Visualize task graphs

visualize(*args[, filename, traverse, ...])

Visualize several dask graphs simultaneously.

Before executing your computation you might consider visualizing the underlying task graph. By looking at the inter-connectedness of tasks you can learn more about potential bottlenecks where parallelism may not be possible, or areas where many tasks depend on each other, which may cause a great deal of communication.

Visualize the low level graph

The .visualize method and dask.visualize function work exactly like the .compute method and dask.compute function, except that rather than computing the result, they produce an image of the task graph.

By default the task graph is rendered from top to bottom. In the case that you prefer to visualize it from left to right, pass rankdir="LR" as a keyword argument to .visualize.

import dask.array as da
x = da.ones((15, 15), chunks=(5, 5))

y = x + x.T

# y.compute()

# visualize the low level Dask graph
y.visualize(filename='transpose.svg')
Dask low level task graph for adding an array to its transpose

Note that the visualize function is powered by the GraphViz system library. This library has a few considerations:

  1. You must install both the graphviz system library (with tools like apt-get, yum, or brew) and the graphviz Python library. If you use Conda then you need to install python-graphviz, which will bring along the graphviz system library as a dependency.

  2. Graphviz takes a while on graphs larger than about 100 nodes. For large computations you might have to simplify your computation a bit for the visualize method to work well.

Visualize the high level graph

The low level Dask task graph can be overwhelimg, especially for large computations. A more concise alternative is to look at the Dask high level graph instead. The high level graph can be visualized using .dask.visualize().

import dask.array as da
x = da.ones((15, 15), chunks=(5, 5))
y = x + x.T

# visualize the high level Dask graph
y.dask.visualize(filename='transpose-hlg.svg')
Dask high level task graph for adding an array to its transpose

Hovering your mouse above each high level graph label will bring up a tooltip with more detailed information about that layer. Note that if you save the graph to disk using the filename= keyword argument in visualize, then the tooltips wil only be preserved by the SVG image format.

High level graph HTML representation

Dask high level graphs also have their own HTML representation, which is useful if you like to work with Jupyter notebooks.

import dask.array as da
x = da.ones((15, 15), chunks=(5, 5))
y = x + x.T

y.dask  # shows the HTML representation in a Jupyter notebook
Dask high level graph HTML representation

You can click on any of the layer names to expand or collapse more detailed information about each layer.