A vision for extensibility to GPU & distributed support for SciPy, scikit-learn, scikit-image and beyond

Over the years, array computing in Python has evolved to support distributed arrays, GPU arrays, and other various kinds of arrays that work with specialized hardware, or carry additional metadata, or use different internal memory representations. The foundational library for array computing in the PyData ecosystem is NumPy. But NumPy alone is a CPU-only library - and a single-threaded one at that - and in a world where it's possible to get a GPU or a CPU with a large core count in the cloud cheaply or even for free in a matter of seconds, that may not seem enough. For the past couple of years, a lot of thought and effort has been spent on devising mechanisms to tackle this problem, and evolve the ecosystem in a gradual way towards a state where PyData libraries can run on a GPU, as well as in distributed mode across multiple GPUs.

We feel like a shared vision has emerged, in bits and pieces. In this post, we aim to articulate that vision and

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