减少分割numpy数组时的内存使用量

Question

我很难用Python释放内存。情况基本上是这样的:我有一个大的数据集分裂成4个文件。每个文件包含一个5000 numpy形状数组(3072,412)的列表。我试图把每个数组的第10列到第20列提取到一个新的列表中。

我想要做的是依次读取每个文件，提取我需要的数据，并在进入下一个文件之前释放我正在使用的内存。但是，删除对象、将其设置为None并将其设置为0，然后调用gc.collect()似乎不起作用。下面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())

下面是几个文件之后的输出：

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)

如果我继续放它走，它将耗尽所有内存，并触发一个MemoryError异常。

有人知道我能做些什么来确保partial_db使用的数据被释放吗？

Answer 1

问题是：

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

对numpy数组进行切片(与普通Python列表不同)几乎不需要时间，也不会浪费空间，原因是它没有复制，它只是在数组的内存中创建另一个视图。通常情况下，那很好。但是在这里，这意味着即使在释放了x本身之后，您仍将保持x的内存存活，因为您从未释放过这些切片。

还有其他方法可以解决这个问题，但最简单的方法是仅仅附加切片的副本：

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