Minimal task scheduling abstraction
Project description
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 http://dask.pydata.org or read developer-focused blogposts about dask’s development.
Install
Dask is easily installable through your favorite Python package manager:
conda install dask or pip install dask[array] or pip install dask[bag] or 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.
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.
Dependencies
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.
LICENSE
New BSD. See License File.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
