Overview
ModelRunner provides a unified Python SDK to call any supported model with a consistent interface. Use it in scripts, services, and notebooks with both async and sync workflows.
For scripts and notebooks, never hardcode secrets. Use environment variables to manage your key securely.
Installation
pip install modelrunner-ai
export MODELRUNNER_KEY=your-api-key
Get your credentials from your ModelRunner account. Keep them server-only and out of version control.
Call a model
Leverage the queue for long-running tasks. Optionally listen to queue updates.
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())
Upload files
Upload local files to ModelRunner storage and receive a temporary URL you can pass to model inputs (for example, image or audio URLs).
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"])
