Deferring computation with delayed is a technique in Dask that allows you to delay computation on individual functions or method calls, instead of on entire computation graphs.
Here's an example of how you can use delayed to defer computation in Dask:
import dask# Define a function to double a numberdef double(x): return x * 2# Define a function to square a numberdef square(x): return x ** 2# Define a function to add two numbersdef add(x, y): return x + y# Delay computation on individual functions using dask.delayedx = dask.delayed(double)(5)y = dask.delayed(square)(x)z = dask.delayed(add)(x, y)# Compute the result by calling the compute method on the final resultresult = z.compute()# Print the resultprint(result) |
In this example, we first define three simple functions: double, square, and add. We then use the dask.delayed function to delay computation on these functions when they are called.
The dask.delayed function creates a Delayed object that represents the delayed computation. When called, the Delayed object returns a dask.Future object that represents the eventual result of the computation. The Delayed object itself is not computed until the compute method is called on the final result.
In this example, we create three Delayed objects by calling dask.delayed on the double, square, and add functions with appropriate arguments. We then use these objects to create a computation graph that computes z = x + y, where x is the result of calling double on 5 and y is the result of calling square on x.
Finally, we compute the final result by calling the compute method on the z object. This triggers the computation of the entire computation graph and returns the final result.
By deferring computation with delayed, we can create complex computation graphs that include individual functions or method calls, allowing us to build more flexible and modular data processing pipelines.