Dask:读取hdf5并写入其他hdf5文件

Question

我正在处理一个比内存更大的hdf5文件。因此，我试图使用dask来修改它。我的目标是加载文件，做一些修改(不一定要保持形状)，并将其保存到其他文件中。我创建我的文件时：

import h5py as h5
import numpy as np

source_file = "source.hdf5"
x = np.zeros((3, 3))  # In practice, x will be larger than memory
with h5.File(source_file, "w") as f:
    f.create_dataset("/x", data=x, compression="gzip")

然后，我使用以下代码加载、修改和保存它。

from dask import array as da
import h5py as h5
from dask.distributed import Client


if __name__ == "__main__":
    dask_client = Client(n_workers=1)  # No need to parallelize, just interested in dask for memory-purposes

    source_file = "source.hdf5"
    temp_filename = "target.hdf5"

    # Load dataframe
    f = h5.File(source_file, "r")
    x_da = da.from_array(f["/x"])

    # Do some modifications
    x_da = x_da * 2

    # Save to target
    x_da.to_hdf5(temp_filename, "/x", compression="gzip")

    # Close original file
    f.close()

但是，这会产生以下错误：

TypeError: ('Could not serialize object of type Dataset.', '<HDF5 dataset "x": shape (3, 3), type "<f8">') distributed.comm.utils - ERROR - ('Could not serialize object of type Dataset.', '<HDF5 dataset "x": shape (3, 3), type "<f8">')

我是不是做错了什么，还是根本不可能？如果是的话，有什么解决办法吗？

提前感谢！

Answer 1

对于感兴趣的人，我创建了一个解决方案，只需在每个块上调用compute()即可。只是分享一下，尽管我仍然对一个更好的解决方案感兴趣。

def to_hdf5(x, filename, datapath):
    """
    Appends dask array to hdf5 file
    """
    with h5.File(filename, "a") as f:
        dset = f.require_dataset(datapath, shape=x.shape, dtype=x.dtype)

        for block_ids in product(*[range(num) for num in x.numblocks]):
            pos = [sum(x.chunks[dim][0 : block_ids[dim]]) for dim in range(len(block_ids))]
            block = x.blocks[block_ids]
            slices = tuple(slice(pos[i], pos[i] + block.shape[i]) for i in range(len(block_ids)))
            dset[slices] = block.compute()