text: matplotlib‘s FAQ has a section dealing with the exact topic of this post: using matplotlib in a web application server. The problem is that I couldn’t find it easily from my Web searches. What I’m doing here is adding my own twist to the answer, and hopefully making it slightly more search-friendly.
  While testing a Web app that I was working on, I noticed that it would often hang. At first I dismissed it as a server problem, but it kept occuring on one particular page. A few hours and many head scratches later, I narrowed the problem down to matplotlib.
  # Negative example; do not use. import matplotlib.pyplot as plt def callback(): # ... (process data) fig = plt.figure() # ... (draw stuff) fig.savefile(path)
  I used matplotlib to draw a plot and save it to a file. The code was quite long, but it involved steps similar to the above listing. The first time it ran, everything went OK. The second time, it always hung at the
  pyplot.figure call. This smelled like a threading / deadlock problem, so I tried to put a lock on the pyplot calls (which I should have done anyway, considering pyplot operates on a single plot at a time). Still, it didn’t work.
  After some Web searches and more head scratching, I accidentally arrived at the FAQ entry mentioned earlier.
  Here’s the gist of the available solutions.
  First option: configure matplotlib to use the Anti-Grain Geometry backend. Continue using pyplot, carefully grouping its commands together and surrounding them with a lock.
  from threading import Lock lock = Lock() import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt def callback(): # ... (process data) with lock: fig = pyplot.figure() # ... (draw stuff) fig.savefile(path)
  Second, better option: dump pyplot and use matplotlib’s object-oriented API instead. For this one, you don’t need to care about threads or locking or whatever.
  from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure def callback(): # ... (process data) fig = Figure() canvas = FigureCanvas(fig) # ... (draw stuff) canvas.print_figure(path)
  Dude!
  You just saved my day.
  I second the above comment: you saved hours of my day…
  Great post! I ran into different problems with the pyplot interface. I missed the canvas element step, which prevented me from switching to the matplotlib API until now. Thanks! 🙂
  Instead of canvas.print_figure() I use fig.savefig() and canvas.close() to finish. Not sure if the canvas.close() is still required in order to keep the memory footprint of my scripts low during massive plotting sessions.
  Thanks a lot, helped me too!
  5 years after this post and it still can save hours of work. Big thx.