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Pyflyby: Improving Efficiency of Jupyter Interactive Sessions

Few things hinder productivity more than interruption. A notification, random realization, or unrelated error can derail one's train of thought when deep in a complex analysis – a frustrating experience.

In the software development context, forgetting to import a statement in an interactive Jupyter session is such an experience. This can be especially frustrating when using typical abbreviations, like np, pd, plt, where the meaning is obvious to the human reader, but not to the computer. The time-to-first-plot, and ability to quickly cleanup one's notebook afterward are critical to an enjoyable and efficient workflow.

In this blogpost we present pyflyby, a project and an extension to IPython and JupyterLab, that, among many things, automatically inserts imports and tidies Python files and notebooks.

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Distributed Training Made Easy with PyTorch-Ignite

PyTorch-Ignite logo

Authors: François Cokelaer, Priyansi, Sylvain Desroziers, Victor Fomin

Writing agnostic distributed code that supports different platforms, hardware configurations (GPUs, TPUs) and communication frameworks is tedious. In this blog, we will discuss how PyTorch-Ignite solves this problem with minimal code change.

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