Daft UDF Tuning

Optimize User-Defined Functions for performance.

UDF Types

Type Decorator Use Case

Stateless @daft.func

Simple transforms. Use async for I/O-bound tasks.

Stateful @daft.cls

Expensive init (e.g., loading models). Supports gpus=N .

Batch @daft.func.batch

Vectorized CPU/GPU ops (NumPy/PyTorch). Faster.

Quick Recipes

1. Async I/O (Web APIs)

@daft.func async def fetch(url: str): async with aiohttp.ClientSession() as s: return await s.get(url).text()

2. GPU Batch Inference (PyTorch/Models)

@daft.cls(gpus=1) class Classifier: def init(self): self.model = load_model().cuda() # Run once per worker

@daft.method.batch(batch_size=32)
def predict(self, images):
    return self.model(images.to_pylist())

Run with concurrency

df.with_column("preds", Classifier(max_concurrency=4).predict(df["img"]))

Tuning Keys

• max_concurrency : Total parallel UDF instances.

• gpus=N : GPU request per instance.

• batch_size : Rows per call. Too small = overhead; too big = OOM.

• into_batches(N) : Pre-slice partitions if memory is tight.