 
             .set_image_blob_channel_axis(-1, 0))  # switch color axis
docs = DocumentArray.from_files('img/*.jpg').apply(preproc)

import torchvision
model = torchvision.models.resnet50(pretrained=True)  # load ResNet50
docs.embed(model, device='cuda')  # embed via GPU to speedup

q = (Document(uri='img/00021.jpg')  # build query image & preprocess
     .load_uri_to_image_blob()
     .set_image_blob_normalization()
     .set_image_blob_channel_axis(-1, 0))
q.embed(model)  # embed
q.match(docs)  # find top-20 nearest neighbours, done!

Done! Now print q.matches and you'll see the URIs of the most similar images.

Add three lines of code to visualize them:

for m in q.matches:
    m.set_image_blob_channel_axis(0, -1).set_image_blob_inv_normalization()
q.matches.plot_image_sprites()

Sweet! FYI, you can use Keras, ONNX, or PaddlePaddle for the embedding model. Jina supports them well.

As-a-Service in 10 Extra Lines

With an extremely trivial refactoring and ten extra lines of code, you can make the local script a ready-to-serve service:

1. Import what we need.

   from jina import Document, DocumentArray, Executor, Flow, requests
   

2. Copy-paste the preprocessing step and wrap it via Executor:

   class PreprocImg(Executor):
       @requests
       def foo(self, docs: DocumentArray, **kwargs):
           for d in docs:
               (d.load_uri_to_image_blob()  # load
                .set_image_blob_normalization()  # normalize color
                .set_image_blob_channel_axis(-1, 0))  # switch color axis
   

3. Copy-paste the embedding step and wrap it via Executor:

   class EmbedImg(Executor):
       def __init__(self, **kwargs):
           super().__init__(**kwargs)
           import torchvision
           self.model = torchvision.models.resnet50(pretrained=True)        
   
       @requests
       def foo(self, docs: DocumentArray, **kwargs):
           docs.embed(self.model)
   

4. Wrap the matching step into an Executor:

   class MatchImg(Executor):
       _da = DocumentArray()
   
       @requests(on='/index')
       def index(self, docs: DocumentArray, **kwargs):
           self._da.extend(docs)
   
       @requests(on='/search')
       def foo(self, docs: DocumentArray, **kwargs):
           docs.match(self._da)
           for d in docs.traverse_flat('r,m'):  # only require for visualization
               d.convert_uri_to_datauri()  # convert to datauri
               d.pop('embedding', 'blob')  # remove unnecessary fields for save bandwidth
   

5. Connect all Executors in a Flow, scale embedding to 3:

   f = Flow(port_expose=12345, protocol='http').add(uses=PreprocImg).add(uses=EmbedImg, replicas=3).add(uses=MatchImg)
   

   Plot it via f.plot('flow.svg') and you get:

6. Index image data and serve REST query publicly:

   with f:
       f.post('/index', DocumentArray.from_files('img/*.jpg'), show_progress=True, request_size=8)
       f.block()
   

Done! Now query it via curl and you get the most similar images:

Or go to http://0.0.0.0:12345/docs and test requests via a Swagger UI:

Or use a Python client to access the service:

from jina import Client, Document
from jina.types.request import Response

def print_matches(resp: Response):  # the callback function invoked when task is done
    for idx, d in enumerate(resp.docs[0].matches):  # print top-3 matches
        print(f'[{idx}]{d.scores["cosine"].value:2f}: "{d.uri}"')

c = Client(protocol='http', port=12345)  # connect to localhost:12345
c.post('/search', Document(uri='img/00021.jpg'), on_done=print_matches)

At this point, you probably have taken 15 minutes but here we are: an image search service with rich features:

✅ Solution as microservices	✅ Scale in/out any component	✅ Query via HTTP/WebSocket/gRPC/Client
✅ Distribute/Dockerize components	✅ Async/non-blocking I/O	✅ Extendable REST interface

Deploy to Kubernetes in 7 Minutes

Have another seven minutes? We'll show you how to bring your service to the next level by deploying it to Kubernetes.

1. Create a Kubernetes cluster and get credentials (example in GCP, more K8s providers here):

   gcloud container clusters create test --machine-type e2-highmem-2  --num-nodes 1 --zone europe-west3-a
   gcloud container clusters get-credentials test --zone europe-west3-a --project jina-showcase
   

2. Move each Executor class to a separate folder with one Python file in each:
   • PreprocImg -> 📁 preproc_img/exec.py
   • EmbedImg -> 📁 embed_img/exec.py
   • MatchImg -> 📁 match_img/exec.py
3. Push all Executors to Jina Hub:

   jina hub push preproc_img
   jina hub push embed_img
   jina hub push embed_img
   

   You will get three Hub Executors that can be used via Docker container.
4. Adjust Flow a bit and open it:

   f = Flow(name='readme-flow', port_expose=12345, infrastructure='k8s').add(uses='jinahub+docker://PreprocImg').add(uses='jinahub+docker://EmbedImg', replicas=3).add(uses='jinahub+docker://MatchImg')
   with f:
       f.block()
   

Intrigued? Find more about Jina from our docs.

Run Quick Demo

• 👗 Fashion image search: jina hello fashion
• 🤖 QA chatbot: pip install "jina[demo]" && jina hello chatbot
• 📰 Multimodal search: pip install "jina[demo]" && jina hello multimodal
• 🍴 Fork the source of a demo to your folder: jina hello fork fashion ../my-proj/

Support

• Join our Slack community to chat to our engineers about your use cases, questions, and support queries.
• Join our Engineering All Hands meet-up to discuss your use case and learn Jina's new features.
  • When? The second Tuesday of every month
  • Where? Zoom (see our public calendar/.ical/Meetup group) and live stream on YouTube
• Subscribe to the latest video tutorials on our YouTube channel

Join Us

Jina 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.

Contributing

We welcome all kinds of contributions from the open-source community, individuals and partners. We owe our success to your active involvement.

• Release cycles and development stages
• Contributing guidelines
• Code of conduct