Create GitHub Actions Workflow To Run All Benchmarks Across Languages

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Introduction

In the world of software development, benchmarking is a crucial step in ensuring the performance and efficiency of our code. With the rise of GitHub Actions, it has become easier to automate and integrate benchmarking into our CI/CD pipelines. In this article, we will explore how to create a GitHub Actions workflow to run all benchmarks across languages, including Python, Julia, and MATLAB.

Setting Up the Workflow

To get started, we need to create a new file in the .github/workflows directory called benchmark.yml. This file will contain the configuration for our workflow.

name: Benchmark

on:
  push:
    branches:
      - main

jobs:
  benchmark:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        language: [python, julia, matlab]

In this example, we define a job called benchmark that runs on an ubuntu-latest environment. We also define a matrix strategy that includes three languages: Python, Julia, and MATLAB.

Implementing Setup Steps for Each Language

Now that we have our matrix strategy set up, let's implement the setup steps for each language.

Python

For Python, we need to set up a conda environment, install the required dependencies, and run the pytest --benchmark-only command.

- name: Setup Python environment
  uses: actions/setup-python@v2
  with:
    python-version: '3.9'

- name: Install dependencies
  run: |
    conda create --name benchmark python=3.9
    conda activate benchmark
    pip install pytest

- name: Run benchmarks
  run: |
    pytest --benchmark-only

Julia

For Julia, we can use the julia/setup-julia action to set up the environment and run the julia --project benches/... command.

- name: Setup Julia environment
  uses: julia/setup-julia@v1
  with:
    julia-version: '1.7'

- name: Run benchmarks
  run: |
    julia --project benches/

MATLAB

For MATLAB, we have two options: we can either install the MATLAB Runtime or use a preconfigured runner. In this example, we will use the preconfigured runner.

- name: Run MATLAB benchmarks
  uses: actions/checkout@v2
  with:
    token: ${{ secrets.MATLAB_TOKEN }}
  run: |
    python harness.py

Uploading Artifacts

Finally, we need to upload the results as workflow artifacts. We can use the actions/upload-artifact action to achieve this.

- name: Upload artifacts
  uses: actions/upload-artifact@v2
  with:
    name: results
    path: results/*.json
    token: ${{ secrets.GITHUB_TOKEN }}

Conclusion

In this article, we have explored how to create a GitHub Actions workflow to run all benchmarks across languages, including Python, Julia, and MATLAB. We have implemented the setup steps for each language and uploaded the results as workflow artifacts. With this workflow, we can that our code is performing optimally and efficiently across different languages.

Acceptance Criteria

To ensure that our workflow is working correctly, we need to meet the following acceptance criteria:

  • All matrix cells complete successfully on CI
  • Artifacts tab shows one JSON/CSV per language & function

Future Work

In the future, we can improve this workflow by adding more languages, implementing more advanced benchmarking techniques, and integrating with other tools and services.

Example Use Cases

This workflow can be used in a variety of scenarios, such as:

  • Benchmarking machine learning models across different languages
  • Comparing the performance of different algorithms and data structures
  • Identifying performance bottlenecks in code

Troubleshooting

If you encounter any issues with this workflow, you can try the following:

  • Check the GitHub Actions logs for errors
  • Verify that the matrix strategy is correctly set up
  • Ensure that the required dependencies are installed
  • Consult the GitHub Actions documentation for more information

References

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Introduction

In our previous article, we explored how to create a GitHub Actions workflow to run all benchmarks across languages, including Python, Julia, and MATLAB. In this article, we will answer some frequently asked questions (FAQs) about this workflow.

Q: What is the purpose of this workflow?

A: The purpose of this workflow is to automate the process of running benchmarks across different languages, including Python, Julia, and MATLAB. This allows developers to easily compare the performance of their code across different languages and identify performance bottlenecks.

Q: What languages are supported by this workflow?

A: This workflow currently supports three languages: Python, Julia, and MATLAB. However, it can be easily extended to support other languages by adding more matrix cells to the workflow.

Q: How do I add more languages to this workflow?

A: To add more languages to this workflow, you need to add more matrix cells to the workflow. For example, if you want to add JavaScript to the workflow, you would add a new matrix cell with the language set to "javascript".

Q: What are the benefits of using this workflow?

A: The benefits of using this workflow include:

  • Easy comparison of performance across different languages
  • Identification of performance bottlenecks
  • Automation of the benchmarking process
  • Easy integration with other tools and services

Q: How do I troubleshoot issues with this workflow?

A: If you encounter any issues with this workflow, you can try the following:

  • Check the GitHub Actions logs for errors
  • Verify that the matrix strategy is correctly set up
  • Ensure that the required dependencies are installed
  • Consult the GitHub Actions documentation for more information

Q: Can I use this workflow with other tools and services?

A: Yes, you can use this workflow with other tools and services. For example, you can integrate this workflow with your CI/CD pipeline to automate the benchmarking process.

Q: How do I customize this workflow for my specific use case?

A: To customize this workflow for your specific use case, you can modify the workflow to include your specific requirements. For example, you can add more languages to the workflow or modify the benchmarking process to suit your needs.

Q: What are some common issues that I may encounter with this workflow?

A: Some common issues that you may encounter with this workflow include:

  • Incorrect setup of the matrix strategy
  • Missing dependencies
  • Incorrect configuration of the workflow
  • Issues with the benchmarking process

Q: How do I resolve issues with this workflow?

A: To resolve issues with this workflow, you can try the following:

  • Check the GitHub Actions logs for errors
  • Verify that the matrix strategy is correctly set up
  • Ensure that the required dependencies are installed
  • Consult the GitHub Actions documentation for more information

Q: Can I use this workflow with other GitHub Actions features?

A: Yes, you can use this workflow with other GitHub Actions features. For example, you can use this workflow with GitHub Actions'-in features such as GitHub Actions' CI/CD pipeline.

Q: How do I get started with this workflow?

A: To get started with this workflow, you can follow these steps:

  1. Create a new GitHub Actions workflow file called benchmark.yml
  2. Define the matrix strategy for the workflow
  3. Add the required dependencies to the workflow
  4. Configure the workflow to run the benchmarking process
  5. Push the workflow to your GitHub repository

Conclusion

In this article, we have answered some frequently asked questions (FAQs) about the GitHub Actions workflow to run all benchmarks across languages. We hope that this article has provided you with the information you need to get started with this workflow and to troubleshoot any issues that you may encounter.