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Daft daft-udf-tuning

Optimize Daft UDF performance. Invoke when user needs GPU inference, encounters slow UDFs, or asks about async/batch processing.

install
source · Clone the upstream repo
git clone https://github.com/Eventual-Inc/Daft
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Eventual-Inc/Daft "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/daft-udf-tuning" ~/.claude/skills/eventual-inc-daft-daft-udf-tuning && rm -rf "$T"
manifest: .claude/skills/daft-udf-tuning/SKILL.md
source content

Daft UDF Tuning

Optimize User-Defined Functions for performance.

UDF Types

TypeDecoratorUse 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.