While not mandatory for most pull requests, we ask that performance related PRs include a benchmark in order to clearly depict the use-case that is being optimized for.

In this section we will review how to setup the benchmarks, and three commands asv dev, asv run and asv continuous.


Begin by installing airspeed velocity in your development environment. Prior to installation, be sure to activate your development environment, then if using venv you may install the requirement with:

source napari-dev/bin/activate
pip install asv

If you are using conda, then the command:

conda activate napari-dev
conda install asv

is more appropriate. Once installed, it is useful to run the command:

asv machine

To let airspeed velocity know more information about your machine.

Writing a benchmark

To write benchmark, add a file in the napari/_benchmarks directory which contains a class with one setup method and at least one method prefixed with time_.

The time_ method should only contain code you wish to benchmark. Therefore it is useful to move everything that prepares the benchmark scenario into the setup method. This function is called before calling a time_ method and its execution time is not factored into the benchmarks.

Take for example the ViewImageSuite benchmark:

import numpy as np
import napari
from qtpy.QtWidgets import QApplication

class ViewImageSuite:
    """Benchmarks for viewing images in the viewer."""

    def setup(self):
        app = QApplication.instance() or QApplication([])
        np.random.seed(0) = np.random.random((512, 512))
        self.viewer = None

    def teardown(self):

    def time_view_image(self):
        """Time to view an image."""
        self.viewer = napari.view_image(

Here, the creation of the image is completed in the setup method, and not included in the reported time of the benchmark.

It is also possible to benchmark features such as peak memory usage. To learn more about the features of asv, please refer to the official airspeed velocity documentation.

Testing the benchmarks locally

Prior to running the true benchmark, it is often worthwhile to test that the code is free of typos. To do so, you may use the command:

asv dev -b ViewImageSuite

Where the ViewImageSuite above will be run once in your current environment to test that everything is in order.

Running your benchmark

The command above is fast, but doesn’t test the performance of the code adequately. To do that you may want to run the benchmark in your current environment to see the performance of your change as you are developing new features. The command asv run -E existing will specify that you wish to run the benchmark in your existing environment. This will save a significant amount of time since building napari can be a time consuming task:

asv run -E existing -b ViewImageSuite

Comparing results to main

Often, the goal of a PR is to compare the results of the modifications in terms of speed to a snapshot of the code that is in the main branch of the napari repository. The command asv continuous is of help here:

asv continuous main your-current-branch -b ViewImageSuite

This call will build out the environments specified in the asv.conf.json file and compare the performance of the benchmark between your current commit and the code in the main branch.

The output may look something like:

$ asv continuous main your-current-branch -b ViewImageSuite
· Creating environments
· Discovering benchmarks
·· Uninstalling from conda-py3.7-cython-numpy1.15-scipy
·· Installing 544c0fe3 <benchmark_docs> into conda-py3.7-cython-numpy1.15-scipy.
· Running 4 total benchmarks (2 commits * 2 environments * 1 benchmarks)
[  0.00%] · For napari commit 37c764cb <benchmark_docs~1> (round 1/2):
[100.00%] ··· ...Image.ViewImageSuite.time_view_image           33.2±2ms


In this case, the differences between HEAD on your-current-branch and main are not significant enough for airspeed velocity to report.


The airspeed velocity tool also supports code profiling using cProfile. For detailed instructions on how to use the profiling functionality see the asv profiling documentation.

To profile a particular benchmark in napari you can run

asv profile benchmark_qt_viewer.QtViewerSuite.time_create_viewer -g snakeviz --python=same

where benchmark_qt_viewer is the file name, QtViewerSuite is the test suite class name, and time_create_viewer is the test method.

To profile a particular parameterized benchmark you can run

asv profile "benchmark_image_layer.Image2DSuite.time_create_layer\(512\)" -g snakeviz --python=same

where benchmark_image_layer is the file name, Image2DSuite is the test suite class name, and time_to_create_layer is the test method and 512 is a valid parameter input to the test method.

Note that we in both these cases we have sent the output of the profiling to snakeviz which you can pip install with

pip install snakeviz

and we use --python=same to profile against our current python environment.