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Showing content from https://github.com/jupyterlab/benchmarks/ below:

jupyterlab/benchmarks: Benchmarking tools for JupyterLab

This repository is a place to make JupyterLab fast.

It tracks with benchmarks tooling the performance evolution of JupyterLab. Read more on the documentation website.

The best way to use this project for benchmark test execution is to start a manual benchmark workflow in the repository actions for performance or memory-leaks.

The performance tests will measure the execution of the following scenario:

• Opening the test notebook
• Switching from the test notebook to a copy of it
• Switching back
• Switching from the test notebook to a text editor
• Switching back
• Search for a word
• Start the debugger
• Closing the test notebook

There are multiple test notebooks available and their size can be tune with a size parameter.

Those cases will be run on the provided challenger repo/branch and in the reference JupyterLab repo at a given branch. Then it will produce a report that can be downloaded as artifacts when done.

The workflow parameters are:

• JupyterLab Git repo [required]: fork name in format {owner}/{repo}
• Git repository reference [required]: typically branch name of a PR
• Reference branch [default: master]: Branch on jupyterlab/jupyterlab to use a reference
• Number of samples [default: 100]: Number of experiments to run to build the statistical distribution of execution time
• Test notebooks to use [default: ["codeNotebook", "mdNotebook", "longOutput", "errorOutputs"]]: The test notebooks to execute; the available test notebooks are: ["codeNotebook", "mdNotebook", "largePlotly", "longOutput", "manyPlotly", "manyOutputs", "errorOutputs"]
• Test files size [default: 100]: tests notebooks are parametrized with an integer that is proportional to their size.

You need to remember that a GitHub job is limited to 6 hours. This means you may need to either reduce the number of samples (be careful) or the list of test notebooks to fit that time span.

The following scenarios are tested for memory leaks:

• notebook: Create a new notebook and delete it.
• file-editor: Create a new text file.
• cell:
  • Add a cell (for all 3 types)
  • Move a cell by drag-and-drop (for all 3 types)

The workflow parameters are:

• JupyterLab Git repository [required]: fork name in format {owner}/{repo}
• Git repository branch [required]: typically branch name of a PR
• Number of samples [default: 7]: Number of experiments to run to detect memory leaks (prefer a prime number).

JupyterLab uses a shared copyright model that enables all contributors to maintain the copyright on their contributions. All code is licensed under the terms of the revised BSD license.

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