Skip to main content

Python

To integrate CodSpeed with your Python codebase, the simplest way is to use our pytest extension: pytest-codspeed. This extension will automatically enable the CodSpeed engine on your benchmarks and allow reporting to CodSpeed.

tip

Creating benchmarks with pytest-codspeed is the same as with the pytest-benchmark API. So if you already have benchmarks written with it, you can start using CodSpeed right away 🚀

Installation​

First, install pytest-codspeed as a development dependency:

poetry add -G dev pytest-codspeed

Usage​

Creating benchmarks​

Marking a whole test function as a benchmark with pytest.mark.benchmark​

import pytest
from statistics import median

@pytest.mark.benchmark
def test_median_performance():
    return median([1, 2, 3, 4, 5])

Benchmarking selected lines of a test function with the benchmark fixture​

import pytest
from statistics import mean

def test_mean_performance(benchmark):
    # Precompute some data useful for the benchmark but that should not be
    # included in the benchmark time
    data = [1, 2, 3, 4, 5]

    # Benchmark the execution of the function
    benchmark(lambda: mean(data))


def test_mean_and_median_performance(benchmark):
    # Precompute some data useful for the benchmark but that should not be
    # included in the benchmark time
    data = [1, 2, 3, 4, 5]

    # Benchmark the execution of the function:
    # The `@benchmark` decorator will automatically call the function and
    # measure its execution
    @benchmark
    def bench():
        mean(data)
        median(data)

Testing the benchmarks locally​

If you want to run the benchmarks tests locally, you can use the --codspeed pytest flag:

info

Running pytest-codspeed locally will not produce any performance reporting. It's only useful for making sure that your benchmarks are working as expected. If you want to get performance reporting, you should run the benchmarks in your CI.

Running the benchmarks in your CI​

To generate performance reports, you need to run the benchmarks in your CI. This allows CodSpeed to detect the CI environment and properly configure the environment.

tip

If you want more details on how to configure the CodSpeed action, you can check out the Continuous Reporting section.

Here is an example of a GitHub Actions workflow that runs the benchmarks and reports the results to CodSpeed on every push to the main branch and every pull request:

.github/workflows/codspeed.yml
name: codspeed-benchmarks

on:
  push:
    branches:
      - "main" # or "master"
  pull_request:
  # `workflow_dispatch` allows CodSpeed to trigger backtest
  # performance analysis in order to generate initial data.
  workflow_dispatch:

jobs:
  benchmarks:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v3
        with:
          python-version: "3.9"

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run benchmarks
        uses: CodSpeedHQ/action@v2
        with:
          token: ${{ secrets.CODSPEED_TOKEN }}
          run: pytest tests/ --codspeed

Recipes​

Running benchmarks in parallel​

If your benchmarks are taking too much time to run under the CodSpeed action, you can run them in parallel to speed up the execution.

pytest-codspeed is compatible with pytest-xdist, a pytest plugin allowing to distribute the execution across multiple processes. Thus, to run your benchmarks in parallel you can simply enable the pytest-xdist plugin on top of pytest-codspeed. This will allow you to run your benchmarks in parallel using multiple processes.

caution

Distributing the execution of the benchmarks only works on a single machine. Distributing across multiple machines is not supported yet.

First, install pytest-xdist as a development dependency:

poetry add -G dev pytest-xdist

Then, you can run your benchmarks in parallel with the pytest-xdist flag:

pytest tests/ --codspeed -n auto

The change in the CI workflow would look like this:

.github/workflows/codspeed.yml
      - name: Run benchmarks
        uses: CodSpeedHQ/action@v2
        with:
          token: ${{ secrets.CODSPEED_TOKEN }}
-          run: pytest tests/ --codspeed
+          run: pytest tests/ --codspeed -n auto

Usage with Nox​

It's possible to use pytest-codspeed with Nox, a Python automation tool that allows you to automate the execution of Python code across multiple environments.

Here is an example configuration file to run benchmarks with pytest-codspeed using Nox:

noxfile.py
import nox

@nox.session
def codspeed(session):
    session.install('pytest')
    session.install('pytest-codspeed')
    session.run('pytest', '--codspeed')

You can then run the benchmarks:

nox --sessions codspeed

To use it with Github Actions, you can use the following workflow:

name: codspeed-benchmarks

on:
  push:
    branches:
      - "main" # or "master"
  pull_request:
  # `workflow_dispatch` allows CodSpeed to trigger backtest
  # performance analysis in order to generate initial data.
  workflow_dispatch:

jobs:
  benchmarks:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v3
        with:
          python-version: "3.12.0"

      - name: Install Nox
        run: pip install nox

      - name: Install dependencies
        run: nox --sessions codspeed --install-only

      - name: Run the action
        uses: CodSpeedHQ/action@v2
        with:
          run: nox --sessions codspeed --reuse-existing-virtualenvs --no-install
          token: ${{ secrets.CODSPEED_TOKEN }}

Splitting the virtualenv installation and the execution of the benchmarks is optional. Though this allows to speed up the execution of the benchmarks since the dependencies will be installed or compiled without the instrumentation enabled.