Visualize task graphs

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

Visualize several low level dask graphs at once.

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.

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()
Dask 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.