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Minimal task scheduling abstraction

Project description

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Dask provides multi-core execution on larger-than-memory datasets using blocked algorithms and task scheduling. It maps high-level NumPy and list operations on large datasets on to graphs of many operations on small in-memory datasets. It then executes these graphs in parallel on a single machine. Dask lets us use traditional NumPy and list programming while operating on inconveniently large data in a small amount of space.

  • dask is a specification to describe task dependency graphs.
  • dask.array is a drop-in NumPy replacement (for a subset of NumPy) that encodes blocked algorithms in dask dependency graphs.
  • dask.bag encodes blocked algorithms on Python lists of arbitrary Python objects.
  • dask.async is a shared-memory asynchronous scheduler efficiently execute dask dependency graphs on multiple cores.

Dask does not currently have a distributed memory scheduler.

See full documentation at or read developer-focused blogposts about dask’s development.


Dask is easily installable through your favorite Python package manager:

conda install dask


pip install dask[array]
pip install dask[bag]
pip install dask[complete]

Dask Graphs

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 Arrays

The dask.array module creates these graphs from NumPy-like operations

>>> import dask.array as da
>>> x = da.random.random((4, 4), blockshape=(2, 2))
>>> x.T[0, 3].dask
{('x', 0, 0): (np.random.random, (2, 2)),
 ('x', 0, 1): (np.random.random, (2, 2)),
 ('x', 1, 0): (np.random.random, (2, 2)),
 ('x', 1, 1): (np.random.random, (2, 2)),
 ('y', 0, 0): (np.transpose, ('x', 0, 0)),
 ('y', 0, 1): (np.transpose, ('x', 1, 0)),
 ('y', 1, 0): (np.transpose, ('x', 0, 1)),
 ('y', 1, 1): (np.transpose, ('x', 1, 1)),
 ('z',): (getitem, ('y', 0, 1), (0, 1))}

Finally, a scheduler executes these graphs to achieve the intended result. The dask.async module contains a shared memory scheduler that efficiently leverages multiple cores.


dask.core supports Python 2.6+ and Python 3.3+ with a common codebase. It is pure Python and requires no dependencies beyond the standard library. It is a light weight dependency.

dask.array depends on numpy.

dask.bag depends on toolz and dill.


New BSD. See License File.

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