Visualize task graphs
Visualize task graphs¶
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¶
.visualize method and
dask.visualize function work exactly like
.compute method and
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
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')
Note that the
visualize function is powered by the GraphViz
system library. This library has a few considerations:
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
graphvizsystem library as a dependency.
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
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')
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
visualize, then the tooltips will 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
You can click on any of the layer names to expand or collapse more detailed information about each layer.