Scaling AI-Based Data Processing with Hugging Face + Dask

Processing 100 Rows with Pandas Scaling to 211 Million Rows with Dask Multi-GPU Parallel Model Inference Conclusion The Hugging Face platform has many datasets and pre-trained models that make using and training state-of-the-art machine learning models increasingly accessible. However, it can be hard to scale AI tasks because AI datasets are often large (100s GBs to TBs) and using Hugging Face transformers for model inference can sometimes be computationally expensive.

Dask, a Python library for distributed computing, can handle out-of-core computing (processing data that doesn’t fit in memory) by breaking datasets into manageable chunks. This makes it easy to do things like:

In this post we show an example of data processing from the FineWeb dataset, using the FineWeb-Edu classifier to identify web pages with high educational value. We’ll show:

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