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Jina logo: Build cross-modal and multi-modal applications on the cloud 路 Neural Search 路 Creative AI 路 Cloud Native


Build cross-modal and multi-modal applications[?] on the cloud

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Jina is a MLOps framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer.

Applications built with Jina enjoy the following features out-of-the-box:

馃寣 Universal

  • Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI's DocArray.
  • Support all mainstream deep learning frameworks.
  • Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS.

Performance

  • Intuitive design pattern for high-performance microservices.
  • Scaling at ease: set replicas, sharding in one line.
  • Duplex streaming between client and server.
  • Async and non-blocking data processing over dynamic flows.

鈽侊笍 Cloud-native

  • Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub.
  • Full observability via Prometheus and Grafana.
  • Fast deployment to Kubernetes, Docker Compose.

馃嵄 Ecosystem

  • Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build.
  • Free CPU/GPU hosting via Jina Cloud.

Jina in Jina AI neural search ecosystem

Documentation

Understand Jina better
馃摋 What is Jina? 馃攳 Is Jina MLOps for search? 馃啔 How Jina compares to alternatives? 鈽侊笍 What is Cloud-Native?
馃敭 What is cross-modal and multimodal锛 馃К What is neural search? 馃帹 What is creative AI?

Install

pip install jina

More install options on Apple Silicon and Windows can be found here.

Get Started

Basic Concepts

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

  • Document is the fundamental data structure.
  • Executor is a Python class with functions that use Documents as IO.
  • Flow ties Executors together into a pipeline and exposes it with an API gateway.

The full glossary is explained here.


Jina: Streamline AI & ML Product Delivery

Streamline AI & ML Product Delivery

A new project starts from local. With Jina, you can easily leverage existing deep learning stacks, improve the quality and quickly build the POC.

import torch
from jina import DocumentArray

model = torch.nn.Sequential(
    torch.nn.Linear(
        in_features=128,
        out_features=128,
    ),
    torch.nn.ReLU(),
    torch.nn.Linear(in_features=128, out_features=32),
)


docs = DocumentArray.from_files('left/*.jpg')
docs.embed(model)

Moving to production, Jina enhances the POC with service endpoint, scalability and adds cloud-native features, making it ready for production without refactoring.

from jina import DocumentArray, Executor, requests
from .embedding import model


class MyExec(Executor):
    @requests(on='/embed')
    async def embed(self, docs: DocumentArray, **kwargs):
        docs.embed(model)
jtype: Flow
with:
  port: 12345
executors:
- uses: MyExec
  replicas: 2

Finally, the project can be easily deployed to the cloud and serve real traffic.

jina cloud deploy ./

Jina: No Infrastructure Complexity, High Engineering Efficiency

Hello world example

Leveraging these three concepts, let's look at a simple example below:

from jina import DocumentArray, Executor, Flow, requests


class MyExec(Executor):
    @requests
    async def add_text(self, docs: DocumentArray, **kwargs):
        for d in docs:
            d.text += 'hello, world!'


f = Flow().add(uses=MyExec).add(uses=MyExec)

with f:
    r = f.post('/', DocumentArray.empty(2))
    print(r.texts)
  • The first line imports three concepts we just introduced;
  • MyExec defines an async function add_text that receives DocumentArray from network requests and appends "hello, world" to .text;
  • f defines a Flow streamlined two Executors in a chain;
  • The with block opens the Flow, sends an empty DocumentArray to the Flow, and prints the result.

Running it gives you:

Running a simple hello-world program

At the last line we see its output ['hello, world!hello, world!', 'hello, world!hello, world!'].

While one could use standard Python with the same number of lines and get the same output, Jina accelerates time to market of your application by making it more scalable and cloud-native. Jina also handles the infrastructure complexity in production and other Day-2 operations so that you can focus on the data application itself.


Jina: Scalability and concurrency at ease

Scalability and concurrency at ease

The example above can be refactored into a Python Executor file and a Flow YAML file:

toy.yml executor.py
jtype: Flow
with:
  port: 51000
  protocol: grpc
executors:
- uses: MyExec
  name: foo
  py_modules:
    - executor.py
- uses: MyExec
  name: bar
  py_modules:
    - executor.py
from jina import DocumentArray, Executor, requests


class MyExec(Executor):
    @requests
    async def add_text(self, docs: DocumentArray, **kwargs):
        for d in docs:
            d.text += 'hello, world!'

Run the following command in the terminal:

jina flow --uses toy.yml

Running a simple hello-world program

The server is successfully started, and you can now use a client to query it.

from jina import Client, Document

c = Client(host='grpc://0.0.0.0:51000')
c.post('/', Document())

This simple refactoring allows developers to write an application in the client-server style. The separation of Flow YAML and Executor Python file does not only make the project more maintainable but also brings scalability and concurrency to the next level:

  • The data flow on the server is non-blocking and async. New request is handled immediately when an Executor is free, regardless if previous request is still being processed.
  • Scalability can be easily achieved by the keywords replicas and needs in YAML/Python. Load-balancing is automatically added when necessary to ensure the maximum throughput.
toy.yml Flowchart
jtype: Flow
with:
  port: 51000
  protocol: grpc
executors:
- uses: MyExec
  name: foo
  py_modules:
    - executor.py
  replicas: 2
- uses: MyExec
  name: bar
  py_modules:
    - executor.py
  replicas: 3
  needs: gateway
- needs: [foo, bar]
  name: baz

Running a simple hello-world program

  • You now have an API gateway that supports gRPC (default), Websockets, and HTTP protocols with TLS.
  • The communication between clients and the API gateway is duplex.
  • The API gateway allows you to route request to a specific Executor while other parts of the Flow are still busy, via .post(..., target_executor=...)

Jina: Seamless Container Integration

Seamless Container integration

Without having to worry about dependencies, you can easily share your Executors with others; or use public/private Executors in your project thanks to Jina Hub.

To create an Executor:

jina hub new 

To push it to Jina Hub:

jina hub push .

To use a Hub Executor in your Flow:

Docker container Sandbox Source
YAML uses: jinahub+docker://MyExecutor uses: jinahub+sandbox://MyExecutor uses: jinahub://MyExecutor
Python .add(uses='jinahub+docker://MyExecutor') .add(uses='jinahub+sandbox://MyExecutor') .add(uses='jinahub://MyExecutor')

Behind this smooth experience is advanced management of Executors:

  • Automated builds on the cloud
  • Store, deploy, and deliver Executors cost-efficiently;
  • Automatically resolve version conflicts and dependencies;
  • Instant delivery of any Executor via Sandbox without pulling anything to local.

Jina: Seamless Container Integration

Fast-lane to cloud-native

Using Kubernetes becomes easy:

jina export kubernetes flow.yml ./my-k8s
kubectl apply -R -f my-k8s

Using Docker Compose becomes easy:

jina export docker-compose flow.yml docker-compose.yml
docker-compose up

Using Prometheus becomes easy:

from jina import Executor, requests, DocumentArray


class MyExec(Executor):
    @requests
    def encode(self, docs: DocumentArray, **kwargs):
        with self.monitor('preprocessing_seconds', 'Time preprocessing the requests'):
            docs.tensors = preprocessing(docs)
        with self.monitor(
            'model_inference_seconds', 'Time doing inference the requests'
        ):
            docs.embedding = model_inference(docs.tensors)

Using Grafana becomes easy, just download this JSON and import it into Grafana:

Jina: Seamless Container Integration

What cloud-native technology is still challenging to you? Tell us, we will handle the complexity and make it easy for you.

Support

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.