API¶

Dask APIs generally follow from upstream APIs:

The Dask Array API follows the NumPy API
The Dask DataFrame API follows the Pandas API
The Dask-ML API follows the Scikit-Learn API and other related machine learning libraries
The Dask Bag API follows the map/filter/groupby/reduce API common in PySpark, PyToolz, and the Python standard library
The Dask Delayed API wraps general Python code
The Real-time Futures API follows the concurrent.futures API from the standard library.

Additionally, Dask has its own functions to start computations, persist data in memory, check progress, and so forth that complement the APIs above. These more general Dask functions are described below:

`compute`(args, *kwargs)	Compute several dask collections at once.
`is_dask_collection`(x)	Returns `True` if `x` is a dask collection
`optimize`(args, *kwargs)	Optimize several dask collections at once.
`persist`(args, *kwargs)	Persist multiple Dask collections into memory
`visualize`(args, *kwargs)	Visualize several dask graphs at once.

These functions work with any scheduler. More advanced operations are available when using the newer scheduler and starting a dask.distributed.Client (which, despite its name, runs nicely on a single machine). This API provides the ability to submit, cancel, and track work asynchronously, and includes many functions for complex inter-task workflows. These are not necessary for normal operation, but can be useful for real-time or advanced operation.

This more advanced API is available in the Dask distributed documentation

dask.compute(*args, **kwargs)¶

Compute several dask collections at once.

Parameters:

args : object: Any number of objects. If it is a dask object, it’s computed and the result is returned. By default, python builtin collections are also traversed to look for dask objects (for more information see the traverse keyword). Non-dask arguments are passed through unchanged.
traverse : bool, optional: By default dask traverses builtin python collections looking for dask objects passed to compute. For large collections this can be expensive. If none of the arguments contain any dask objects, set traverse=False to avoid doing this traversal.
scheduler : string, optional: Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.
optimize_graph : bool, optional: If True [default], the optimizations for each collection are applied before computation. Otherwise the graph is run as is. This can be useful for debugging.
kwargs: Extra keywords to forward to the scheduler function.

Examples

>>> import dask.array as da
>>> a = da.arange(10, chunks=2).sum()
>>> b = da.arange(10, chunks=2).mean()
>>> compute(a, b)
(45, 4.5)

By default, dask objects inside python collections will also be computed:

>>> compute({'a': a, 'b': b, 'c': 1})  # doctest: +SKIP
({'a': 45, 'b': 4.5, 'c': 1},)

dask.is_dask_collection(x)¶: Returns True if x is a dask collection

dask.optimize(*args, **kwargs)¶

Optimize several dask collections at once.

Returns equivalent dask collections that all share the same merged and optimized underlying graph. This can be useful if converting multiple collections to delayed objects, or to manually apply the optimizations at strategic points.

Note that in most cases you shouldn’t need to call this method directly.

Parameters:

*args : objects: Any number of objects. If a dask object, its graph is optimized and merged with all those of all other dask objects before returning an equivalent dask collection. Non-dask arguments are passed through unchanged.
traverse : bool, optional: By default dask traverses builtin python collections looking for dask objects passed to optimize. For large collections this can be expensive. If none of the arguments contain any dask objects, set traverse=False to avoid doing this traversal.
optimizations : list of callables, optional: Additional optimization passes to perform.
**kwargs: Extra keyword arguments to forward to the optimization passes.

Examples

>>> import dask.array as da
>>> a = da.arange(10, chunks=2).sum()
>>> b = da.arange(10, chunks=2).mean()
>>> a2, b2 = optimize(a, b)

>>> a2.compute() == a.compute()
True
>>> b2.compute() == b.compute()
True

dask.persist(*args, **kwargs)¶

Persist multiple Dask collections into memory

This turns lazy Dask collections into Dask collections with the same metadata, but now with their results fully computed or actively computing in the background.

For example a lazy dask.array built up from many lazy calls will now be a dask.array of the same shape, dtype, chunks, etc., but now with all of those previously lazy tasks either computed in memory as many small numpy.array (in the single-machine case) or asynchronously running in the background on a cluster (in the distributed case).

This function operates differently if a dask.distributed.Client exists and is connected to a distributed scheduler. In this case this function will return as soon as the task graph has been submitted to the cluster, but before the computations have completed. Computations will continue asynchronously in the background. When using this function with the single machine scheduler it blocks until the computations have finished.

When using Dask on a single machine you should ensure that the dataset fits entirely within memory.

Parameters:

*args: Dask collections
scheduler : string, optional: Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.
traverse : bool, optional: By default dask traverses builtin python collections looking for dask objects passed to persist. For large collections this can be expensive. If none of the arguments contain any dask objects, set traverse=False to avoid doing this traversal.
optimize_graph : bool, optional: If True [default], the graph is optimized before computation. Otherwise the graph is run as is. This can be useful for debugging.
**kwargs: Extra keywords to forward to the scheduler function.

Returns:

New dask collections backed by in-memory data

Examples

>>> df = dd.read_csv('/path/to/*.csv')  # doctest: +SKIP
>>> df = df[df.name == 'Alice']  # doctest: +SKIP
>>> df['in-debt'] = df.balance < 0  # doctest: +SKIP
>>> df = df.persist()  # triggers computation  # doctest: +SKIP

>>> df.value().min()  # future computations are now fast  # doctest: +SKIP
-10
>>> df.value().max()  # doctest: +SKIP
100

>>> from dask import persist  # use persist function on multiple collections
>>> a, b = persist(a, b)  # doctest: +SKIP

dask.visualize(*args, **kwargs)¶

Visualize several dask graphs at once.

Requires graphviz to be installed. All options that are not the dask graph(s) should be passed as keyword arguments.

Parameters:

dsk : dict(s) or collection(s): The dask graph(s) to visualize.
filename : str or None, optional: The name (without an extension) of the file to write to disk. If filename is None, no file will be written, and we communicate with dot using only pipes.
format : {‘png’, ‘pdf’, ‘dot’, ‘svg’, ‘jpeg’, ‘jpg’}, optional: Format in which to write output file. Default is ‘png’.
optimize_graph : bool, optional: If True, the graph is optimized before rendering. Otherwise, the graph is displayed as is. Default is False.
color: {None, ‘order’}, optional: Options to color nodes. Provide cmap= keyword for additional colormap
**kwargs: Additional keyword arguments to forward to to_graphviz.

Returns:

result : IPython.diplay.Image, IPython.display.SVG, or None: See dask.dot.dot_graph for more information.