Function bind

dask/graph_manipulation.py:210–312

Make ``children`` collection(s), optionally omitting sub-collections, dependent on ``parents`` collection(s). Two examples follow. The first example creates an array ``b2`` whose computation first computes an array ``a`` completely and then computes ``b`` completely, recomputing ``

(
    children: T,
    parents,
    *,
    omit=None,
    seed: Hashable | None = None,
    assume_layers: bool = True,
    split_every: float | Literal[False] | None = None,
)

def bind(
    children: T,
    parents,
    *,
    omit=None,
    seed: Hashable | None = None,
    assume_layers: bool = True,
    split_every: float | Literal[False] | None = None,
) -> T:
    """
    Make ``children`` collection(s), optionally omitting sub-collections, dependent on
    ``parents`` collection(s). Two examples follow.

    The first example creates an array ``b2`` whose computation first computes an array
    ``a`` completely and then computes ``b`` completely, recomputing ``a`` in the
    process:

    >>> import dask
    >>> import dask.array as da
    >>> a = da.ones(4, chunks=2)
    >>> b = a + 1
    >>> b2 = bind(b, a)
    >>> len(b2.dask)
    9
    >>> b2.compute()
    array([2., 2., 2., 2.])

    The second example creates arrays ``b3`` and ``c3``, whose computation first
    computes an array ``a`` and then computes the additions, this time not
    recomputing ``a`` in the process:

    >>> c = a + 2
    >>> b3, c3 = bind((b, c), a, omit=a)
    >>> len(b3.dask), len(c3.dask)
    (7, 7)
    >>> dask.compute(b3, c3)
    (array([2., 2., 2., 2.]), array([3., 3., 3., 3.]))

    Parameters
    ----------
    children
        Dask collection or nested structure of Dask collections
    parents
        Dask collection or nested structure of Dask collections
    omit
        Dask collection or nested structure of Dask collections
    seed
        Hashable used to seed the key regeneration. Omit to default to a random number
        that will produce different keys at every call.
    assume_layers
        True
            Use a fast algorithm that works at layer level, which assumes that all
            collections in ``children`` and ``omit``

            #. use :class:`~dask.highlevelgraph.HighLevelGraph`,
            #. define the ``__dask_layers__()`` method, and
            #. never had their graphs squashed and rebuilt between the creation of the
               ``omit`` collections and the ``children`` collections; in other words if