Sometimes problems don’t fit into one of the collections like
dask.dataframe. In these cases, users can parallelize custom algorithms
using the simpler
dask.delayed interface. This allows one to create graphs
directly with a light annotation of normal python code.
>>> x = dask.delayed(inc)(1) >>> y = dask.delayed(inc)(2) >>> z = dask.delayed(add)(x, y) >>> z.compute() 7 >>> z.vizualize()
Sometimes we face problems that are parallelizable, but don’t fit high-level abstractions Dask array or Dask dataframe. Consider the following example:
def inc(x): return x + 1 def double(x): return x + 2 def add(x, y): return x + y data = [1, 2, 3, 4, 5] output =  for x in data: a = inc(x) b = double(x) c = add(a, b) output.append(c) total = sum(output)
There is clearly parallelism in this problem (many of the
add functions can evaluate independently), but it’s not
clear how to convert this to a big array or big dataframe computation.
As written this code runs sequentially in a single thread. However we see that a lot of this could be executed in parallel.
delayed function decorates your functions so that they operate
lazily. Rather than executing your function immediately it will defer
execution, placing the function and its arguments into a task graph.
||Wraps a function or object to produce a
We slightly modify our code our code by wrapping functions in
This delays the execution of the function and generates a dask graph instead.
import dask output =  for x in data: a = dask.delayed(inc)(x) b = dask.delayed(double)(x) c = dask.delayed(add)(a, b) output.append(c) total = dask.delayed(sum)(output)
We used the
dask.delayed function to wrap the function calls that we want
to turn into tasks. None of the
have happened yet, instead the object
total is a
Delayed result that
contains a task graph of the entire computation. Looking at the graph we see
clear opportunities for parallel execution. The dask schedulers will exploit
this parallelism, generally improving performance. (although not in this
example, because these functions are already very small and fast.)
total.visualize() # see image to the right
We can now compute this lazy result to execute the graph in parallel:
>>> total.compute() 45
It is also common to see the delayed function used as a decorator. Here is a reproduction of our original problem as a parallel code.
import dask @dask.delayed def inc(x): return x + 1 @dask.delayed def double(x): return x + 2 @dask.delayed def add(x, y): return x + y data = [1, 2, 3, 4, 5] output =  for x in data: a = inc(x) b = double(x) c = add(a, b) output.append(c) total = dask.delayed(sum)(output)