Question

我在使用Python释放内存时遇到了麻烦。情况基本上是这样的：我将一个大型数据集拆分为4个文件。每个文件包含5000个numpy形状阵列（3072,412）的列表。我试图将每个数组的第10到第20列提取到一个新列表中。

我想要做的是按顺序读取每个文件，提取我需要的数据，然后释放我正在使用的内存，然后再转到下一个文件。但是，删除对象，将其设置为无并将其设置为0，然后调用gc.collect()似乎不起作用。这是我正在使用的代码片段：

num_files=4
start=10
end=20           
fields = []
for j in range(num_files):
    print("Working on file ", j)
    source_filename = base_filename + str(j) + ".pkl"
    print("Memory before: ", psutil.virtual_memory())
    partial_db = joblib.load(source_filename)
    print("GC tracking for partial_db is ",gc.is_tracked(partial_db))
    print("Memory after loading partial_db:",psutil.virtual_memory())
    for x in partial_db:
        fields.append(x[:,start:end])
    print("Memory after appending to fields: ",psutil.virtual_memory())
    print("GC Counts before del: ", gc.get_count())
    partial_db = None
    print("GC Counts after del: ", gc.get_count())
    gc.collect()
    print("GC Counts after collection: ", gc.get_count())
    print("Memory after freeing partial_db: ", psutil.virtual_memory())

这是几个文件后的输出：

Working on file  0
Memory before:  svmem(total=67509161984, available=66177449984,percent=2.0, used=846712832, free=33569669120, active=27423051776, inactive=5678043136, buffers=22843392, cached=33069936640, shared=15945728)
GC tracking for partial_db is  True
Memory after loading partial_db:  svmem(total=67509161984, available=40785944576, percent=39.6, used=26238181376, free=8014237696, active=54070542336, inactive=4540620800, buffers=22892544, cached=33233850368, shared=15945728)
Memory after appending to fields:  svmem(total=67509161984, available=40785944576, percent=39.6, used=26238181376, free=8014237696, active=54070542336, inactive=4540620800, buffers=22892544, cached=33233850368, shared=15945728)
GC Counts before del:  (0, 7, 3)
GC Counts after del:  (0, 7, 3)
GC Counts after collection:  (0, 0, 0)
Memory after freeing partial_db:  svmem(total=67509161984, available=40785944576, percent=39.6, used=26238181376, free=8014237696, active=54070542336, inactive=4540620800, buffers=22892544, cached=33233850368, shared=15945728)
Working on file  1
Memory before:  svmem(total=67509161984, available=40785944576, percent=39.6, used=26238181376, free=8014237696, active=54070542336, inactive=4540620800, buffers=22892544, cached=33233850368, shared=15945728)
GC tracking for partial_db is  True
Memory after loading partial_db:  svmem(total=67509161984, available=15378006016, percent=77.2, used=51626561536, free=265465856, active=62507155456, inactive=3761905664, buffers=10330112, cached=15606804480, shared=15945728)
Memory after appending to fields:  svmem(total=67509161984, available=15378006016, percent=77.2, used=51626561536, free=265465856, active=62507155456, inactive=3761905664, buffers=10330112, cached=15606804480, shared=15945728)
GC Counts before del:  (0, 4, 2)
GC Counts after del:  (0, 4, 2)
GC Counts after collection:  (0, 0, 0)
Memory after freeing partial_db:  svmem(total=67509161984, available=15378006016, percent=77.2, used=51626561536, free=265465856, active=62507155456, inactive=3761905664, buffers=10330112, cached=15606804480, shared=15945728)

如果我继续放手，它将耗尽所有内存并触发MemoryError例外。

任何人都知道我可以做些什么来确保释放partial_db使用的数据？

Answer 1

问题在于：

for x in partial_db:
    fields.append(x[:,start:end])

切割numpy数组（与普通的Python列表不同）的原因几乎没有时间，没有浪费的空间是它没有复制，它只是创建了数组内存的另一个视图。通常，这很好。但是在这里，这意味着即使在释放x之后，你仍然保持x的记忆存活，因为你永远不会释放那些切片。

还有其他方法，但最简单的方法是只附加切片的副本：

for x in partial_db:
    fields.append(x[:,start:end].copy())

切片numpy数组时减少内存使用量

1 个答案: