JuliaPy / JuliaPy/PythonCall.jl
Memory leak when calling certain python packages from Julia via PythonCall
- Lenguaje dominante
- Julia
- Estrellas
- 1.1k
- Forks
- 86
- Merge medio
- 1 d 22 h
- PR fusionados (30 d)
- 3
Descripción
Affects: PythonCall
Describe the bug
Memory is not released when repeatedly creating and closing matplotlib figures via PythonCall. I originally had the issue with an elaborate cartopy plotting loop, but have managed to recreate it in a simple while true loop MWE. Each iteration allocates a new figure/axes via plt.subplots, immediately closes it with plt.close, calls PythonCall.pydel! on both the figure and axes objects, and calls GC.gc(). However, there is still a leak until the process is killed.
This does not occur when repeatedly allocating and deleting a numpy array in an equivalent loop, so the issue has something to do with what kind of python objects are created. I also have similar memory leak issues with scikit-learn.
The issue is reproducable both in the Julia REPL and in the VS Code Julia extension, and when I execute a script containing the below code from the command line.
MWE:
using PythonCall
@py import matplotlib.pyplot as plt
i = 0
while true
global i += 1
fig, ax = plt.subplots(1, 1)
plt.close(fig)
PythonCall.pydel!(ax)
PythonCall.pydel!(fig)
GC.gc()
println("Iteration $i done")
end
My system
OS: macOS 15.7.4 (arm64, Apple Silicon)
Julia: 1.12.6
Python: 3.14.2 (conda-forge, Clang 20.1.8)
PythonCall: 0.9.31
matplotlib: 3.10.8
Thanks for your help!
Guía de contribución
No hay ninguna guía de contribución indexada para este repositorio
Línea de trabajo
Start by running the supplied PythonCall and matplotlib MWE with the listed Julia, Python, and package versions, then compare its memory behavior with the numpy loop. Investigate why closing and deleting the figure and axes does not release memory; done means the repeated loop no longer grows indefinitely and the behavior is verified in the REPL and command-line script.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- julia, matplotlib, python
- Área
- performance
- Tipo de issue
- Error
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Tranquilo
- Claridad
- Bastante claro
- Aptitud para principiantes
- 45/100