Parallelize code with
In this section we parallelize simple for-loop style code with Dask and
dask.delayed. Often, this is the only function that you will need to convert functions for use with Dask.
This is a simple way to use
dask to parallelize existing codebases or build complex systems. This will also help us to develop an understanding for later sections.
First let’s make some toy functions,
add, that sleep for a while to simulate work. We’ll then time running these functions normally.
In the next section we’ll parallelize this code.
from time import sleep def inc(x): sleep(1) return x + 1 def add(x, y): sleep(1) return x + y
We time the execution of this normal code using the
%%time magic, which is a special function of the Jupyter Notebook.
%%time # This takes three seconds to run because we call each # function sequentially, one after the other x = inc(1) y = inc(2) z = add(x, y)
Parallelize with the
Those two increment calls could be called in parallel, because they are totally independent of one-another.
We’ll transform the
add functions using the
dask.delayed function. When we call the delayed version by passing the arguments, exactly as before, but the original function isn’t actually called yet - which is why the cell execution finishes very quickly. Instead, a delayed object is made, which keeps track of the function to call and the arguments to pass to it.
from dask import delayed
%%time # This runs immediately, all it does is build a graph x = delayed(inc)(1) y = delayed(inc)(2) z = delayed(add)(x, y)
This ran immediately, since nothing has really happened yet.
To get the result, call
compute. Notice that this runs faster than the original code.
%%time # This actually runs our computation using a local thread pool z.compute()
What just happened?¶
z object is a lazy
Delayed object. This object holds everything we need to compute the final result, including references to all of the functions that are required and their inputs and relationship to one-another. We can evaluate the result with
.compute() as above or we can visualize the task graph for this value with
# Look at the task graph for `z` z.visualize()
Notice that this includes the names of the functions from before, and the logical flow of the outputs of the
inc functions to the inputs of
Some questions to consider:¶
Why did we go from 3s to 2s? Why weren’t we able to parallelize down to 1s?
What would have happened if the inc and add functions didn’t include the
sleep(1)? Would Dask still be able to speed up this code?
What if we have multiple outputs or also want to get access to x or y?
Exercise: Parallelize a for loop¶
for loops are one of the most common things that we want to parallelize. Use
sum to parallelize the computation below:
data = [1, 2, 3, 4, 5, 6, 7, 8]
%%time # Sequential code results =  for x in data: y = inc(x) results.append(y) total = sum(results)
%%time # Your parallel code here...
How do the graph visualizations compare with the given solution, compared to a version with the
sum function used directly rather than wrapped with
delay? Can you explain the latter version? You might find the result of the following expression illuminating
delayed(inc)(1) + delayed(inc)(2)
Exercise: Parallelizing a for-loop code with control flow¶
Often we want to delay only some functions, running a few of them immediately. This is especially helpful when those functions are fast and help us to determine what other slower functions we should call. This decision, to delay or not to delay, is usually where we need to be thoughtful when using
In the example below we iterate through a list of inputs. If that input is even then we want to call
inc. If the input is odd then we want to call
is_even decision to call
double has to be made immediately (not lazily) in order for our graph-building Python code to proceed.
def double(x): sleep(1) return 2 * x def is_even(x): return not x % 2 data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
%%time # Sequential code results =  for x in data: if is_even(x): y = double(x) else: y = inc(x) results.append(y) total = sum(results) print(total)
%%time # Your parallel code here... # TODO: parallelize the sequential code above using dask.delayed # You will need to delay some functions, but not all
Some questions to consider:¶
What are other examples of control flow where we can’t use delayed?
What would have happened if we had delayed the evaluation of
is_even(x)in the example above?
What are your thoughts on delaying
sum? This function is both computational but also fast to run.
Exercise: Parallelizing a Pandas Groupby Reduction¶
In this exercise we read several CSV files and perform a groupby operation in parallel. We are given sequential code to do this and parallelize it with
The computation we will parallelize is to compute the mean departure delay per airport from some historical flight data. We will do this by using
dask.delayed together with
pandas. In a future section we will do this same exercise with
First, run this code to prep some data, if you have not already done so.
This downloads and extracts some historical flight data for flights out of NYC between 1990 and 2000. The data is originally from here.
import os sorted(os.listdir(os.path.join('data', 'nycflights')))
Read one file with
pandas.read_csv and compute mean departure delay¶
import pandas as pd df = pd.read_csv(os.path.join('data', 'nycflights', '1990.csv')) df.head()
# What is the schema? df.dtypes
# What originating airports are in the data? df.Origin.unique()
# Mean departure delay per-airport for one year df.groupby('Origin').DepDelay.mean()
Sequential code: Mean Departure Delay Per Airport¶
The above cell computes the mean departure delay per-airport for one year. Here we expand that to all years using a sequential for loop.
from glob import glob filenames = sorted(glob(os.path.join('data', 'nycflights', '*.csv')))
%%time sums =  counts =  for fn in filenames: # Read in file df = pd.read_csv(fn) # Groupby origin airport by_origin = df.groupby('Origin') # Sum of all departure delays by origin total = by_origin.DepDelay.sum() # Number of flights by origin count = by_origin.DepDelay.count() # Save the intermediates sums.append(total) counts.append(count) # Combine intermediates to get total mean-delay-per-origin total_delays = sum(sums) n_flights = sum(counts) mean = total_delays / n_flights
Parallelize the code above¶
dask.delayed to parallelize the code above. Some extra things you will need to know.
Methods and attribute access on delayed objects work automatically, so if you have a delayed object you can perform normal arithmetic, slicing, and method calls on it and it will produce the correct delayed calls.
x = delayed(np.arange)(10) y = (x + 1)[::2].sum() # everything here was delayed
.compute()method works well when you have a single output. When you have multiple outputs you might want to use the
>>> x = delayed(np.arange)(10) >>> y = x ** 2 >>> min_, max_ = compute(y.min(), y.max()) >>> min_, max_ (0, 81)
This way Dask can share the intermediate values (like
y = x**2)
So your goal is to parallelize the code above (which has been copied below) using
dask.delayed. You may also want to visualize a bit of the computation to see if you’re doing it correctly.
from dask import compute
%%time # copied sequential code sums =  counts =  for fn in filenames: # Read in file df = pd.read_csv(fn) # Groupby origin airport by_origin = df.groupby('Origin') # Sum of all departure delays by origin total = by_origin.DepDelay.sum() # Number of flights by origin count = by_origin.DepDelay.count() # Save the intermediates sums.append(total) counts.append(count) # Combine intermediates to get total mean-delay-per-origin total_delays = sum(sums) n_flights = sum(counts) mean = total_delays / n_flights
%%time # your code here
If you load the solution, add
%%time to the top of the cell to measure the running time.
# ensure the results still match mean
Some questions to consider:¶
How much speedup did you get? Is this how much speedup you’d expect?
Experiment with where to call
compute. What happens when you call it on
counts? What happens if you wait and call it on
Experiment with delaying the call to
sum. What does the graph look like if
sumis delayed? What does the graph look like if it isn’t?
Can you think of any reason why you’d want to do the reduction one way over the other?