问题描述
我想从生成器列表中创建一个 dask.Bag （或 dask.Array ）。棘手的是，生成器（在评估时）太大而无法存储。
I would like to create a dask.Bag (or dask.Array) from a list of generators. The gotcha is that the generators (when evaluated) are too large for memory.
delayed_array = [delayed(generator) for generator in list_of_generators]
my_bag = db.from_delayed(delayed_array)
NB list_of_generators 就是-发电机还没有还没被消耗。
NB list_of_generators is exactly that - the generators haven't been consumed (yet).
我的问题是，创建 delayed_array 时，生成器被消耗了，RAM耗尽了。有没有一种方法可以将这些长列表放入 Bag 而不先消耗它们，或者至少以块的形式消耗它们，从而使RAM使用率保持较低水平？
My problem is that when creating delayed_array the generators are consumed and RAM is exhausted. Is there a way to get these long lists into the Bag without first consuming them, or at least consuming them in chunks so RAM use is kept low?
NNB我可以将生成器写入磁盘，然后将文件加载到 Bag -但是我想我也许可以使用 dask 解决这个问题？
NNB I could write the generators to disk, and then load the files into the Bag - but I thought I might be able to use dask to get around this?
推荐答案
Dask.bag的一个不错的子集可以与大型迭代器一起使用。您的解决方案几乎是完美的，但是您需要提供一个在调用时创建生成器的函数，而不是生成器本身。
A decent subset of Dask.bag can work with large iterators. Your solution is almost perfect, but you'll need to provide a function that creates your generators when called rather than the generators themselves.
In [1]: import dask.bag as db
In [2]: import dask
In [3]: b = db.from_delayed([dask.delayed(range)(i) for i in [100000000] * 5])
In [4]: b
Out[4]: dask.bag<bag-fro..., npartitions=5>
In [5]: b.take(5)
Out[5]: (0, 1, 2, 3, 4)
In [6]: b.sum()
Out[6]: <dask.bag.core.Item at 0x7f852d8737b8>
In [7]: b.sum().compute()
Out[7]: 24999999750000000
但是，肯定有一些方法可以咬你。一些稍微复杂些的轻快行李操作确实需要使分区具体化，这可能会炸毁RAM。
However, there are certainly ways that this can bite you. Some slightly more complex dask bag operations do need to make partitions concrete, which could blow out RAM.
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