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.
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
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:
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:
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
To run any of the tests with coverage, use the following:
To report on a subset of the Bokeh package, pass e.g. -cov=bokeh/models.
To run any of the tests without standard output captured use:
See the pytest documentation for further information on pytest and
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
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.
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 maintain the basic functionality of the Python portion of
the Bokeh library. A few general guidelines will help you write Python unit
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
import bokeh.models.transforms as bmt
GOOD: from bokeh.embed import components
from bokeh.embed import components
BAD: from ..document import Document
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.
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.
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.
Every push to the master branch or any Pull Request branch on GitHub
automatically triggers a full test build on the GithubCI continuous
You can see the list of all current and previous builds at this URL:
There are a number of files that affect the build configuration:
Instructions for building a conda noarch package for Bokeh. This
file is the single source of truth for build and test (but not
Used to build sdist packages and “dev” installs. This file is also
the single source of truth for runtime dependencies.
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.