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Minimal task scheduling abstraction

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A minimal task scheduling abstraction.

See Dask documentation at


New BSD. See License File.


dask is not yet on any package index. It is still experimental.

python install


Consider the following simple program

def inc(i):
    return i + 1

def add(a, b):
    return a + b

x = 1
y = inc(x)
z = add(y, 10)

We encode this as a dictionary in the following way

d = {'x': 1,
     'y': (inc, 'x'),
     'z': (add, 'y', 10)}

While less aesthetically pleasing this dictionary may now be analyzed, optimized, and computed on by other Python code, not just the Python interpreter.

A simple dask dictionary


dask.core supports Python 2.6+ and Python 3.2+ with a common codebase. It is pure Python and requires no dependencies beyond the standard library.

It is, in short, a light weight dependency.

The threaded implementation depends on networkx. The Array dataset depends on numpy and the blaze family of projects.

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