Bokeh is a large, multi-language project, and relies on varied and extensive tests and testing tools in order to maintain capability and prevent regressions. This chapter describes how to run various tests locally in a development environment, guidelines for writing tests, and information regarding the continuous testing infrastructure.

Running Tests Locally

Before attempting to run Bokeh tests, make sure you have successfully run through all of the instructions in the Getting Set Up section of the Developer’s Guide.

Test Selection

Additionally, on some platforms you may need to increase the maximum number of open file descriptors as some tests open many files to test the server.

ulimit -n 1024

To run all the basic python unit tests, run the following command at the top level of the repository:

pytest tests/unit

Note that this includes unit tests that require Selenium as well as appropriate web drivers (e.g. chromedriver and geckodriver) to be installed. To exclude those unit tests, you can run the command:

pytest -m "not selenium" tests/unit

To run just the BokehJS unit tests, execute:

pytest tests/test_bokehjs.py

Alternatively, you can also navigate to the bokehjs subdirectory of the source checkout and execute:

node make test

You can run all available tests (python and JS unit tests, as well as example and integration tests) from the top-level directory by executing:


To learn more about marking test functions and selecting/deselecting them for a run, please consult the pytest documentation for custom markers. The list of currently defined test markers is below:

  • sampledata: a test for requiring bokeh.sampledata be downloaded

  • selenium: a test requiring selenium

Code Coverage

To run any of the tests with coverage, use the following:

pytest --cov=bokeh

To report on a subset of the Bokeh package, pass e.g. -cov=bokeh/models.

Other Options

To run any of the tests without standard output captured use:

pytest -s

See the pytest documentation for further information on pytest and its options.

Examples Tests

The examples tests run a selection of the Bokeh examples and generate images to compare against previous releases. A report is generated that displays the current and previous images, as well as any image difference.


The tests do not currently fail if the images are different, the test report must be inspected manually.

To run just the examples tests, run the command:

pytest --report-path=examples.html test_examples.py

After the tests have run, you will be able to see the test report at examples.html. Running locally, you can name the test report whatever you want.

The examples tests can run slowly, to speed them up, you can parallelize them:

pytest --report-path=examples.html -n 5 test_examples.py

Where n is the number of cores you want to use.

In addition, the examples tests generate a log file, examples.log which you can view at examples.log in the same directory that you the tests were run from.

Integration Tests

Writing Tests

In order to help keep Bokeh maintainable, all Pull Requests that touch code should normally be accompanied by relevant tests. While exceptions may be made for specific circumstances, the default assumption should be that a Pull Request without tests may not be merged.

Python Unit Tests

Python unit tests maintain the basic functionality of the Python portion of the Bokeh library. A few general guidelines will help you write Python unit tests:

absolute imports

In order to ensure that Bokeh’s unit tests as relocatable and unambiguous as possible, always prefer absolute imports in test files. When convenient, import and use the entire module under test:

  • GOOD: import bokeh.models.transforms as bmt

  • GOOD: from bokeh.embed import components

  • BAD: from ..document import Document


All new tests should use and assume pytest for test running, fixtures, parameterized testing, etc. New tests should not use the unittest module of the Python standard library.

JavaScript Unit Tests

These tests maintain the functionality of the BokehJS portion of the Bokeh project. The BokehJS tests are located in bokehjs/test. They are written using Chai “expect” style. If new test files are added, an appropriate entry in the directory index file should be added.

Integration Tests

To add a new screenshot integration test, first make sure you can run existing screenshot tests, for example tests/integration/annotations/test_whisker.py. New screenshot tests should follow these general guidelines:

  • Be as simple as possible (only include things under test and nothing extra)

  • Prefer the bokeh.models API

Once a new test is written, a base image for comparison is needed. To create a new base image, add --set-new-base-screenshot to your the standard pytest command to run the test. This will generate an image with the name base__<name_of_your_test>.png in the appropriate directory. Use git to check this image into the repository, and then all future screenshot tests will be compared against this base image.

Continuous Integration

Every push to the master branch or any Pull Request branch on GitHub automatically triggers a full test build on the GithubCI continuous integration service.

You can see the list of all current and previous builds at this URL: https://github.com/bokeh/bokeh/actions


There are a number of files that affect the build configuration:

  • conda.recipe/meta.yaml

    Instructions for building a conda noarch package for Bokeh. This file is the single source of truth for build and test (but not runtime) dependencies.

  • setup.py

    Used to build sdist packages and “dev” installs. This file is also the single source of truth for runtime dependencies.

  • setup.cfg

    Contains some global configuration for build and test tools such as versioneer and pytest.


CI services provide finite free build workers to Open Source projects. Grouping commits into meaningful chunks of work before pushing into GitHub (i.e. not pushing on every commit) will help you be considerate of others needing these limited resources.