Anaconda Enterprise: Documentation

# API

`delayed` | Wraps a function or object to produce a `Delayed`.

`dask.delayed.``delayed`()

  Wraps a function or object to produce a `Delayed`.

  `Delayed` objects act as proxies for the object they wrap, but all operations on them are done lazily by building up a dask graph internally.

  Parameters: | obj (object) – The function or object to wrap name (string or hashable, optional) – The key to use in the underlying graph for the wrapped object. Defaults to hashing content. pure (bool, optional) – Indicates whether calling the resulting `Delayed` object is a pure operation. If True, arguments to the call are hashed to produce deterministic keys. If not provided, the default is to check the global `delayed_pure` setting, and fallback to `False` if unset. nout (int, optional) – The number of outputs returned from calling the resulting `Delayed` object. If provided, the `Delayed` output of the call can be iterated into `nout` objects, allowing for unpacking of results. By default iteration over `Delayed` objects will error. Note, that `nout=1` expects `obj`, to return a tuple of length 1, and consequently for nout=0`, `obj` should return an empty tuple. traverse (bool, optional) – By default dask traverses builtin python collections looking for dask objects passed to `delayed`. For large collections this can be expensive. If `obj` doesn’t contain any dask objects, set `traverse=False` to avoid doing this traversal.

  Examples

  Apply to functions to delay execution:

      >>> def inc(x):
      ...     return x + 1

      >>> inc(10)
      11

      >>> x = delayed(inc, pure=True)(10)
      >>> type(x) == Delayed
      True
      >>> x.compute()
      11

  Can be used as a decorator:

      >>> @delayed(pure=True)
      ... def add(a, b):
      ...     return a + b
      >>> add(1, 2).compute()
      3

  `delayed` also accepts an optional keyword `pure`. If False, then subsequent calls will always produce a different `Delayed`. This is useful for non-pure functions (such as `time` or `random`).

      >>> from random import random
      >>> out1 = delayed(random, pure=False)()
      >>> out2 = delayed(random, pure=False)()
      >>> out1.key == out2.key
      False

  If you know a function is pure (output only depends on the input, with no global state), then you can set `pure=True`. This will attempt to apply a consistent name to the output, but will fallback on the same behavior of `pure=False` if this fails.

      >>> @delayed(pure=True)
      ... def add(a, b):
      ...     return a + b
      >>> out1 = add(1, 2)
      >>> out2 = add(1, 2)
      >>> out1.key == out2.key
      True

  Instead of setting `pure` as a property of the callable, you can also set it contextually using the `delayed_pure` setting. Note that this influences the call and not the creation of the callable:

      >>> import dask
      >>> @delayed
      ... def mul(a, b):
      ...     return a * b
      >>> with dask.set_options(delayed_pure=True):
      ...     print(mul(1, 2).key == mul(1, 2).key)
      True
      >>> with dask.set_options(delayed_pure=False):
      ...     print(mul(1, 2).key == mul(1, 2).key)
      False

  The key name of the result of calling a delayed object is determined by hashing the arguments by default. To explicitly set the name, you can use the `dask_key_name` keyword when calling the function:

      >>> add(1, 2)
      Delayed('add-3dce7c56edd1ac2614add714086e950f')
      >>> add(1, 2, dask_key_name='three')
      Delayed('three')

  Note that objects with the same key name are assumed to have the same result. If you set the names explicitly you should make sure your key names are different for different results.

      >>> add(1, 2, dask_key_name='three')
      >>> add(2, 1, dask_key_name='three')
      >>> add(2, 2, dask_key_name='four')

  `delayed` can also be applied to objects to make operations on them lazy:

      >>> a = delayed([1, 2, 3])
      >>> isinstance(a, Delayed)
      True
      >>> a.compute()
      [1, 2, 3]

  The key name of a delayed object is hashed by default if `pure=True` or is generated randomly if `pure=False` (default). To explicitly set the name, you can use the `name` keyword:

      >>> a = delayed([1, 2, 3], name='mylist')
      >>> a
      Delayed('mylist')

  Delayed results act as a proxy to the underlying object. Many operators are supported:

      >>> (a + [1, 2]).compute()
      [1, 2, 3, 1, 2]
      >>> a[1].compute()
      2

  Method and attribute access also works:

      >>> a.count(2).compute()
      1

  Note that if a method doesn’t exist, no error will be thrown until runtime:

      >>> res = a.not_a_real_method()
      >>> res.compute()
      AttributeError("'list' object has no attribute 'not_a_real_method'")

  “Magic” methods (e.g. operators and attribute access) are assumed to be pure, meaning that subsequent calls must return the same results. This is not overrideable. To invoke an impure attribute or operator, you’d need to use it in a delayed function with `pure=False`.

      >>> class Incrementer(object):
      ...     def __init__(self):
      ...         self._n = 0
      ...     @property
      ...     def n(self):
      ...         self._n += 1
      ...         return self._n
      ...
      >>> x = delayed(Incrementer())
      >>> x.n.key == x.n.key
      True
      >>> get_n = delayed(lambda x: x.n, pure=False)
      >>> get_n(x).key == get_n(x).key
      False

  In contrast, methods are assumed to be impure by default, meaning that subsequent calls may return different results. To assume purity, set pure=True. This allows sharing of any intermediate values.

      >>> a.count(2, pure=True).key == a.count(2, pure=True).key
      True

  As with function calls, method calls also respect the global `delayed_pure` setting and support the `dask_key_name` keyword:

      >>> a.count(2, dask_key_name="count_2")
      Delayed('count_2')
      >>> with dask.set_options(delayed_pure=True):
      ...     print(a.count(2).key == a.count(2).key)
      True