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PyTorch has become the de facto standard for development and research in deep learning. Among the many factors contributing to its popularity is the wide array of customization hooks it provides. These extension mechanisms allow developers to build new functionality on top of PyTorch while maintaining compatibility with its core backend features—a powerful capability for engineers, researchers, and curious hackers, both in-core and downstream. In this talk, we’ll explore various ways to extend PyTorch and present concrete examples of these techniques in action.

PyTorch customization hooks extension mechanisms

Explore Buckaroo, an open-source dataframe UI for pandas and polars that lets you scroll, search, and summarize dataframes directly in your notebook — no boilerplate code needed. You’ll learn how to use Buckaroo in daily workflows, from essential UI features to advanced capabilities like the low-code interface, dataframe diffing, automatic cleaning, and Pandera integration. This talk is aimed at data scientists with a basic understanding of pandas and Jupyter notebooks.

buckaroo Pandas Polars pandera low-code interface dataframe diffing automatic cleaning
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