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. .. currentmodule:: dask.array .. autosummary:: from_array from_delayed from_npy_stack from_zarr stack concatenate NumPy Slicing ------------- .. autosummary:: from_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 `_: .. code-block:: Python >>> 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: .. code-block:: Python >>> 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 :doc:`Array API `: .. autosummary:: random.binomial random.normal random.poisson random.random .. code-block:: python >>> import dask.array as da >>> rng = da.random.default_rng() >>> x = rng.random((10000, 10000), chunks=(1000, 1000)) Concatenation and Stacking -------------------------- .. autosummary:: stack concatenate Often we store data in several different locations and want to stitch them together: .. code-block:: Python 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 :doc:`concatenation and stacking ` docs. Using ``dask.delayed`` ---------------------- .. autosummary:: from_delayed stack concatenate 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 :doc:`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``: .. code-block:: python 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 :doc:`documentation on using dask.delayed with collections`. Often it is substantially faster to use ``da.map_blocks`` rather than ``da.stack`` .. code-block:: python 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: .. code-block:: python >>> df = dask.dataframes.from_pandas(...) >>> df.to_dask_array() dask.array This mirrors the `to_numpy `_ function in Pandas. The ``values`` attribute is also supported: .. code-block:: python >>> df.values dask.array 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: .. code-block:: python >>> df.to_dask_array(lengths=True) dask.array 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: .. code-block:: python >>> df.to_records() dask.array 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: .. code-block:: python >>> df.map_partitions(np.asarray) dask.array Interactions with NumPy arrays ------------------------------ Dask array operations will automatically convert NumPy arrays into single-chunk dask arrays: .. code-block:: python >>> 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: .. code-block:: python >>> x = da.ones(10, chunks=(5,)) >>> y = np.ones(10) >>> z = x + y >>> z dask.array 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 :code:`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 :code:`dask.delayed`. .. code-block:: python 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 :doc:`documentation on Array Chunks ` for more information. Store Dask Arrays ================= .. autosummary:: store to_hdf5 to_npy_stack to_zarr compute In Memory --------- .. autosummary:: compute 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: .. code-block:: Python >>> 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 ------------------- .. autosummary:: store You can store Dask arrays in any object that supports NumPy-style slice assignment like ``h5py.Dataset``: .. code-block:: Python >>> 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: .. code-block:: Python >>> da.store([array1, array2], [output1, output2]) # doctest: +SKIP HDF5 ---- .. autosummary:: to_hdf5 HDF5 is sufficiently common that there is a special function ``to_hdf5`` to store data into HDF5 files using ``h5py``: .. code-block:: Python >>> 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: .. code-block:: Python >>> 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 :doc:`remote data services ` such as S3 and GCS is supported. For example, to save data to a local zarr dataset you would do: .. code-block:: Python >>> arr.to_zarr('output.zarr') or to save to a particular bucket on S3: .. code-block:: Python >>> arr.to_zarr('s3://mybucket/output.zarr', storage_option={'key': 'mykey', 'secret': 'mysecret'}) or your own custom zarr Array: .. code-block:: Python >>> 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: .. code-block:: Python >>> arr.to_tiledb('output.tdb') or to save to a bucket on S3: .. code-block:: python >>> 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 -------------------- .. autosummary:: store 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``. .. code-block:: python 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. .. code-block:: Python >>> 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: .. code-block:: python >>> 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: .. code-block:: python >>> 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: .. code-block:: python 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: .. code-block:: python with dask.config.set(array_plugins=[warn_on_large_chunks, lambda x: x.compute()]): ... .. _Zarr: https://zarr.readthedocs.io/en/stable/