modelrunner-ai 0.0.13

Python client for modelrunner.ai

modelrunner.ai Python client

This is a Python client library for interacting with ML models deployed on modelrunner.ai.

Getting started

To install the client, run:

pip install modelrunner-ai

To use the client, you need to have an API key. You can get one by signing up at modelrunner.ai. Once you have it, set it as an environment variable:

export MODELRUNNER_KEY=your-api-key

Now you can use the client to interact with your models. Here's an example of how to use it:

import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.run_async("bytedance/sdxl-lightning-4step", arguments={"prompt": "two friends cooking together"})
    print(response["output"])


asyncio.run(main())

Uploading files

If the model requires files as input, you can upload them directly to media.modelrunner.ai (our CDN) and pass the URLs to the client. Here's an example:

import modelrunner_ai

input_image = modelrunner_ai.upload_file("./image.jpg")
print(input_image)
response = modelrunner_ai.run("swook/inspyrenet", arguments={"image_path": input_image})
print(response["output"])

Queuing requests

When you want to send a request and keep receiving updates on its status, you can use the submit method. Here's an example:

import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async("bytedance/sdxl-lightning-4step", arguments={"prompt": "two friends cooking together"})

    logs_index = 0
    async for event in response.iter_events(with_logs=True):
        if isinstance(event, modelrunner_ai.Queued):
            print("Queued. Position:", event.position)
        elif isinstance(event, (modelrunner_ai.InProgress, modelrunner_ai.Completed)):
            new_logs = event.logs[logs_index:]
            for log in new_logs:
                print(log["message"])
            logs_index = len(event.logs)

    result = await response.get()
    print(result["output"])


asyncio.run(main())