[Bug]: Memory leak in 3.4.3

Bug summary

As reported in jupyterlab/jupyterlab#11182, running below code with a 30M row data frame (pandas) only increases memory.

Code for reproduction

%matplotlib widget  # with this commented out or not, same result
import modin.pandas as pd
import matplotlib.pyplot as plt

# get your df

plt.rcParams["figure.figsize"] = [40, 15]
plt.rcParams["figure.autolayout"] = True

# plt.cla()  # with this commented out or not, same result
# plt.clf()  # with this commented out or not, same result
# plt.close(fig)  # with this commented out or not, same result

# this works but then of course the figure is empty
# import gc
# fig.clear()  # also this must be commented out on the second run since on the first one it's not defined
# gc.collect()

fig, axs = plt.subplots(1, 1, sharex=True)
# fig.clear()  # draws only one plot, still leaks
axs.plot(df[df.columns[0]], color="black")

ax1 = axs.twinx()
ax1.plot(df[df.columns[1]], color="blue")

# with this 2 lines commented out or not, same result
fig.tight_layout()
plt.show()

Actual outcome

Memory only increases.

Expected outcome

No memory leak prefferably.

Operating system

Ubuntu 21.04

Matplotlib Version

3.4.3

Matplotlib Backend

Qt5Agg, same with %matplotlib and %matplotlib widget

Python version

3.7.10

Jupyter version

3.1.11

Other libraries

Conda's env.yaml:

name: test
channels:
  - pyviz
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=1_gnu
  - abseil-cpp=20210324.2=h9c3ff4c_0
  - alembic=1.7.3=pyhd8ed1ab_0
  - alsa-lib=1.2.3=h516909a_0
  - anyio=3.3.0=py37h89c1867_0
  - argcomplete=1.12.3=pyhd8ed1ab_2
  - argon2-cffi=20.1.0=py37h5e8e339_2
  - arrow-cpp=5.0.0=py37hdf48254_5_cpu
  - async_generator=1.10=py_0
  - attrs=21.2.0=pyhd8ed1ab_0
  - autopage=0.4.0=pyhd8ed1ab_0
  - aws-c-cal=0.5.11=h95a6274_0
  - aws-c-common=0.6.2=h7f98852_0
  - aws-c-event-stream=0.2.7=h3541f99_13
  - aws-c-io=0.10.5=hfb6a706_0
  - aws-checksums=0.1.11=ha31a3da_7
  - aws-sdk-cpp=1.8.186=hb4091e7_3
  - babel=2.9.1=pyh44b312d_0
  - backcall=0.2.0=pyh9f0ad1d_0
  - backports=1.0=py_2
  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
  - backports.zoneinfo=0.2.1=py37h5e8e339_4
  - bleach=4.1.0=pyhd8ed1ab_0
  - bokeh=2.3.3=py37h89c1867_0
  - brotlipy=0.7.0=py37h5e8e339_1001
  - bzip2=1.0.8=h7f98852_4
  - c-ares=1.17.2=h7f98852_0
  - ca-certificates=2021.5.30=ha878542_0
  - certifi=2021.5.30=py37h89c1867_0
  - cffi=1.14.6=py37hc58025e_0
  - chardet=4.0.0=py37h89c1867_1
  - charset-normalizer=2.0.0=pyhd8ed1ab_0
  - click=8.0.1=py37h89c1867_0
  - clickhouse-cityhash=1.0.2.3=py37h3340039_2
  - clickhouse-driver=0.2.1=py37h5e8e339_0
  - cliff=3.9.0=pyhd8ed1ab_0
  - cloudpickle=2.0.0=pyhd8ed1ab_0
  - cmaes=0.8.2=pyh44b312d_0
  - cmd2=2.2.0=py37h89c1867_0
  - colorama=0.4.4=pyh9f0ad1d_0
  - colorcet=2.0.6=pyhd8ed1ab_0
  - colorlog=6.4.1=py37h89c1867_0
  - conda=4.10.3=py37h89c1867_1
  - conda-package-handling=1.7.3=py37h5e8e339_0
  - cramjam=2.3.1=py37h5e8e339_1
  - cryptography=3.4.7=py37h5d9358c_0
  - cycler=0.10.0=py_2
  - cytoolz=0.11.0=py37h5e8e339_3
  - dask=2021.9.0=pyhd8ed1ab_0
  - dask-core=2021.9.0=pyhd8ed1ab_0
  - datashader=0.13.0=pyh6c4a22f_0
  - datashape=0.5.4=py_1
  - dbus=1.13.6=h48d8840_2
  - debugpy=1.4.1=py37hcd2ae1e_0
  - decorator=5.1.0=pyhd8ed1ab_0
  - defusedxml=0.7.1=pyhd8ed1ab_0
  - distributed=2021.9.0=py37h89c1867_0
  - entrypoints=0.3=py37hc8dfbb8_1002
  - expat=2.4.1=h9c3ff4c_0
  - fastparquet=0.7.1=py37hb1e94ed_0
  - filelock=3.0.12=pyh9f0ad1d_0
  - fontconfig=2.13.1=hba837de_1005
  - freetype=2.10.4=h0708190_1
  - fsspec=2021.8.1=pyhd8ed1ab_0
  - gettext=0.19.8.1=h0b5b191_1005
  - gflags=2.2.2=he1b5a44_1004
  - gitdb=4.0.7=pyhd8ed1ab_0
  - gitpython=3.1.23=pyhd8ed1ab_1
  - glib=2.68.4=h9c3ff4c_0
  - glib-tools=2.68.4=h9c3ff4c_0
  - glog=0.5.0=h48cff8f_0
  - greenlet=1.1.1=py37hcd2ae1e_0
  - grpc-cpp=1.40.0=h850795e_0
  - gst-plugins-base=1.18.5=hf529b03_0
  - gstreamer=1.18.5=h76c114f_0
  - heapdict=1.0.1=py_0
  - holoviews=1.14.5=py_0
  - hvplot=0.7.3=py_0
  - icu=68.1=h58526e2_0
  - idna=3.1=pyhd3deb0d_0
  - importlib-metadata=4.8.1=py37h89c1867_0
  - importlib_metadata=4.8.1=hd8ed1ab_0
  - importlib_resources=5.2.2=pyhd8ed1ab_0
  - ipykernel=6.4.1=py37h6531663_0
  - ipympl=0.7.0=pyhd8ed1ab_0
  - ipython=7.27.0=py37h6531663_0
  - ipython_genutils=0.2.0=py_1
  - ipywidgets=7.6.5=pyhd8ed1ab_0
  - jbig=2.1=h7f98852_2003
  - jedi=0.18.0=py37h89c1867_2
  - jinja2=3.0.1=pyhd8ed1ab_0
  - joblib=1.0.1=pyhd8ed1ab_0
  - jpeg=9d=h36c2ea0_0
  - json5=0.9.5=pyh9f0ad1d_0
  - jsonschema=3.2.0=py37hc8dfbb8_1
  - jupyter-server-mathjax=0.2.3=pyhd8ed1ab_0
  - jupyter_client=7.0.2=pyhd8ed1ab_0
  - jupyter_contrib_core=0.3.3=py_2
  - jupyter_contrib_nbextensions=0.5.1=py37hc8dfbb8_1
  - jupyter_core=4.7.1=py37h89c1867_0
  - jupyter_highlight_selected_word=0.2.0=py37h89c1867_1002
  - jupyter_latex_envs=1.4.6=py37h89c1867_1001
  - jupyter_nbextensions_configurator=0.4.1=py37h89c1867_2
  - jupyter_server=1.11.0=pyhd8ed1ab_0
  - jupyterlab=3.1.11=pyhd8ed1ab_0
  - jupyterlab-git=0.32.2=pyhd8ed1ab_0
  - jupyterlab_pygments=0.1.2=pyh9f0ad1d_0
  - jupyterlab_server=2.8.1=pyhd8ed1ab_0
  - jupyterlab_widgets=1.0.2=pyhd8ed1ab_0
  - kiwisolver=1.3.2=py37h2527ec5_0
  - krb5=1.19.2=hcc1bbae_0
  - lcms2=2.12=hddcbb42_0
  - ld_impl_linux-64=2.36.1=hea4e1c9_2
  - lerc=2.2.1=h9c3ff4c_0
  - libarchive=3.5.2=hccf745f_0
  - libblas=3.9.0=11_linux64_openblas
  - libbrotlicommon=1.0.9=h7f98852_5
  - libbrotlidec=1.0.9=h7f98852_5
  - libbrotlienc=1.0.9=h7f98852_5
  - libcblas=3.9.0=11_linux64_openblas
  - libclang=11.1.0=default_ha53f305_1
  - libcurl=7.78.0=h2574ce0_0
  - libdeflate=1.7=h7f98852_5
  - libedit=3.1.20191231=he28a2e2_2
  - libev=4.33=h516909a_1
  - libevent=2.1.10=hcdb4288_3
  - libffi=3.3=h58526e2_2
  - libgcc-ng=11.1.0=hc902ee8_8
  - libgfortran-ng=11.1.0=h69a702a_8
  - libgfortran5=11.1.0=h6c583b3_8
  - libglib=2.68.4=h3e27bee_0
  - libgomp=11.1.0=hc902ee8_8
  - libiconv=1.16=h516909a_0
  - liblapack=3.9.0=11_linux64_openblas
  - libllvm11=11.1.0=hf817b99_2
  - libnghttp2=1.43.0=h812cca2_0
  - libogg=1.3.4=h7f98852_1
  - libopenblas=0.3.17=pthreads_h8fe5266_1
  - libopus=1.3.1=h7f98852_1
  - libpng=1.6.37=h21135ba_2
  - libpq=13.3=hd57d9b9_0
  - libprotobuf=3.16.0=h780b84a_0
  - libsodium=1.0.18=h36c2ea0_1
  - libsolv=0.7.19=h780b84a_5
  - libssh2=1.10.0=ha56f1ee_0
  - libstdcxx-ng=11.1.0=h56837e0_8
  - libta-lib=0.4.0=h516909a_0
  - libthrift=0.14.2=he6d91bd_1
  - libtiff=4.3.0=hf544144_1
  - libutf8proc=2.6.1=h7f98852_0
  - libuuid=2.32.1=h7f98852_1000
  - libuv=1.42.0=h7f98852_0
  - libvorbis=1.3.7=h9c3ff4c_0
  - libwebp-base=1.2.1=h7f98852_0
  - libxcb=1.13=h7f98852_1003
  - libxkbcommon=1.0.3=he3ba5ed_0
  - libxml2=2.9.12=h72842e0_0
  - libxslt=1.1.33=h15afd5d_2
  - llvmlite=0.37.0=py37h9d7f4d0_0
  - locket=0.2.0=py_2
  - lxml=4.6.3=py37h77fd288_0
  - lz4-c=1.9.3=h9c3ff4c_1
  - lzo=2.10=h516909a_1000
  - mako=1.1.5=pyhd8ed1ab_0
  - mamba=0.15.3=py37h7f483ca_0
  - markdown=3.3.4=pyhd8ed1ab_0
  - markupsafe=2.0.1=py37h5e8e339_0
  - matplotlib=3.4.3=py37h89c1867_0
  - matplotlib-base=3.4.3=py37h1058ff1_0
  - matplotlib-inline=0.1.3=pyhd8ed1ab_0
  - mistune=0.8.4=py37h5e8e339_1004
  - modin-core=0.10.2=py37h89c1867_3
  - modin-ray=0.10.2=py37h89c1867_3
  - msgpack-python=1.0.2=py37h2527ec5_1
  - multipledispatch=0.6.0=py_0
  - mysql-common=8.0.25=ha770c72_2
  - mysql-libs=8.0.25=hfa10184_2
  - nb_conda_kernels=2.3.1=py37h89c1867_0
  - nbclassic=0.3.1=pyhd8ed1ab_1
  - nbclient=0.5.4=pyhd8ed1ab_0
  - nbconvert=6.1.0=py37h89c1867_0
  - nbdime=3.1.0=pyhd8ed1ab_0
  - nbformat=5.1.3=pyhd8ed1ab_0
  - ncurses=6.2=h58526e2_4
  - nest-asyncio=1.5.1=pyhd8ed1ab_0
  - notebook=6.4.3=pyha770c72_0
  - nspr=4.30=h9c3ff4c_0
  - nss=3.69=hb5efdd6_0
  - numba=0.54.0=py37h2d894fd_0
  - numpy=1.20.3=py37h038b26d_1
  - olefile=0.46=pyh9f0ad1d_1
  - openjpeg=2.4.0=hb52868f_1
  - openssl=1.1.1l=h7f98852_0
  - optuna=2.9.1=pyhd8ed1ab_0
  - orc=1.6.10=h58a87f1_0
  - packaging=21.0=pyhd8ed1ab_0
  - pandas=1.3.2=py37he8f5f7f_0
  - pandoc=2.14.2=h7f98852_0
  - pandocfilters=1.4.2=py_1
  - panel=0.12.1=py_0
  - param=1.11.1=pyh6c4a22f_0
  - parquet-cpp=1.5.1=1
  - parso=0.8.2=pyhd8ed1ab_0
  - partd=1.2.0=pyhd8ed1ab_0
  - patsy=0.5.2=pyhd8ed1ab_0
  - pbr=5.6.0=pyhd8ed1ab_0
  - pcre=8.45=h9c3ff4c_0
  - pexpect=4.8.0=py37hc8dfbb8_1
  - pickle5=0.0.11=py37h5e8e339_0
  - pickleshare=0.7.5=py37hc8dfbb8_1002
  - pillow=8.3.2=py37h0f21c89_0
  - pip=21.2.4=pyhd8ed1ab_0
  - prettytable=2.2.0=pyhd8ed1ab_0
  - prometheus_client=0.11.0=pyhd8ed1ab_0
  - prompt-toolkit=3.0.20=pyha770c72_0
  - psutil=5.8.0=py37h5e8e339_1
  - pthread-stubs=0.4=h36c2ea0_1001
  - ptyprocess=0.7.0=pyhd3deb0d_0
  - pyarrow=5.0.0=py37h58331f5_5_cpu
  - pycosat=0.6.3=py37h5e8e339_1006
  - pycparser=2.20=pyh9f0ad1d_2
  - pyct=0.4.6=py_0
  - pyct-core=0.4.6=py_0
  - pygments=2.10.0=pyhd8ed1ab_0
  - pykalman=0.9.5=py_1
  - pyopenssl=20.0.1=pyhd8ed1ab_0
  - pyparsing=2.4.7=pyh9f0ad1d_0
  - pyperclip=1.8.2=pyhd8ed1ab_2
  - pyqt=5.12.3=py37h89c1867_7
  - pyqt-impl=5.12.3=py37he336c9b_7
  - pyqt5-sip=4.19.18=py37hcd2ae1e_7
  - pyqtchart=5.12=py37he336c9b_7
  - pyqtwebengine=5.12.1=py37he336c9b_7
  - pyrsistent=0.17.3=py37h5e8e339_2
  - pysocks=1.7.1=py37h89c1867_3
  - python=3.7.10=hffdb5ce_100_cpython
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python_abi=3.7=2_cp37m
  - pytz=2021.1=pyhd8ed1ab_0
  - pyviz_comms=2.1.0=py_0
  - pyyaml=5.4.1=py37h5e8e339_1
  - pyzmq=22.2.1=py37h336d617_0
  - qt=5.12.9=hda022c4_4
  - ray-core=1.6.0=py37hf931bba_0
  - re2=2021.09.01=h9c3ff4c_0
  - readline=8.1=h46c0cb4_0
  - redis-py=3.5.3=pyh9f0ad1d_0
  - reproc=14.2.3=h7f98852_0
  - reproc-cpp=14.2.3=h9c3ff4c_0
  - requests=2.26.0=pyhd8ed1ab_0
  - requests-unixsocket=0.2.0=py_0
  - ruamel_yaml=0.15.80=py37h5e8e339_1004
  - s2n=1.0.10=h9b69904_0
  - scikit-learn=0.24.2=py37hf0f1638_1
  - send2trash=1.8.0=pyhd8ed1ab_0
  - setproctitle=1.1.10=py37h5e8e339_1004
  - setuptools=58.0.4=py37h89c1867_0
  - six=1.16.0=pyh6c4a22f_0
  - smmap=3.0.5=pyh44b312d_0
  - snappy=1.1.8=he1b5a44_3
  - sniffio=1.2.0=py37h89c1867_1
  - sortedcontainers=2.4.0=pyhd8ed1ab_0
  - sqlalchemy=1.4.25=py37h5e8e339_0
  - sqlite=3.36.0=h9cd32fc_1
  - statsmodels=0.12.2=py37hb1e94ed_0
  - stevedore=3.4.0=py37h89c1867_0
  - ta-lib=0.4.19=py37ha21ca33_2
  - tabulate=0.8.9=pyhd8ed1ab_0
  - tblib=1.7.0=pyhd8ed1ab_0
  - tensorboardx=2.4=pyhd8ed1ab_0
  - terminado=0.12.1=py37h89c1867_0
  - testpath=0.5.0=pyhd8ed1ab_0
  - threadpoolctl=2.2.0=pyh8a188c0_0
  - thrift=0.13.0=py37hcd2ae1e_2
  - tk=8.6.11=h27826a3_1
  - toolz=0.11.1=py_0
  - tornado=6.1=py37h5e8e339_1
  - tqdm=4.62.2=pyhd8ed1ab_0
  - traitlets=5.1.0=pyhd8ed1ab_0
  - typing_extensions=3.10.0.0=pyha770c72_0
  - tzdata=2021a=he74cb21_1
  - tzlocal=3.0=py37h89c1867_2
  - urllib3=1.26.6=pyhd8ed1ab_0
  - wcwidth=0.2.5=pyh9f0ad1d_2
  - webencodings=0.5.1=py_1
  - websocket-client=0.57.0=py37h89c1867_4
  - wheel=0.37.0=pyhd8ed1ab_1
  - widgetsnbextension=3.5.1=py37h89c1867_4
  - xarray=0.19.0=pyhd8ed1ab_1
  - xeus=2.0.0=h7d0c39e_0
  - xeus-python=0.13.0=py37h4b46df4_1
  - xeus-python-shell=0.1.5=pyhd8ed1ab_0
  - xorg-libxau=1.0.9=h7f98852_0
  - xorg-libxdmcp=1.1.3=h7f98852_0
  - xz=5.2.5=h516909a_1
  - yaml=0.2.5=h516909a_0
  - zeromq=4.3.4=h9c3ff4c_1
  - zict=2.0.0=py_0
  - zipp=3.5.0=pyhd8ed1ab_0
  - zlib=1.2.11=h516909a_1010
  - zstandard=0.15.2=py37h5e8e339_0
  - zstd=1.5.0=ha95c52a_0
  - pip:
    - absl-py==0.13.0
    - aiohttp==3.7.4.post0
    - aiohttp-cors==0.7.0
    - aioredis==1.3.1
    - async-timeout==3.0.1
    - autograd==1.3
    - bayesian-optimization==1.2.0
    - blessings==1.7
    - cachetools==4.2.2
    - cma==2.7.0
    - colorful==0.5.4
    - cython==0.29.24
    - future==0.18.2
    - google-api-core==1.31.2
    - google-auth==1.35.0
    - google-auth-oauthlib==0.4.6
    - googleapis-common-protos==1.53.0
    - gpustat==0.6.0
    - gpy==1.10.0
    - gpytorch==1.5.1
    - grpcio==1.40.0
    - hebo==0.1.0
    - hiredis==2.0.0
    - multidict==5.1.0
    - nevergrad==0.4.3.post8
    - nvidia-ml-py3==7.352.0
    - oauthlib==3.1.1
    - opencensus==0.7.13
    - opencensus-context==0.1.2
    - paramz==0.9.5
    - protobuf==3.17.3
    - py-spy==0.3.9
    - pyasn1==0.4.8
    - pyasn1-modules==0.2.8
    - pymoo==0.4.2.2
    - requests-oauthlib==1.3.0
    - rsa==4.7.2
    - scipy==1.5.4
    - sklearn==0.0
    - tensorboard==2.6.0
    - tensorboard-data-server==0.6.1
    - tensorboard-plugin-wit==1.8.0
    - torch==1.9.1
    - werkzeug==2.0.1
    - yarl==1.6.3

Installation

conda

Conda channel

No response

Please generate some fake data for a reproducible example.

Running this cell in jupyter-lab just increases memory every time the cell is executed:

import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(1, 1, sharex=True)
axs.plot(np.zeros(30_000_000))
plt.show()

It does appear to use more memory each time, but if you throw in a gc.collect() in between, then there's no leak. I don't know that we can do anything about the lack of garbage collection.

Thanks for confirmation. Perhaps you could then update the docs so that anybody that wants to draw a nice plot doesn't have to play a detective for a Week. I don't suppose including the call to gc.collect() is an option since it's probably also a performance hit (but would perhaps make sense to add a flag to plt.show() e.g. plt.show(collect_garbage=True)) or like we have object oriented approach to matplotlib, we could have a section in the docs (e.g. "best practices"/"anti-patterns" like the guys at ray have) about performance and "big" data?

@jmakov

@jmakov I'm sorry you had difficulties with this.

We do have significant reference cycles (the line knows about the axes and the axes knows about the Line2D), so even though you no longer have a direct reference to anything (and if you closed the GUI window we do not either), but due to circular references the reference counts still are not 0. Eventually Python will collect the lingering objects, however for performance reasons only collects the oldest generation every so often (see https://devguide.python.org/garbage_collector/#optimization-generations for details and why in 2014 we backed off on calling gc.collect() see #3045). Because the problem here is a small number of extremely large arrays, it may take more executions of the cell to trigger the automatic collection than you have memory to wait!

I am pretty 👎🏻 on adding additional kwargs to either plt.show() or plt.close() because this is not a Matplotlib specific problem (it can happen with anything with big objects and circular references), but between this, the issues with Qt + C++ cleanup issues, pyplot's holding onto references, and the opposite problem with widgets and animations we I think we need an explanatory page about how Python's gc / object life cycle intersects with Matplotlib.

Thank you for the detailed explanation. Agreed, we should then add something to the docs addressing plotting "big" arrays.

@QuLogic

@QuLogic didn't have time to test before - using gc.collect() before/after plt.show() makes no difference - the memory still only increases after each call. Can you post an example that works for you?

How are you testing this? Please test without Jupyter lab. If you can replicate then it is our problem. If you cannot, then it is something in Jupyter lab or ipympl. Thanks.

I'm using Jupyter Lab but prepared a script to test:

import gc
import time

import matplotlib.pyplot as plt
import numpy as np

for i in range(5):
    fig, axs = plt.subplots(1, 1, sharex=True)
    axs.plot(np.zeros(30_000_000))
    plt.show()
    gc.collect()  # without this memory only increases
    time.sleep(3)

Thanks for feedback, will raise the issue with Jupyter guys then.

In the above, you are not closing anything, so I would expect the memory to increase.

Still increases (or do I need to close it in a different way?):

import matplotlib.pyplot as plt
import numpy as np

df = np.zeros(30_000_000)

for i in range(8):
    fig, axs = plt.subplots(1, 1, sharex=True)
    axs.plot(df)
    plt.show()
    # gc.collect()
    plt.close("all")  # same with plt.close(fig)

So gc.collect needs to be called explicitly. So as discussed, looks like a Python issue still it's not intuitive and would be great to have something in the docs. The problem in Jupyter-Lab still remains and am trying to reproduce it and then notify the Jupyter guys.

Is that call to plt.show() blocking or not and are you manually closing the GUI windows?

It blocks until I manually close the window.

What about the "sharex=True"? If that is set to False, does it still happen? Just trying to figure out where the circular references live. Another thing to consider is if jupyter is holding references to the objects that are being created.

…

On Thu, Oct 7, 2021 at 6:16 AM Jernej Makovsek ***@***.***> wrote: It blocks until I manually close the window. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#21259 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AACHF6G3PKY6UU7GA66LVZLUFVXQFANCNFSM5FFORADQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

Same behavior with sharex=False. I've run this in a separate script (as recommended here) outside of Jupyiter.

I am also experiencing a memory increase in my code when using version 3.4.3. I recently upgraded my matplotlib version in hopes to use some of the newer features with the subplots. The code was working with no memory leaks at all in version 3.3.3. However, with version 3.4.3 it seems the figure manager is no longer handling the closing of the figures.

I am not using Jupiter notebook, but running the python interpreter directly from the terminal. I've also run a profiler tool and everything seems to point back to matplotlib. The profiler (FIL) is claiming the issue is somewhere in the new_figure_manager() method. The claim is ~90% of the memory is retained in the first call to creating a figure.

My code looks something like

import matplotlib.plot as mp_plt

for path_idx, path in enumerate(file_list):
    with file.open(path, "rw") as data_list:
        data = data_list[0].data
        n_x, n_y, n_images = data.shape
        img_plot = mp_plt.figure(1, figsize=(8, 8), dpi=600) # This is where the memory leak begins; Doesn't reuses figure?
        gs = img_plot.add_gridspec(3, 2)

        for idx in range(n_images):
            ax = img_plot.add_subplot(gs[idx])
            # ax.set_stuff
            ax_img = ax.imshow(data)
            # Some stuff with ax_img and save individual subplots

        img_plot.savefig("some_path.jpeg")
        mp_plt.close("all")

The close("all") helped solve the same basic memory leak in 3.3.3, but seems to do nothing in 3.4.3.

@exoticDFT

@exoticDFT your case looks like it's #20300.

I believe this was fixed by #23712, see comments there for why there was a large build up that was fixed by a manual call to gc.collect(2).

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[Bug]: Memory leak in 3.4.3 #21259

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[Bug]: Memory leak in 3.4.3 #21259

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QuLogic commented on Oct 1, 2021

jmakov commented on Oct 1, 2021

QuLogic commented on Oct 1, 2021

jmakov commented on Oct 1, 2021

tacaswell commented on Oct 2, 2021

jmakov commented on Oct 2, 2021

jmakov commented on Oct 6, 2021

jklymak commented on Oct 6, 2021

jmakov commented on Oct 6, 2021

jklymak commented on Oct 6, 2021

jmakov commented on Oct 6, 2021

tacaswell commented on Oct 7, 2021

jmakov commented on Oct 7, 2021

WeatherGod commented on Oct 7, 2021

jmakov commented on Oct 7, 2021

exoticDFT commented on Nov 11, 2021

timhoffm commented on Nov 12, 2021

tacaswell commented on Dec 5, 2022

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