Treffer: Scaling Python, and How!

Data scientists can efficiently parallelize Python programs on laptops, and then run the code tested locally on a cluster practically without any changes. This open source project has made great strides in the last year, is approaching 50% API coverage of NumPy, and can automatically scale out Python code for the supported APIs without the user needing to understand how to scale or even how it works. The Python community wanted a more Pythonic way to scale their code while also reducing the complexity of shifting code from a single machine to distributed environments. [Extracted from the article]

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