Create Dask Arrays
Contents
Create Dask Arrays¶
You can load or store Dask arrays from a variety of common sources like HDF5, NetCDF, Zarr, or any format that supports NumPy-style slicing.
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Create dask array from something that looks like an array. |
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Create a dask array from a dask delayed value |
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Load dask array from stack of npy files |
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Load array from the zarr storage format |
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Stack arrays along a new axis |
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Concatenate arrays along an existing axis |
NumPy Slicing¶
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Create dask array from something that looks like an array. |
Many storage formats have Python projects that expose storage using NumPy slicing syntax. These include HDF5, NetCDF, BColz, Zarr, GRIB, etc. For example, we can load a Dask array from an HDF5 file using h5py:
>>> import h5py
>>> f = h5py.File('myfile.hdf5') # HDF5 file
>>> d = f['/data/path'] # Pointer on on-disk array
>>> d.shape # d can be very large
(1000000, 1000000)
>>> x = d[:5, :5] # We slice to get numpy arrays
Given an object like d above that has dtype and shape properties
and that supports NumPy style slicing, we can construct a lazy Dask array:
>>> import dask.array as da
>>> x = da.from_array(d, chunks=(1000, 1000))
This process is entirely lazy. Neither creating the h5py object nor wrapping
it with da.from_array have loaded any data.
Random Data¶
For experimentation or benchmarking it is common to create arrays of random
data. The dask.array.random module implements most of the functions in the
numpy.random module. We list some common functions below but for a full
list see the Array API:
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Draw samples from a binomial distribution. |
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Draw random samples from a normal (Gaussian) distribution. |
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Draw samples from a Poisson distribution. |
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Return random floats in the half-open interval [0.0, 1.0). |
>>> import dask.array as da
>>> rng = da.random.default_rng()
>>> x = rng.random((10000, 10000), chunks=(1000, 1000))
Concatenation and Stacking¶
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Stack arrays along a new axis |
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Concatenate arrays along an existing axis |
Often we store data in several different locations and want to stitch them together:
dask_arrays = []
for fn in filenames:
f = h5py.File(fn)
d = f['/data']
array = da.from_array(d, chunks=(1000, 1000))
dask_arrays.append(array)
x = da.concatenate(dask_arrays, axis=0) # concatenate arrays along first axis
For more information, see concatenation and stacking docs.
Using dask.delayed¶
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Create a dask array from a dask delayed value |
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Stack arrays along a new axis |
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Concatenate arrays along an existing axis |
Sometimes NumPy-style data resides in formats that do not support NumPy-style
slicing. We can still construct Dask arrays around this data if we have a
Python function that can generate pieces of the full array if we use
dask.delayed. Dask delayed lets us delay a single function
call that would create a NumPy array. We can then wrap this delayed object
with da.from_delayed, providing a dtype and shape to produce a
single-chunked Dask array. Furthermore, we can use stack or concatenate from
before to construct a larger lazy array.
As an example, consider loading a stack of images using skimage.io.imread:
import skimage.io
import dask.array as da
import dask
imread = dask.delayed(skimage.io.imread, pure=True) # Lazy version of imread
filenames = sorted(glob.glob('*.jpg'))
lazy_images = [imread(path) for path in filenames] # Lazily evaluate imread on each path
sample = lazy_images[0].compute() # load the first image (assume rest are same shape/dtype)
arrays = [da.from_delayed(lazy_image, # Construct a small Dask array
dtype=sample.dtype, # for every lazy value
shape=sample.shape)
for lazy_image in lazy_images]
stack = da.stack(arrays, axis=0) # Stack all small Dask arrays into one
See documentation on using dask.delayed with collections.
Often it is substantially faster to use da.map_blocks rather than da.stack
import glob
import skimage.io
import numpy as np
import dask.array as da
filenames = sorted(glob.glob('*.jpg'))
def read_one_image(block_id, filenames=filenames, axis=0):
# a function that reads in one chunk of data
path = filenames[block_id[axis]]
image = skimage.io.imread(path)
return np.expand_dims(image, axis=axis)
# load the first image (assume rest are same shape/dtype)
sample = skimage.io.imread(filenames[0])
stack = da.map_blocks(
read_one_image,
dtype=sample.dtype,
chunks=((1,) * len(filenames), *sample.shape)
)
From Dask DataFrame¶
There are several ways to create a Dask array from a Dask DataFrame. Dask DataFrames have a to_dask_array method:
>>> df = dask.dataframes.from_pandas(...)
>>> df.to_dask_array()
dask.array<values, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>
This mirrors the to_numpy
function in Pandas. The values attribute is also supported:
>>> df.values
dask.array<values, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>
However, these arrays do not have known chunk sizes because dask.dataframe does not track the number of rows in each partition. This means that some operations like slicing will not operate correctly.
The chunk sizes can be computed:
>>> df.to_dask_array(lengths=True)
dask.array<array, shape=(100, 3), dtype=float64, chunksize=(50, 3), chunktype=numpy.ndarray>
Specifying lengths=True triggers immediate computation of the chunk sizes.
This enables downstream computations that rely on having known chunk sizes
(e.g., slicing).
The Dask DataFrame to_records method also returns a Dask Array, but does not compute the shape
information:
>>> df.to_records()
dask.array<to_records, shape=(nan,), dtype=(numpy.record, [('index', '<i8'), ('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), chunksize=(nan,), chunktype=numpy.ndarray>
If you have a function that converts a Pandas DataFrame into a NumPy array,
then calling map_partitions with that function on a Dask DataFrame will
produce a Dask array:
>>> df.map_partitions(np.asarray)
dask.array<asarray, shape=(nan, 3), dtype=float64, chunksize=(nan, 3), chunktype=numpy.ndarray>
Interactions with NumPy arrays¶
Dask array operations will automatically convert NumPy arrays into single-chunk dask arrays:
>>> x = da.sum(np.ones(5))
>>> x.compute()
5
When NumPy and Dask arrays interact, the result will be a Dask array. Automatic rechunking rules will generally slice the NumPy array into the appropriate Dask chunk shape:
>>> x = da.ones(10, chunks=(5,))
>>> y = np.ones(10)
>>> z = x + y
>>> z
dask.array<add, shape=(10,), dtype=float64, chunksize=(5,), chunktype=numpy.ndarray>
These interactions work not just for NumPy arrays but for any object that has shape and dtype attributes and implements NumPy slicing syntax.
Memory mapping¶
Memory mapping can be a highly effective method to access raw binary data since
it has nearly zero overhead if the data is already in the file system cache. For
the threaded scheduler, creating a Dask array from a raw binary file can be as simple as
a = da.from_array(np.memmap(filename, shape=shape, dtype=dtype, mode='r')).
For multiprocessing or distributed schedulers, the memory map for each array
chunk should be created on the correct worker process and not on the main
process to avoid data transfer through the cluster. This can be achieved by
wrapping the function that creates the memory map using dask.delayed.
import numpy as np
import dask
import dask.array as da
def mmap_load_chunk(filename, shape, dtype, offset, sl):
'''
Memory map the given file with overall shape and dtype and return a slice
specified by :code:`sl`.
Parameters
----------
filename : str
shape : tuple
Total shape of the data in the file
dtype:
NumPy dtype of the data in the file
offset : int
Skip :code:`offset` bytes from the beginning of the file.
sl:
Object that can be used for indexing or slicing a NumPy array to
extract a chunk
Returns
-------
numpy.memmap or numpy.ndarray
View into memory map created by indexing with :code:`sl`,
or NumPy ndarray in case no view can be created using :code:`sl`.
'''
data = np.memmap(filename, mode='r', shape=shape, dtype=dtype, offset=offset)
return data[sl]
def mmap_dask_array(filename, shape, dtype, offset=0, blocksize=5):
'''
Create a Dask array from raw binary data in :code:`filename`
by memory mapping.
This method is particularly effective if the file is already
in the file system cache and if arbitrary smaller subsets are
to be extracted from the Dask array without optimizing its
chunking scheme.
It may perform poorly on Windows if the file is not in the file
system cache. On Linux it performs well under most circumstances.
Parameters
----------
filename : str
shape : tuple
Total shape of the data in the file
dtype:
NumPy dtype of the data in the file
offset : int, optional
Skip :code:`offset` bytes from the beginning of the file.
blocksize : int, optional
Chunk size for the outermost axis. The other axes remain unchunked.
Returns
-------
dask.array.Array
Dask array matching :code:`shape` and :code:`dtype`, backed by
memory-mapped chunks.
'''
load = dask.delayed(mmap_load_chunk)
chunks = []
for index in range(0, shape[0], blocksize):
# Truncate the last chunk if necessary
chunk_size = min(blocksize, shape[0] - index)
chunk = dask.array.from_delayed(
load(
filename,
shape=shape,
dtype=dtype,
offset=offset,
sl=slice(index, index + chunk_size)
),
shape=(chunk_size, ) + shape[1:],
dtype=dtype
)
chunks.append(chunk)
return da.concatenate(chunks, axis=0)
x = mmap_dask_array(
filename='testfile-50-50-100-100-float32.raw',
shape=(50, 50, 100, 100),
dtype=np.float32
)
Chunks¶
See documentation on Array Chunks for more information.
Store Dask Arrays¶
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Store dask arrays in array-like objects, overwrite data in target |
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Store arrays in HDF5 file |
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Write dask array to a stack of .npy files |
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Save array to the zarr storage format |
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Compute several dask collections at once. |
In Memory¶
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Compute several dask collections at once. |
If you have a small amount of data, you can call np.array or .compute()
on your Dask array to turn in to a normal NumPy array:
>>> x = da.arange(6, chunks=3)
>>> y = x**2
>>> np.array(y)
array([0, 1, 4, 9, 16, 25])
>>> y.compute()
array([0, 1, 4, 9, 16, 25])
NumPy style slicing¶
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Store dask arrays in array-like objects, overwrite data in target |
You can store Dask arrays in any object that supports NumPy-style slice
assignment like h5py.Dataset:
>>> import h5py
>>> f = h5py.File('myfile.hdf5')
>>> d = f.require_dataset('/data', shape=x.shape, dtype=x.dtype)
>>> da.store(x, d)
Also, you can store several arrays in one computation by passing lists of sources and destinations:
>>> da.store([array1, array2], [output1, output2]) # doctest: +SKIP
HDF5¶
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Store arrays in HDF5 file |
HDF5 is sufficiently common that there is a special function to_hdf5 to
store data into HDF5 files using h5py:
>>> da.to_hdf5('myfile.hdf5', '/y', y) # doctest: +SKIP
You can store several arrays in one computation with the function
da.to_hdf5 by passing in a dictionary:
>>> da.to_hdf5('myfile.hdf5', {'/x': x, '/y': y}) # doctest: +SKIP
Zarr¶
The Zarr format is a chunk-wise binary array storage file format with a good selection of encoding and compression options. Due to each chunk being stored in a separate file, it is ideal for parallel access in both reading and writing (for the latter, if the Dask array chunks are aligned with the target). Furthermore, storage in remote data services such as S3 and GCS is supported.
For example, to save data to a local zarr dataset you would do:
>>> arr.to_zarr('output.zarr')
or to save to a particular bucket on S3:
>>> arr.to_zarr('s3://mybucket/output.zarr', storage_option={'key': 'mykey',
'secret': 'mysecret'})
or your own custom zarr Array:
>>> z = zarr.create((10,), dtype=float, store=zarr.ZipStore("output.zarr"))
>>> arr.to_zarr(z)
To retrieve those data, you would do da.from_zarr with exactly the same arguments. The
chunking of the resultant Dask array is defined by how the files were saved, unless
otherwise specified.
TileDB¶
TileDB is a binary array format and storage manager with tunable chunking, layout, and compression options. The TileDB storage manager library includes support for scalable storage backends such as S3 API compatible object stores and HDFS, with automatic scaling, and supports multi-threaded and multi-process reads (consistent) and writes (eventually-consistent).
To save data to a local TileDB array:
>>> arr.to_tiledb('output.tdb')
or to save to a bucket on S3:
>>> arr.to_tiledb('s3://mybucket/output.tdb',
storage_options={'vfs.s3.aws_access_key_id': 'mykey',
'vfs.s3.aws_secret_access_key': 'mysecret'})
Files may be retrieved by running da.from_tiledb with the same URI, and any necessary arguments.
Intermediate storage¶
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Store dask arrays in array-like objects, overwrite data in target |
In some cases, one may wish to store an intermediate result in long term
storage. This differs from persist, which is mainly used to manage
intermediate results within Dask that don’t necessarily have longevity.
Also it differs from storing final results as these mark the end of the Dask
graph. Thus intermediate results are easier to reuse without reloading data.
Intermediate storage is mainly useful in cases where the data is needed
outside of Dask (e.g. on disk, in a database, in the cloud, etc.). It can
be useful as a checkpoint for long running or error-prone computations.
The intermediate storage use case differs from the typical storage use case as
a Dask Array is returned to the user that represents the result of that
storage operation. This is typically done by setting the store function’s
return_stored flag to True.
x.store() # stores data, returns nothing
x = x.store(return_stored=True) # stores data, returns new dask array backed by that data
The user can then decide whether the
storage operation happens immediately (by setting the compute flag to
True) or later (by setting the compute flag to False). In all
other ways, this behaves the same as a normal call to store. Some examples
are shown below.
>>> import dask.array as da
>>> import zarr as zr
>>> c = (2, 2)
>>> d = da.ones((10, 11), chunks=c)
>>> z1 = zr.open_array('lazy.zarr', shape=d.shape, dtype=d.dtype, chunks=c)
>>> z2 = zr.open_array('eager.zarr', shape=d.shape, dtype=d.dtype, chunks=c)
>>> d1 = d.store(z1, compute=False, return_stored=True)
>>> d2 = d.store(z2, compute=True, return_stored=True)
This can be combined with any other storage strategies either noted above, in the docs or for any specialized storage types.
Plugins¶
We can run arbitrary user-defined functions on Dask arrays whenever they are
constructed. This allows us to build a variety of custom behaviors that improve
debugging, user warning, etc. You can register a list of functions to run on
all Dask arrays to the global array_plugins= value:
>>> def f(x):
... print(x.nbytes)
>>> with dask.config.set(array_plugins=[f]):
... x = da.ones((10, 1), chunks=(5, 1))
... y = x.dot(x.T)
80
80
800
800
If the plugin function returns None, then the input Dask array will be returned without change. If the plugin function returns something else, then that value will be the result of the constructor.
Examples¶
Automatically compute¶
We may wish to turn some Dask array code into normal NumPy code. This is useful, for example, to track down errors immediately that would otherwise be hidden by Dask’s lazy semantics:
>>> with dask.config.set(array_plugins=[lambda x: x.compute()]):
... x = da.arange(5, chunks=2)
>>> x # this was automatically converted into a numpy array
array([0, 1, 2, 3, 4])
Warn on large chunks¶
We may wish to warn users if they are creating chunks that are too large:
def warn_on_large_chunks(x):
shapes = list(itertools.product(*x.chunks))
nbytes = [x.dtype.itemsize * np.prod(shape) for shape in shapes]
if any(nb > 1e9 for nb in nbytes):
warnings.warn("Array contains very large chunks")
with dask.config.set(array_plugins=[warn_on_large_chunks]):
...
Combine¶
You can also combine these plugins into a list. They will run one after the other, chaining results through them:
with dask.config.set(array_plugins=[warn_on_large_chunks, lambda x: x.compute()]):
...