Cloudera Machine Learning supports using simple plot to create data visualizations.
To create a simple plot, run a console in your favorite language and paste in the following code sample:
# A standard R plot plot(rnorm(1000)) # A ggplot2 plot library("ggplot2") qplot(hp, mpg, data=mtcars, color=am, facets=gear~cyl, size=I(3), xlab="Horsepower", ylab="Miles per Gallon")
import matplotlib.pyplot as plt import random plt.plot([random.normalvariate(0,1) for i in xrange(1,1000)])
Cloudera Machine Learning processes each line of code individually (unlike notebooks that process code per-cell). This means if your plot requires multiple commands, you will see incomplete plots in the workbench as each line is processed.
To get around this behavior, wrap all your plotting commands in one Python function. Cloudera Machine Learning will then process the function as a whole, and not as individual lines. You should then see your plots as expected.