Matplotlib 错误会导致内存泄漏.我怎样才能释放那段记忆? [英] Matplotlib errors result in a memory leak. How can I free up that memory?

问题描述

我正在运行一个包含 matplotlib 并允许用户指定图形轴的 django 应用程序.这可能会导致溢出错误:超出 Agg 复杂度"

I am running a django app that includes matplotlib and allows the user to specify the axes of the graph. This can result in 'Overflow Error: Agg complexity exceeded'

当这种情况发生时，多达 100MB 的 RAM 会被占用.通常我会使用 fig.gcf()、plot.close() 和 gc.collect() 来释放内存，但是内存与错误相关的似乎与绘图对象无关.

When that happens up to 100MB of RAM get tied up. Normally I free that memory up using fig.gcf(), plot.close(), and gc.collect(), but the memory associated with the error does not seem to be associated with the plot object.

有谁知道我怎样才能释放那段记忆?

Does anyone know how I can release that memory?

谢谢.

这里是一些代码，它给了我 Agg 复杂性错误.

Here is some code that gives me the Agg Complexity Error.

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np      
import gc

a = np.arange(1000000)
b = np.random.randn(1000000)

fig = plt.figure(num=1, dpi=100, facecolor='w', edgecolor='w')
fig.set_size_inches(10,7)
ax = fig.add_subplot(111)
ax.plot(a, b)

fig.savefig('yourdesktop/random.png')   # code gives me an error here

fig.clf()    # normally I use these lines to release the memory
plt.close()
del a, b
gc.collect()

推荐答案

我假设您至少可以运行您发布的代码一次.该问题仅在多次运行发布的代码后才会出现.正确吗?

I assume you can run the code you posted at least once. The problem only manifests itself after running the posted code many times. Correct?

如果是这样，以下内容可以避免问题，而无需真正确定问题的根源.也许这是一件坏事，但这在紧要关头起作用:只需使用 multiprocessing 在单独的进程中运行内存密集型代码.您不必担心 fig.clf() 或 plt.close() 或 del a,b 或 gc.collect().进程结束时所有内存都被释放.

If so, the following avoids the problem without really identifying the source of the problem. Maybe that is a bad thing, but this works in a pinch: Simply use multiprocessing to run the memory-intensive code in a separate process. You don't have to worry about fig.clf() or plt.close() or del a,b or gc.collect(). All memory is freed when the process ends.

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np      

import multiprocessing as mp

def worker():
    N=1000000
    a = np.arange(N)
    b = np.random.randn(N)

    fig = plt.figure(num=1, dpi=100, facecolor='w', edgecolor='w')
    fig.set_size_inches(10,7)
    ax = fig.add_subplot(111)
    ax.plot(a, b)

    fig.savefig('/tmp/random.png')   # code gives me an error here

if __name__=='__main__':
    proc=mp.Process(target=worker)
    proc.daemon=True
    proc.start()
    proc.join()

您也不必proc.join().join 将阻塞主进程，直到 worker 完成.如果省略 join，则主进程将继续在后台运行 worker 进程.

You don't have to proc.join() either. The join will block the main process until the worker completes. If you omit the join, then the main process simply continues with the worker process working in the background.

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