Python 垃圾收集有时在 Jupyter Notebook 中不起作用

我的一些 Jupyter 笔记本经常出现 RAM 不足的情况，而且我似乎无法释放不再需要的内存。这是一个例子：

import gc
thing = Thing()
result = thing.do_something(...)
thing = None
gc.collect()

正如你可以假设的那样，thing使用大量内存来做某事，然后我就不再需要它了。我应该能够释放它使用的内存。即使它不会写入我可以从笔记本访问的任何变量，垃圾收集器也不会正确释放空间。我发现的唯一解决方法是写作result进入pickle，重新启动内核，加载result从pickle开始，然后继续。这在运行长笔记本时确实很不方便。如何正确释放内存？

这里有很多问题。第一个是 IPython（Jupyter 在幕后使用的东西，当您看到类似的内容时，会保留对对象的附加引用）Out[67]。事实上，您可以使用该语法来调用该对象并对其执行某些操作。例如。str(Out[67])。第二个问题是 Jupyter 似乎保留了自己的输出变量引用，因此只有完全重置 IPython 才有效。但这与重新启动笔记本电脑没有太大区别。

不过有一个解决方案！我编写了一个您可以运行的函数，该函数将清除所有变量，除了您明确要求保留的变量之外。

def my_reset(*varnames):
    """
    varnames are what you want to keep
    """
    globals_ = globals()
    to_save = {v: globals_[v] for v in varnames}
    to_save['my_reset'] = my_reset  # lets keep this function by default
    del globals_
    get_ipython().magic("reset")
    globals().update(to_save)

你会像这样使用它：

x = 1
y = 2
my_reset('x')
assert 'y' not in globals()
assert x == 1

下面我写了一个笔记本，向您展示了幕后发生的一些事情，以及如何使用weakref模块。您可以尝试运行它，看看它是否可以帮助您了解发生了什么。

In [1]: class MyObject:
            pass

In [2]: obj = MyObject()

In [3]: # now lets try deleting the object
        # First, create a weak reference to obj, so we can know when it is truly deleted.
        from weakref import ref
        from sys import getrefcount
        r = ref(obj)
        print("the weak reference looks like", r)
        print("it has a reference count of", getrefcount(r()))
        # this prints a ref count of 2 (1 for obj and 1 because getrefcount
        # had a reference to obj)
        del obj
        # since obj was the only strong reference to the object, it should have been 
        # garbage collected now.
        print("the weak reference looks like", r)

the weak reference looks like <weakref at 0x7f29a809d638; to 'MyObject' at 0x7f29a810cf60>
it has a reference count of 2
the weak reference looks like <weakref at 0x7f29a809d638; dead>

In [4]: # lets try again, but this time we won't print obj, will just do "obj"
        obj = MyObject()

In [5]: print(getrefcount(obj))
        obj

2
Out[5]: <__main__.MyObject at 0x7f29a80a0c18>

In [6]: # note the "Out[5]". This is a second reference to our object
        # and will keep it alive if we delete obj
        r = ref(obj)
        del obj
        print("the weak reference looks like", r)
        print("with a reference count of:", getrefcount(r()))

the weak reference looks like <weakref at 0x7f29a809db88; to 'MyObject' at 0x7f29a80a0c18>
with a reference count of: 7

In [7]: # So what happened? It's that Out[5] that is keeping the object alive.
        # if we clear our Out variables it should go away...
        # As it turns out Juypter keeps a number of its own variables lying around, 
        # so we have to reset pretty everything.

In [8]: def my_reset(*varnames):
            """
            varnames are what you want to keep
            """
            globals_ = globals()
            to_save = {v: globals_[v] for v in varnames}
            to_save['my_reset'] = my_reset  # lets keep this function by default
            del globals_
            get_ipython().magic("reset")
            globals().update(to_save)

        my_reset('r') # clear everything except our weak reference to the object
        # you would use this to keep "thing" around.

Once deleted, variables cannot be recovered. Proceed (y/[n])? y

In [9]: print("the weak reference looks like", r)

the weak reference looks like <weakref at 0x7f29a809db88; dead>