Inspecting Dask objects

Dask itself is just a specification on top of normal Python dictionaries. Objects like dask.Array are just a thin wrapper around these dictionaries with a little bit of shape metadata.

Users should only have to interact with the higher-level Array objects. Developers may want to dive more deeply into the dictionaries/task graphs themselves

dask attribute

The first step is to look at the .dask attribute of an array

>>> import dask.array as da
>>> x = da.ones((5, 15), chunks=(5, 5))
>>> x.dask
{('wrapped_1', 0, 0): (ones, (5, 5)),
 ('wrapped_1', 0, 1): (ones, (5, 5)),
 ('wrapped_1', 0, 2): (ones, (5, 5))}

This attribute becomes more interesting as you perform operations on your Array objects

>>> (x + 1).dask
{('wrapped_1', 0, 0): (ones, (5, 5)),
 ('wrapped_1', 0, 1): (ones, (5, 5)),
 ('wrapped_1', 0, 2): (ones, (5, 5))
 ('x_1', 0, 0): (add, ('wrapped_1', 0, 0), 1),
 ('x_1', 0, 1): (add, ('wrapped_1', 0, 1), 1),
 ('x_1', 0, 2): (add, ('wrapped_1', 0, 2), 1)}

Visualize graphs with DOT

basic ones + 1 graph

If you have basic graphviz tools like dot installed then dask can also generate visual graphs from your task graphs.

>>> d = (x + 1).dask
>>> from dask.dot import dot_graph
>>> dot_graph(d)
Writing graph to mydask.pdf

The result is shown to the right.