10. Introduction to Dask#

Attribution: The following notebook is based on the great notebook created by Ryan Abernathey in the Earth and Environmental Data Science book (link)

10.1. What is Dask?#

Dask is a Python library that provides flexible parallel computing and distributed data processing capabilities. It is designed to handle larger-than-memory computations and parallelize tasks across multiple cores and even distributed clusters. Dask’s primary goal is to make it easier for developers to scale their data analysis and processing workflows while maintaining a familiar and Pythonic interface.

Dask is composed of two components: 1) Task Scheduler, and 2) “Big Data” collections. These collections include parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

Dask Overview

Dask overview (source: Dask Documentation)

10.2. Dask Arrays#

A dask array looks and feels a lot like a NumPy array. However, a dask array doesn’t directly hold any data. Instead, it symbolically represents the computations needed to generate the data. Nothing is actually computed until the actual numerical values are needed. This mode of operation is called “lazy”; it allows one to build up complex, large calculations symbolically before turning them over the scheduler for execution.

Dask arrays coordinate many NumPy arrays arranged into a grid. These arrays may live on disk or on other machines.

Dask Array

(source Dask Documentation)

If we want to create a NumPy array of all ones, we do it like this:

import numpy as np
shape = (1000, 4000)
ones_np = np.ones(shape)
ones_np

array([[1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       ...,
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.]])

This array contains ~ 30 MB of data:

ones_np.nbytes / (1024 * 1024)

30.517578125

Now let’s create the same array using dask’s array interface.

import dask.array as da
ones = da.ones(shape)
ones
Array Chunk
Bytes 30.52 MiB 30.52 MiB
Shape (1000, 4000) (1000, 4000)
Dask graph 1 chunks in 1 graph layer
Data type float64 numpy.ndarray
4000 1000

The dask array representation reveals the concept of “chunks”. “Chunks” describes how the array is split into sub-arrays. We did not specify any chunks, so Dask just used one single chunk for the array. This is not much different from a NumPy array at this point.

10.2.1. Specifying Chunks#

However, we could have split up the array into many chunks.

There are several ways to specify chunks. In this lecture, we will use a block shape.

chunk_shape = (1000, 1000)
ones = da.ones(shape, chunks=chunk_shape)
ones
Array Chunk
Bytes 30.52 MiB 7.63 MiB
Shape (1000, 4000) (1000, 1000)
Dask graph 4 chunks in 1 graph layer
Data type float64 numpy.ndarray
4000 1000

Notice that we just see a symbolic represetnation of the array, including its shape, dtype, and chunksize. No data has been generated yet. When we call .compute() on a dask array, the computation is trigger and the dask array becomes a NumPy array.

ones.compute()

array([[1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       ...,
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.]])

In order to understand what happened when we called .compute(), we can visualize the dask graph, the symbolic operations that make up the array

ones.visualize()
../_images/46c67a1a13c228401bda6081b86cd1644fff8697fc79c6716e2ea5e61371504c.png

Our array has four chunks. To generate it, dask calls np.ones four times and then concatenates this together into one array.

Rather than immediately loading a dask array (which puts all the data into RAM), it is more common to want to reduce the data somehow. For example

sum_of_ones = ones.sum()
sum_of_ones.visualize()
../_images/8366a5dc7d832f718ac7b976fa164d4e93c737334abccddc579e6ba92e5c4886.png

Here we see dask’s strategy for finding the sum. This simple example illustrates the beauty of dask: it automatically designs an algorithm appropriate for custom operations with big data.

If we make our operation more complex, the graph gets more complex.

fancy_calculation = (ones * ones[::-1, ::-1]).mean()
fancy_calculation.visualize()
../_images/9c78f16bf061d4f7703c7cf1070e2b40d71cd396da31ae188fc2ba081a0e227b.png

10.2.2. A Bigger Calculation#

The examples above were toy examples; the data (32 MB) is nowhere nearly big enough to warrant the use of dask.

We can make it a lot bigger!

bigshape = (200000, 4000)
big_ones = da.ones(bigshape, chunks=chunk_shape)
big_ones
Array Chunk
Bytes 5.96 GiB 7.63 MiB
Shape (200000, 4000) (1000, 1000)
Dask graph 800 chunks in 1 graph layer
Data type float64 numpy.ndarray
4000 200000 
big_ones.nbytes / (1024 * 1024)

6103.515625

This dataset is 3.2 GB, rather MB! This is probably close to or greater than the amount of available RAM than you have in your computer. Nevertheless, dask has no problem working on it.

Do not try to .visualize() this array!

When doing a big calculation, dask also has some tools to help us understand what is happening under the hood

from dask.diagnostics import ProgressBar

big_calc = (big_ones * big_ones[::-1, ::-1]).mean()

with ProgressBar():
    result = big_calc.compute()
result

[########################################] | 100% Completed | 1.95 sms

1.0

10.2.3. Reduction#

All the usual NumPy methods work on dask arrays (Check this section on Dask Documentation to learn what NumPy methods work in Dask which ones don’t). You can also apply NumPy function directly to a dask array, and it will stay lazy.

big_ones_reduce = (np.cos(big_ones)**2).mean(axis=0)
big_ones_reduce
Array Chunk
Bytes 31.25 kiB 7.81 kiB
Shape (4000,) (1000,)
Dask graph 4 chunks in 8 graph layers
Data type float64 numpy.ndarray
4000 1

Plotting also triggers computation, since we need the actual values

from matplotlib import pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (12,8)

plt.plot(big_ones_reduce)

[<matplotlib.lines.Line2D at 0x7f1d0d81fb10>]
../_images/14d961edcf8f2c1114f9a545d309a67d476a46ea9dc8cf9572efd76840132d24.png

10.3. Distributed Clusters#

Once we are ready to make a bigger calculation with dask, we can use a Dask Distributed cluster.

Warning

A common mistake is to move to distributed mode too soon. For smaller data, distributed will actually be much slower than the default multi-threaded scheduler or not using Dask at all. You should only use distributed when your data is much larger than what your computer can handle in memory.

10.3.1. Local Cluster#

A local cluster uses all the CPU cores of the machine it is running on. For our cloud-based Jupyterlab environments, that is just 2 cores–not very much. However, it’s good to know about.

from dask.distributed import Client, LocalCluster
cluster = LocalCluster()
client = Client(cluster)
client

Client

Client-a3b77291-5ee9-11ee-8693-6e1c11398774

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: /user/halemohammad@clarku.edu/proxy/8787/status

Cluster Info

Note that the “Dashboard” link will open a new page where you can monitor a computation’s progress.

big_calc.compute()

1.0

Here is another bigger calculation.

random_values = da.random.normal(size=(2e8,), chunks=(1e6,))
hist, bins = da.histogram(random_values, bins=100, range=[-5, 5]) 
hist
Array Chunk
Bytes 800 B 800 B
Shape (100,) (100,)
Dask graph 1 chunks in 7 graph layers
Data type int64 numpy.ndarray
100 1 
# actually trigger the computation
hist_c = hist.compute()

# plot results
x = 0.5 * (bins[1:] + bins[:-1])
width = np.diff(bins)
plt.bar(x, hist_c, width);
../_images/9737005a0da81ca204d668cdfc7229e853a8024afea565902b169d557136fc53.png