Jina-Serve

Jina-serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. Scale your services from local development to production while focusing on your core logic.

Key Features

• Native support for all major ML frameworks and data types
• High-performance service design with scaling, streaming, and dynamic batching
• LLM serving with streaming output
• Built-in Docker integration and Executor Hub
• One-click deployment to Jina AI Cloud
• Enterprise-ready with Kubernetes and Docker Compose support

Comparison with FastAPI

Key advantages over FastAPI:

• DocArray-based data handling with native gRPC support
• Built-in containerization and service orchestration
• Seamless scaling of microservices
• One-command cloud deployment

Install

pip install jina

See guides for Apple Silicon and Windows.

Core Concepts

Three main layers:

• Data: BaseDoc and DocList for input/output
• Serving: Executors process Documents, Gateway connects services
• Orchestration: Deployments serve Executors, Flows create pipelines

Build AI Services

Let’s create a gRPC-based AI service using StableLM:

from jina import Executor, requests
from docarray import DocList, BaseDoc
from transformers import pipeline


class Prompt(BaseDoc):
    text: str


class Generation(BaseDoc):
    prompt: str
    text: str


class StableLM(Executor):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.generator = pipeline(
            'text-generation', model='stabilityai/stablelm-base-alpha-3b'
        )

    @requests
    def generate(self, docs: DocList[Prompt], **kwargs) -> DocList[Generation]:
        generations = DocList[Generation]()
        prompts = docs.text
        llm_outputs = self.generator(prompts)
        for prompt, output in zip(prompts, llm_outputs):
            generations.append(Generation(prompt=prompt, text=output))
        return generations

Deploy with Python or YAML:

from jina import Deployment
from executor import StableLM

dep = Deployment(uses=StableLM, timeout_ready=-1, port=12345)

with dep:
    dep.block()

jtype: Deployment
with:
 uses: StableLM
 py_modules:
   - executor.py
 timeout_ready: -1
 port: 12345

Use the client:

from jina import Client
from docarray import DocList
from executor import Prompt, Generation

prompt = Prompt(text='suggest an interesting image generation prompt')
client = Client(port=12345)
response = client.post('/', inputs=[prompt], return_type=DocList[Generation])

Build Pipelines

Chain services into a Flow:

from jina import Flow

flow = Flow(port=12345).add(uses=StableLM).add(uses=TextToImage)

with flow:
    flow.block()

Scaling and Deployment

Local Scaling

Boost throughput with built-in features:

• Replicas for parallel processing
• Shards for data partitioning
• Dynamic batching for efficient model inference

Example scaling a Stable Diffusion deployment:

jtype: Deployment
with:
 uses: TextToImage
 timeout_ready: -1
 py_modules:
   - text_to_image.py
 env:
  CUDA_VISIBLE_DEVICES: RR
 replicas: 2
 uses_dynamic_batching:
   /default:
     preferred_batch_size: 10
     timeout: 200

Cloud Deployment

Containerize Services

1. Structure your Executor:

   TextToImage/
   ├── executor.py
   ├── config.yml
   ├── requirements.txt

2. Configure: ```yaml

   config.yml

   jtype: TextToImage py_modules:

• executor.py metas: name: TextToImage description: Text to Image generation Executor ```

3. Push to Hub:

   jina hub push TextToImage

Deploy to Kubernetes

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

Use Docker Compose

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

JCloud Deployment

Deploy with a single command:

jina cloud deploy jcloud-flow.yml

LLM Streaming

Enable token-by-token streaming for responsive LLM applications:

1. Define schemas: ```python from docarray import BaseDoc

class PromptDocument(BaseDoc): prompt: str max_tokens: int

class ModelOutputDocument(BaseDoc): token_id: int generated_text: str

2. Initialize service:
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel


class TokenStreamingExecutor(Executor):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.model = GPT2LMHeadModel.from_pretrained('gpt2')

3. Implement streaming:

   @requests(on='/stream')
   async def task(self, doc: PromptDocument, **kwargs) -> ModelOutputDocument:
    input = tokenizer(doc.prompt, return_tensors='pt')
    input_len = input['input_ids'].shape[1]
    for _ in range(doc.max_tokens):
        output = self.model.generate(**input, max_new_tokens=1)
        if output[0][-1] == tokenizer.eos_token_id:
            break
        yield ModelOutputDocument(
            token_id=output[0][-1],
            generated_text=tokenizer.decode(
                output[0][input_len:], skip_special_tokens=True
            ),
        )
        input = {
            'input_ids': output,
            'attention_mask': torch.ones(1, len(output[0])),
        }

4. Serve and use: ```python

   Server

   with Deployment(uses=TokenStreamingExecutor, port=12345, protocol=’grpc’) as dep: dep.block()

Client

async def main(): client = Client(port=12345, protocol=’grpc’, asyncio=True) async for doc in client.stream_doc( on=’/stream’, inputs=PromptDocument(prompt=’what is the capital of France ?’, max_tokens=10), return_type=ModelOutputDocument, ): print(doc.generated_text)

## Support

Jina-serve is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE).