.. currentmodule:: xarray .. _dask: Parallel computing with Dask ============================ Xarray integrates with `Dask `__ to support parallel computations and streaming computation on datasets that don't fit into memory. Currently, Dask is an entirely optional feature for xarray. However, the benefits of using Dask are sufficiently strong that Dask may become a required dependency in a future version of xarray. For a full example of how to use xarray's Dask integration, read the `blog post introducing xarray and Dask`_. More up-to-date examples may be found at the `Pangeo project's gallery `_ and at the `Dask examples website `_. .. _blog post introducing xarray and Dask: http://stephanhoyer.com/2015/06/11/xray-dask-out-of-core-labeled-arrays/ What is a Dask array? --------------------- .. image:: ../_static/dask_array.png :width: 40 % :align: right :alt: A Dask array Dask divides arrays into many small pieces, called *chunks*, each of which is presumed to be small enough to fit into memory. Unlike NumPy, which has eager evaluation, operations on Dask arrays are lazy. Operations queue up a series of tasks mapped over blocks, and no computation is performed until you actually ask values to be computed (e.g., to print results to your screen or write to disk). At that point, data is loaded into memory and computation proceeds in a streaming fashion, block-by-block. The actual computation is controlled by a multi-processing or thread pool, which allows Dask to take full advantage of multiple processors available on most modern computers. For more details on Dask, read `its documentation `__. Note that xarray only makes use of ``dask.array`` and ``dask.delayed``. .. _dask.io: Reading and writing data ------------------------ The usual way to create a ``Dataset`` filled with Dask arrays is to load the data from a netCDF file or files. You can do this by supplying a ``chunks`` argument to :py:func:`~xarray.open_dataset` or using the :py:func:`~xarray.open_mfdataset` function. .. ipython:: python :suppress: import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) np.set_printoptions(precision=3, linewidth=100, threshold=100, edgeitems=3) ds = xr.Dataset( { "temperature": ( ("time", "latitude", "longitude"), np.random.randn(30, 180, 180), ), "time": pd.date_range("2015-01-01", periods=30), "longitude": np.arange(180), "latitude": np.arange(89.5, -90.5, -1), } ) ds.to_netcdf("example-data.nc") .. ipython:: python ds = xr.open_dataset("example-data.nc", chunks={"time": 10}) ds In this example ``latitude`` and ``longitude`` do not appear in the ``chunks`` dict, so only one chunk will be used along those dimensions. It is also entirely equivalent to opening a dataset using :py:meth:`~xarray.open_dataset` and then chunking the data using the ``chunk`` method, e.g., ``xr.open_dataset('example-data.nc').chunk({'time': 10})``. To open multiple files simultaneously in parallel using Dask delayed, use :py:func:`~xarray.open_mfdataset`:: xr.open_mfdataset('my/files/*.nc', parallel=True) This function will automatically concatenate and merge datasets into one in the simple cases that it understands (see :py:func:`~xarray.combine_by_coords` for the full disclaimer). By default, :py:meth:`~xarray.open_mfdataset` will chunk each netCDF file into a single Dask array; again, supply the ``chunks`` argument to control the size of the resulting Dask arrays. In more complex cases, you can open each file individually using :py:meth:`~xarray.open_dataset` and merge the result, as described in :ref:`combining data`. Passing the keyword argument ``parallel=True`` to :py:meth:`~xarray.open_mfdataset` will speed up the reading of large multi-file datasets by executing those read tasks in parallel using ``dask.delayed``. You'll notice that printing a dataset still shows a preview of array values, even if they are actually Dask arrays. We can do this quickly with Dask because we only need to compute the first few values (typically from the first block). To reveal the true nature of an array, print a DataArray: .. ipython:: python ds.temperature Once you've manipulated a Dask array, you can still write a dataset too big to fit into memory back to disk by using :py:meth:`~xarray.Dataset.to_netcdf` in the usual way. .. ipython:: python ds.to_netcdf("manipulated-example-data.nc") By setting the ``compute`` argument to ``False``, :py:meth:`~xarray.Dataset.to_netcdf` will return a ``dask.delayed`` object that can be computed later. .. ipython:: python from dask.diagnostics import ProgressBar # or distributed.progress when using the distributed scheduler delayed_obj = ds.to_netcdf("manipulated-example-data.nc", compute=False) with ProgressBar(): results = delayed_obj.compute() .. note:: When using Dask's distributed scheduler to write NETCDF4 files, it may be necessary to set the environment variable `HDF5_USE_FILE_LOCKING=FALSE` to avoid competing locks within the HDF5 SWMR file locking scheme. Note that writing netCDF files with Dask's distributed scheduler is only supported for the `netcdf4` backend. A dataset can also be converted to a Dask DataFrame using :py:meth:`~xarray.Dataset.to_dask_dataframe`. .. ipython:: python :okwarning: df = ds.to_dask_dataframe() df Dask DataFrames do not support multi-indexes so the coordinate variables from the dataset are included as columns in the Dask DataFrame. .. ipython:: python :suppress: import os os.remove("example-data.nc") os.remove("manipulated-example-data.nc") Using Dask with xarray ---------------------- Nearly all existing xarray methods (including those for indexing, computation, concatenating and grouped operations) have been extended to work automatically with Dask arrays. When you load data as a Dask array in an xarray data structure, almost all xarray operations will keep it as a Dask array; when this is not possible, they will raise an exception rather than unexpectedly loading data into memory. Converting a Dask array into memory generally requires an explicit conversion step. One notable exception is indexing operations: to enable label based indexing, xarray will automatically load coordinate labels into memory. .. tip:: By default, dask uses its multi-threaded scheduler, which distributes work across multiple cores and allows for processing some datasets that do not fit into memory. For running across a cluster, `setup the distributed scheduler `_. The easiest way to convert an xarray data structure from lazy Dask arrays into *eager*, in-memory NumPy arrays is to use the :py:meth:`~xarray.Dataset.load` method: .. ipython:: python ds.load() You can also access :py:attr:`~xarray.DataArray.values`, which will always be a NumPy array: .. ipython:: :verbatim: In [5]: ds.temperature.values Out[5]: array([[[ 4.691e-01, -2.829e-01, ..., -5.577e-01, 3.814e-01], [ 1.337e+00, -1.531e+00, ..., 8.726e-01, -1.538e+00], ... # truncated for brevity Explicit conversion by wrapping a DataArray with ``np.asarray`` also works: .. ipython:: :verbatim: In [5]: np.asarray(ds.temperature) Out[5]: array([[[ 4.691e-01, -2.829e-01, ..., -5.577e-01, 3.814e-01], [ 1.337e+00, -1.531e+00, ..., 8.726e-01, -1.538e+00], ... Alternatively you can load the data into memory but keep the arrays as Dask arrays using the :py:meth:`~xarray.Dataset.persist` method: .. ipython:: python ds = ds.persist() :py:meth:`~xarray.Dataset.persist` is particularly useful when using a distributed cluster because the data will be loaded into distributed memory across your machines and be much faster to use than reading repeatedly from disk. .. warning:: On a single machine :py:meth:`~xarray.Dataset.persist` will try to load all of your data into memory. You should make sure that your dataset is not larger than available memory. .. note:: For more on the differences between :py:meth:`~xarray.Dataset.persist` and :py:meth:`~xarray.Dataset.compute` see this `Stack Overflow answer `_ and the `Dask documentation `_. For performance you may wish to consider chunk sizes. The correct choice of chunk size depends both on your data and on the operations you want to perform. With xarray, both converting data to a Dask arrays and converting the chunk sizes of Dask arrays is done with the :py:meth:`~xarray.Dataset.chunk` method: .. ipython:: python :suppress: ds = ds.chunk({"time": 10}) .. ipython:: python rechunked = ds.chunk({"latitude": 100, "longitude": 100}) You can view the size of existing chunks on an array by viewing the :py:attr:`~xarray.Dataset.chunks` attribute: .. ipython:: python rechunked.chunks If there are not consistent chunksizes between all the arrays in a dataset along a particular dimension, an exception is raised when you try to access ``.chunks``. .. note:: In the future, we would like to enable automatic alignment of Dask chunksizes (but not the other way around). We might also require that all arrays in a dataset share the same chunking alignment. Neither of these are currently done. NumPy ufuncs like ``np.sin`` transparently work on all xarray objects, including those that store lazy Dask arrays: .. ipython:: python import numpy as np np.sin(rechunked) To access Dask arrays directly, use the :py:attr:`DataArray.data ` attribute. This attribute exposes array data either as a Dask array or as a NumPy array, depending on whether it has been loaded into Dask or not: .. ipython:: python ds.temperature.data .. note:: ``.data`` is also used to expose other "computable" array backends beyond Dask and NumPy (e.g. sparse and pint arrays). .. _dask.automatic-parallelization: Automatic parallelization with ``apply_ufunc`` and ``map_blocks`` ----------------------------------------------------------------- Almost all of xarray's built-in operations work on Dask arrays. If you want to use a function that isn't wrapped by xarray, and have it applied in parallel on each block of your xarray object, you have three options: 1. Extract Dask arrays from xarray objects (``.data``) and use Dask directly. 2. Use :py:func:`~xarray.apply_ufunc` to apply functions that consume and return NumPy arrays. 3. Use :py:func:`~xarray.map_blocks`, :py:meth:`Dataset.map_blocks` or :py:meth:`DataArray.map_blocks` to apply functions that consume and return xarray objects. ``apply_ufunc`` ~~~~~~~~~~~~~~~ Another option is to use xarray's :py:func:`~xarray.apply_ufunc`, which can automate `embarrassingly parallel `__ "map" type operations where a function written for processing NumPy arrays should be repeatedly applied to xarray objects containing Dask arrays. It works similarly to :py:func:`dask.array.map_blocks` and :py:func:`dask.array.blockwise`, but without requiring an intermediate layer of abstraction. For the best performance when using Dask's multi-threaded scheduler, wrap a function that already releases the global interpreter lock, which fortunately already includes most NumPy and Scipy functions. Here we show an example using NumPy operations and a fast function from `bottleneck `__, which we use to calculate `Spearman's rank-correlation coefficient `__: .. code-block:: python import numpy as np import xarray as xr import bottleneck def covariance_gufunc(x, y): return ( (x - x.mean(axis=-1, keepdims=True)) * (y - y.mean(axis=-1, keepdims=True)) ).mean(axis=-1) def pearson_correlation_gufunc(x, y): return covariance_gufunc(x, y) / (x.std(axis=-1) * y.std(axis=-1)) def spearman_correlation_gufunc(x, y): x_ranks = bottleneck.rankdata(x, axis=-1) y_ranks = bottleneck.rankdata(y, axis=-1) return pearson_correlation_gufunc(x_ranks, y_ranks) def spearman_correlation(x, y, dim): return xr.apply_ufunc( spearman_correlation_gufunc, x, y, input_core_dims=[[dim], [dim]], dask="parallelized", output_dtypes=[float], ) The only aspect of this example that is different from standard usage of ``apply_ufunc()`` is that we needed to supply the ``output_dtypes`` arguments. (Read up on :ref:`comput.wrapping-custom` for an explanation of the "core dimensions" listed in ``input_core_dims``.) Our new ``spearman_correlation()`` function achieves near linear speedup when run on large arrays across the four cores on my laptop. It would also work as a streaming operation, when run on arrays loaded from disk: .. ipython:: :verbatim: In [56]: rs = np.random.RandomState(0) In [57]: array1 = xr.DataArray(rs.randn(1000, 100000), dims=["place", "time"]) # 800MB In [58]: array2 = array1 + 0.5 * rs.randn(1000, 100000) # using one core, on NumPy arrays In [61]: %time _ = spearman_correlation(array1, array2, 'time') CPU times: user 21.6 s, sys: 2.84 s, total: 24.5 s Wall time: 24.9 s In [8]: chunked1 = array1.chunk({"place": 10}) In [9]: chunked2 = array2.chunk({"place": 10}) # using all my laptop's cores, with Dask In [63]: r = spearman_correlation(chunked1, chunked2, "time").compute() In [64]: %time _ = r.compute() CPU times: user 30.9 s, sys: 1.74 s, total: 32.6 s Wall time: 4.59 s One limitation of ``apply_ufunc()`` is that it cannot be applied to arrays with multiple chunks along a core dimension: .. ipython:: :verbatim: In [63]: spearman_correlation(chunked1, chunked2, "place") ValueError: dimension 'place' on 0th function argument to apply_ufunc with dask='parallelized' consists of multiple chunks, but is also a core dimension. To fix, rechunk into a single Dask array chunk along this dimension, i.e., ``.rechunk({'place': -1})``, but beware that this may significantly increase memory usage. This reflects the nature of core dimensions, in contrast to broadcast (non-core) dimensions that allow operations to be split into arbitrary chunks for application. .. tip:: For the majority of NumPy functions that are already wrapped by Dask, it's usually a better idea to use the pre-existing ``dask.array`` function, by using either a pre-existing xarray methods or :py:func:`~xarray.apply_ufunc()` with ``dask='allowed'``. Dask can often have a more efficient implementation that makes use of the specialized structure of a problem, unlike the generic speedups offered by ``dask='parallelized'``. ``map_blocks`` ~~~~~~~~~~~~~~ Functions that consume and return xarray objects can be easily applied in parallel using :py:func:`map_blocks`. Your function will receive an xarray Dataset or DataArray subset to one chunk along each chunked dimension. .. ipython:: python ds.temperature This DataArray has 3 chunks each with length 10 along the time dimension. At compute time, a function applied with :py:func:`map_blocks` will receive a DataArray corresponding to a single block of shape 10x180x180 (time x latitude x longitude) with values loaded. The following snippet illustrates how to check the shape of the object received by the applied function. .. ipython:: python def func(da): print(da.sizes) return da.time mapped = xr.map_blocks(func, ds.temperature) mapped Notice that the :py:meth:`map_blocks` call printed ``Frozen({'time': 0, 'latitude': 0, 'longitude': 0})`` to screen. ``func`` is received 0-sized blocks! :py:meth:`map_blocks` needs to know what the final result looks like in terms of dimensions, shapes etc. It does so by running the provided function on 0-shaped inputs (*automated inference*). This works in many cases, but not all. If automatic inference does not work for your function, provide the ``template`` kwarg (see below). In this case, automatic inference has worked so let's check that the result is as expected. .. ipython:: python mapped.load(scheduler="single-threaded") mapped.identical(ds.time) Note that we use ``.load(scheduler="single-threaded")`` to execute the computation. This executes the Dask graph in `serial` using a for loop, but allows for printing to screen and other debugging techniques. We can easily see that our function is receiving blocks of shape 10x180x180 and the returned result is identical to ``ds.time`` as expected. Here is a common example where automated inference will not work. .. ipython:: python :okexcept: def func(da): print(da.sizes) return da.isel(time=[1]) mapped = xr.map_blocks(func, ds.temperature) ``func`` cannot be run on 0-shaped inputs because it is not possible to extract element 1 along a dimension of size 0. In this case we need to tell :py:func:`map_blocks` what the returned result looks like using the ``template`` kwarg. ``template`` must be an xarray Dataset or DataArray (depending on what the function returns) with dimensions, shapes, chunk sizes, attributes, coordinate variables *and* data variables that look exactly like the expected result. The variables should be dask-backed and hence not incur much memory cost. .. note:: Note that when ``template`` is provided, ``attrs`` from ``template`` are copied over to the result. Any ``attrs`` set in ``func`` will be ignored. .. ipython:: python template = ds.temperature.isel(time=[1, 11, 21]) mapped = xr.map_blocks(func, ds.temperature, template=template) Notice that the 0-shaped sizes were not printed to screen. Since ``template`` has been provided :py:func:`map_blocks` does not need to infer it by running ``func`` on 0-shaped inputs. .. ipython:: python mapped.identical(template) :py:func:`map_blocks` also allows passing ``args`` and ``kwargs`` down to the user function ``func``. ``func`` will be executed as ``func(block_xarray, *args, **kwargs)`` so ``args`` must be a list and ``kwargs`` must be a dictionary. .. ipython:: python def func(obj, a, b=0): return obj + a + b mapped = ds.map_blocks(func, args=[10], kwargs={"b": 10}) expected = ds + 10 + 10 mapped.identical(expected) .. tip:: As :py:func:`map_blocks` loads each block into memory, reduce as much as possible objects consumed by user functions. For example, drop useless variables before calling ``func`` with :py:func:`map_blocks`. Chunking and performance ------------------------ The ``chunks`` parameter has critical performance implications when using Dask arrays. If your chunks are too small, queueing up operations will be extremely slow, because Dask will translate each operation into a huge number of operations mapped across chunks. Computation on Dask arrays with small chunks can also be slow, because each operation on a chunk has some fixed overhead from the Python interpreter and the Dask task executor. Conversely, if your chunks are too big, some of your computation may be wasted, because Dask only computes results one chunk at a time. A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements (e.g., a 1000x1000 matrix). With large arrays (10+ GB), the cost of queueing up Dask operations can be noticeable, and you may need even larger chunksizes. .. tip:: Check out the dask documentation on `chunks `_. Optimization Tips ----------------- With analysis pipelines involving both spatial subsetting and temporal resampling, Dask performance can become very slow in certain cases. Here are some optimization tips we have found through experience: 1. Do your spatial and temporal indexing (e.g. ``.sel()`` or ``.isel()``) early in the pipeline, especially before calling ``resample()`` or ``groupby()``. Grouping and resampling triggers some computation on all the blocks, which in theory should commute with indexing, but this optimization hasn't been implemented in Dask yet. (See `Dask issue #746 `_). 2. Save intermediate results to disk as a netCDF files (using ``to_netcdf()``) and then load them again with ``open_dataset()`` for further computations. For example, if subtracting temporal mean from a dataset, save the temporal mean to disk before subtracting. Again, in theory, Dask should be able to do the computation in a streaming fashion, but in practice this is a fail case for the Dask scheduler, because it tries to keep every chunk of an array that it computes in memory. (See `Dask issue #874 `_) 3. Specify smaller chunks across space when using :py:meth:`~xarray.open_mfdataset` (e.g., ``chunks={'latitude': 10, 'longitude': 10}``). This makes spatial subsetting easier, because there's no risk you will load chunks of data referring to different chunks (probably not necessary if you follow suggestion 1). 4. Using the h5netcdf package by passing ``engine='h5netcdf'`` to :py:meth:`~xarray.open_mfdataset` can be quicker than the default ``engine='netcdf4'`` that uses the netCDF4 package. 5. Some dask-specific tips may be found `here `_. 6. The dask `diagnostics `_ can be useful in identifying performance bottlenecks.