Batching
Group items to saturate your GPU or database
`.batch(n)` accumulates `n` items before emitting them as a list. `.temporal_batch(s)` groups everything arriving within `s` seconds — perfect for real-time streams (audio, sensors, WebSocket).
python
from olympipe import Pipeline
import time
# Fixed batch: group 64 items before processing
results = (
Pipeline(image_paths) # 10 000 images
.task(load_image, count=4) # parallel loading
.batch(64) # group into batches
.task(model.predict) # GPU inference
.wait_for_result()
)
# Temporal batch: 500ms sliding window
results = (
Pipeline(sensor_stream)
.task(parse_frame)
.temporal_batch(0.5) # everything arriving within 500ms
.task(aggregate)
.wait_for_result()
)⚡ Performance
10 000 images, GPU inference
No batching 50.0s
batch=16 12.0s
batch=64 3.2s
batch=128 2.1s
🚀 23.8× faster
How it works
Without batching, each item triggers a separate GPU round-trip. With a batch of 64, a single call processes 64 images — the fixed GPU latency is amortized over 64× more work.