Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

Better tests and benchmarks with pytest-benchmark

The Unix time command is a versatile tool that can be used to assess the running time of small programs on a variety of platforms. For larger Python applications and libraries, a more comprehensive solution that deals with both testing and benchmarking is pytest, in combination with its pytest-benchmark plugin.

In this section, we will write a simple benchmark for our application using the pytest testing framework. For the interested reader, the pytest documentation, which can be found at, is the best resource to learn more about the framework and its uses.


You can install pytest from the console using the pip install pytest command. The benchmarking plugin can be installed, similarly, by issuing the pip install pytest-benchmark command.

A testing framework is a set of tools that simplifies writing, executing, and debugging tests and provides rich reports and summaries of the test results. When using the pytest framework, it is recommended to place tests separately from the application code. In the following example, we create the file, which contains the test_evolve function:

    from simul import Particle, ParticleSimulator

    def test_evolve():
        particles = [Particle( 0.3,  0.5, +1),
                     Particle( 0.0, -0.5, -1),
                     Particle(-0.1, -0.4, +3)]

        simulator = ParticleSimulator(particles)


        p0, p1, p2 = particles

        def fequal(a, b, eps=1e-5):
            return abs(a - b) < eps

        assert fequal(p0.x, 0.210269)
        assert fequal(p0.y, 0.543863)

        assert fequal(p1.x, -0.099334)
        assert fequal(p1.y, -0.490034)

        assert fequal(p2.x,  0.191358)
        assert fequal(p2.y, -0.365227)

The  pytest executable can be used from the command line to discover and run tests contained in Python modules. To execute a specific test, we can use the pytest path/to/ syntax. To execute test_evolve,  we can type the following command in a console to obtain simple but informative output:

$ pytest

platform linux -- Python 3.5.2, pytest-3.0.5, py-1.4.32, pluggy-0.4.0
rootdir: /home/gabriele/workspace/hiperf/chapter1, inifile: plugins:
collected 2 items .

=========================== 1 passed in 0.43 seconds ===========================

Once we have a test in place, it is possible for you to execute your test as a benchmark using the pytest-benchmark plugin. If we change our test function so that it accepts an argument named benchmark, the pytest framework will automatically pass the benchmark resource as an argument (in pytest terminology, these resources are called fixtures). The benchmark resource can be called by passing the function that we intend to benchmark as the first argument, followed by the additional arguments. In the following snippet, we illustrate the edits necessary to benchmark the ParticleSimulator.evolve function:

    from simul import Particle, ParticleSimulator

    def test_evolve(benchmark):
        # ... previous code
benchmark(simulator.evolve, 0.1)

To run the benchmark, it is sufficient to rerun the pytest command. The resulting output will contain detailed timing information regarding the test_evolve function, as shown:

For each test collected, pytest-benchmark will execute the benchmark function several times and provide a statistic summary of its running time. The output shown earlier is very interesting as it shows how running times vary between runs.

In this example, the benchmark in test_evolve was run 34 times (column Rounds), its timings ranged between 29 and 41 ms (Min and Max), and the Average and Median times were fairly similar at about 30 ms, which is actually very close to the best timing obtained. This example demonstrates how there can be substantial performance variability between runs, and that when taking timings with one-shot tools such as time, it is a good idea to run the program multiple times and record a representative value, such as the minimum or the median.

pytest-benchmark has many more features and options that can be used to take accurate timings and analyze the results. For more information, consult the documentation at