runware-serverless

The surface you write a Runware Serverless app against: two decorators, and nothing else to install.

pip install runware-serverless

from runware_serverless import endpoint, serve


@serve
class ImageTools:
    def load(self) -> None:
        self.pipe = load_my_pipeline()

    @endpoint
    def generate(self, prompt: str, steps: int = 4) -> dict:
        return {"image": self.pipe(prompt, steps).encode()}

runware serverless deploy model.py --id image-tools --gpu-type h100

See the Serverless documentation for the whole picture. This package is only the part your editor needs.

examples/ has two apps you can deploy as they are: the smallest one that works, and one with two endpoints whose request shapes have nothing in common.

What the decorators do

@serve marks the class as the app. @endpoint marks a method as callable, at the path its own name derives: run_upscale serves run-upscale.

Neither one wraps what it decorates. Both record and hand the class or the method straight back, so your file stays importable and your own tests can instantiate the class and call its handlers directly.

load runs once per worker before any request reaches it. It is required, even when there is nothing to load, because it is part of how a deployment class is identified.

What it does not do

This package holds no serving machinery, no build logic and no runtime. The base install declares no dependencies at all, so it costs you nothing and it cannot conflict with your model's own requirements.

When you deploy, Runware supplies the implementation behind these same two names. That is why the decorators record rather than execute: the marks they leave are what the platform reads when it imports your file.

You do not need to list this package in your own requirements. A deploy ignores it if you do, and serves its own copy either way.

Images in a request

An image travels as a base64 string. Annotate it on a field of a request model, and take that model as your handler's parameter:

import pydantic
from runware_serverless import endpoint, serve
from runware_serverless.schema import Base64RGBImage


class UpscaleTask(pydantic.BaseModel):
    image: Base64RGBImage
    factor: int = 2


@serve
class Upscaler:
    def load(self) -> None: ...

    @endpoint
    def upscale(self, task: UpscaleTask) -> dict:
        return {"width": task.image.width * task.factor}

The platform publishes a string of format: base64 for that field, decodes what a caller sends, and task.image is a PIL.Image by the time your method runs. A malformed payload is refused before that, as a validation error naming the field.

It has to be a field on a model. A bare parameter, def upscale(self, image: Base64RGBImage), is left exactly as it arrived: the platform binds a parameter by validating the model it names, so a parameter that is not a pydantic.BaseModel is passed through untouched and your method receives the raw string.

Base64RGBAImage is the same thing for an image whose alpha channel has to survive. Base64RGBImage converts to RGB.

These are the one part of the package with dependencies, Pillow and pydantic, so they come as an extra rather than in the base install:

pip install 'runware-serverless[schema]'

Configuration lives on the app

GPU type, worker counts, idle TTL and concurrency are set on the app, through the CLI, the API or the dashboard, and are changeable while it is serving. None of them are expressible in your source file, and the decorators take no arguments for them.

See Compute and scaling.

Requirements

Python 3.10 or newer, and nothing else unless you annotate an image, which needs the schema extra above.

License

MIT