Visualizing a task graph can be very useful for understanding the structure of a computation graph and identifying potential bottlenecks or optimization opportunities. Dask provides several tools for visualizing task graphs, including the dask.visualize function and the dask.diagnostics module.

Here's an example of how you can visualize a task graph using dask.visualize:

import dask
import dask.array as da
import matplotlib.pyplot as plt
 
# Create a large random array
x = da.random.random((10000, 10000), chunks=(1000, 1000))
 
# Compute the mean of the array
y = x.mean()
 
# Visualize the task graph
dask.visualize(y, filename='task-graph.pdf')
 
# Show the plot
plt.show()

In this example, we first create a large random array using dask.array. We then compute the mean of the array using the mean method, which creates a computation graph.

To visualize the task graph, we use the dask.visualize function, which creates a plot of the computation graph. The plot can be saved to a file by specifying a filename as an argument to the function.

Finally, we use matplotlib to display the plot. The resulting plot shows the structure of the computation graph, with nodes representing individual computations and edges representing dependencies between computations.

By visualizing the task graph, we can gain insight into the structure of the computation and identify potential bottlenecks or optimization opportunities. For example, we may be able to identify computations that can be parallelized or optimized, or we may be able to identify dependencies that can be eliminated or reordered to improve performance.