Using Hugging Face models and datasets is powerful for machine learning, but scaling tasks like model inference on large datasets can be challenging.
Dask handles out-of-core computing, breaking up datasets into manageable chunks so that even large-scale tasks can run smoothly.
In this example, we processed the FineWeb dataset (~715 GB in memory) using the 🤗 HF FineWeb-Edu classifier. Locally, processing 100 rows with pandas took ~10 seconds, but scaling up to 211M rows was possible with Dask on multi-GPU clusters deployed with Coiled.
Results:
- Handled large-scale text classification, filtering, and saved results to Hugging Face storage
- Optimized GPU utilization to efficiently use expensive hardware
This example could be adapted for other workflows like:
- Genomic data filtering
- Large-scale content extraction
- Multimodal AI (audio, image, text)
Had a lot of fun learning about Hugging Face and putting together this example with Quentin Lhoest, James Bourbeau, and Daniel van Strien
Blog post: https://lnkd.in/gcV348fA