README.md

async-batcher

Small package that provides a way to add zero latency cost dynamic batching for ML model serving on web frameworks.

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Usage

Installing

Clone the project and just run:

make install

Create you own model

Simply create an instance of the Batcher class and start it. After that you can simply request a prediction with the predict function.

from async_batcher import Batcher

batcher = Batcher(
	batch_prediction_fn=lambda x : x, 
	event_loop=event_loop,
	max_batch_size=max_batch_size
)

batcher.start()

batcher.predict(x)

Alternatively, if the event loop is initialized later, you can pass it to the Batcher instance when you start it. For example in FastAPI you can do the following:

@app.on_event("startup")
async def startup_event():
	loop = asyncio.get_running_loop()
	asyncio.create_task(batcher.start(loop))

For a hackable example you can check the fastapi_example.py module from the example directory.