uniserve 0.0.4

model serving frame work

UniServe

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UniServe is a model serving framework built on top of LitServe, designed to simplify the deployment and serving of machine learning models.

Features

• Easy deployment of machine learning models with minimal code
• Support for both simple APIs and file-based APIs
• Built-in support for batching and multi-device inference
• Extensible base classes for custom model APIs
• CLI tool for launching model servers
• Compatible with OpenAI API format for easy client integration

Installation

pip install uniserve

Or install from source:

pip install -e .

Quick Start

Run example

# Run with a simple API example
uniserve --api-list example.simple_api.api_list --port 8000

# Run with a simple API example in the no installed source code
uv run uniserve --api-list example.simple_api.api_list --port 8000

Create your own API

Create a custom API by extending the BaseLitAPI class:

from uniserve import BaseLitAPI

class MyModelAPI(BaseLitAPI):
    def load_model(self):
        # Load your model here
        model = MyModel()
        return model

    def decode_request(self, request):
        # Decode the incoming request
        return request.get("input")

    def predict(self, x):
        # Run model inference
        return self.model(x)

    def encode_response(self, output):
        # Encode the output into a response
        return {"result": output}

# Create an instance
my_api = MyModelAPI()
api_list = [my_api]

Then run your API:

uniserve --api-list my_module.api_list --port 8000

Debug Without Request

UniServe provides a convenient way to debug your API implementation without sending actual HTTP requests. This is especially useful during development to quickly test your model logic.

You can use the built-in debug method in your API class:

# Directly test your API methods without HTTP requests
api = MyModelAPI()
result = api.debug(request={"input": "test_data"}, device="cpu")
print(result)

See example/uni_api_debug.py for a complete example of how to debug your API implementation.

CLI Options

• --api-list: Fully qualified name of the API list object to serve (required)
• --port: Port to run the server on (default: 8000)
• --host: Host to run the server on (default: 127.0.0.1)
• --accelerator: Accelerator to use (e.g., 'cuda', 'cpu', 'auto') (default: auto)
• --devices: Devices to use (e.g., 'auto', '[0]', '[0,1]') (default: auto)
• --api-path: API endpoint path (default: None)

Base Classes

• BaseLitAPI: Base class for simple APIs, extends LitAPI with a setup method
• RequestLitAPI: Base class for handling file uploads with additional parameters
• MultiFileLitAPI: Alternative base class for handling multiple file uploads
• NNRequestLitAPI: Specialized base class for neural network inference with structured preprocessing and postprocessing steps

Examples

Check the example/ directory for more detailed examples of how to use UniServe with different types of models.

License

This project is licensed under the GPL-3.0 License - see the LICENSE file for details.