Matplotlib memory leak in Ubuntu, but not in Windows 10 - matplotlib

Question

I have a code that reads images from folders and annotates the images. I have about 10K images of size 3K x 3K. I apply cell detection, and I put an annotation on the images. For simplicity, I in the snapshot code provided below, the code generates a random image and random annotations with X and Y location.
The code works fine, in windows 10, with python 3.6 for both multiprocessing and without multiprocessing. However, when I submit to the HPC server with ubuntu 18.04 and Python 3.6, I am getting memory error with Matplotlib. I have tried the following, but no luck in resolving the problem.
* plt.close(fig)
* plt.figure().clear()
* plt.clf()
* plt.close()
'''
import os
import matplotlib.pyplot as plot
import pandas as pd
import multiprocessing as mp
import numpy as np
import seaborn as sns
sns.set_palette('bright')
def mark_points(im, df, file_name):
# this code put scatter plot on the image and saves the the figure
dpi = 100
height, width, nbands = im.shape
# What size does the figure need to be in inches to fit the image?
fig_size = width / float(dpi), height / float(dpi)
# Create a figure of the right size with one axes that takes up the full figure
fig = plt.figure(figsize=fig_size)
ax = fig.add_axes([0, 0, 1, 1])
# Hide spines, ticks, etc.
ax.axis('off')
# Display the image.
ax.imshow(im, interpolation='nearest')
sns.scatterplot(x='X',
y='Y',
data=df,
hue='Class',
ax=ax,
s=2,
edgecolor=None,
legend=False)
ax.set(xlim=[0, width], ylim=[height, 0], aspect=1)
# save image with cell annotation
fig.savefig(file_name, dpi=dpi, transparent=True)
# plt.close(fig)
plt.figure().clear()
plt.clf()
plt.close()
def annotate_images(output_dir,
process_num):
# create an image and random X and Y location for annotation
os.makedirs(output_dir, exist_ok=True)
for j in range(10000):
# this approximately the size of image I am processing
im_height, im_width = 3000, 3000
# print('{}'.format(process_num))
file_name = 'my_file_name_{}.jpg'.format(process_num)
output_file = os.path.join(output_dir, file_name)
# generate dataframe with data to plot
df = pd.DataFrame(columns=['X', 'Y', 'Class'])
num_points = 20000
df['X'] = np.random.randint(0, im_width, size=(num_points), dtype='int32').tolist()
df['Y'] = np.random.randint(0, im_height, size=(num_points), dtype='int32').tolist()
classes_ = ['A', 'B', 'C']
df['Class'] = [classes_[np.random.randint(0, 3)] for _ in range(num_points)]
# generate a random image
im = np.random.randint(low=0, high=255, size=(im_height, im_width, 3), dtype=np.uint8)
mark_points(im=im, df=df, file_name=output_file)
def run(output_dir,
num_processes=1,
multi_process=False
):
# number of folders
n = 100
for i in range(n):
if multi_process is True:
processes = [
mp.Process(target=annotate_images, args=(output_dir, process_num)) for process_num in range(num_processes)]
print('{} processes created'.format(num_processes))
# Run processes
for p in processes:
p.start()
# Exit the completed processes
for p in processes:
p.join()
print('All Processes finished!!!')
else:
annotate_images(output_dir=output_dir,
process_num=0
)
if __name__ == '__main__':
num_processes = 4
multi_process = True
home_dir = r'Output'
for i in range(3):
params = dict(output_dir=home_dir,
num_processes=num_processes,
multi_process=multi_process)
run(**params)
'''
Error message
image_annotate_cells.py:44: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
plt.figure().clear()
image_annotate_cells.py:22: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
fig = plt.figure(figsize=fig_size)
Process Process-1:
Traceback (most recent call last):
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "image_annotate_cells.py", line 74, in annotate_images
mark_points(im=im, df=df, file_name=output_file)
File "image_annotate_cells.py", line 42, in mark_points
fig.savefig(file_name, dpi=dpi, transparent=True)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/figure.py", line 2203, in savefig
self.canvas.print_figure(fname, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/backend_bases.py", line 2126, in print_figure
**kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py", line 358, in wrapper
return func(*args, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py", line 584, in print_jpg
FigureCanvasAgg.draw(self)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py", line 393, in draw
self.figure.draw(self.renderer)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/figure.py", line 1736, in draw
renderer, self, artists, self.suppressComposite)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/image.py", line 137, in _draw_list_compositing_images
a.draw(renderer)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/axes/_base.py", line 2630, in draw
mimage._draw_list_compositing_images(renderer, self, artists)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/image.py", line 137, in _draw_list_compositing_images
a.draw(renderer)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/collections.py", line 894, in draw
Collection.draw(self, renderer)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/collections.py", line 369, in draw
self._offset_position)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/site-packages/matplotlib/path.py", line 197, in vertices
#property
KeyboardInterrupt
Traceback (most recent call last):
File "image_annotate_cells.py", line 124, in <module>
run(**params)
File "image_annotate_cells.py", line 97, in run
p.join()
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/multiprocessing/process.py", line 124, in join
res = self._popen.wait(timeout)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/multiprocessing/popen_fork.py", line 50, in wait
return self.poll(os.WNOHANG if timeout == 0.0 else 0)
File "/home/DMP/DUDMP/COPAINGE/yhagos/.conda/envs/tf2cpu/lib/python3.6/multiprocessing/popen_fork.py", line 28, in poll
pid, sts = os.waitpid(self.pid, flag)
KeyboardInterrupt