02 - Run Your First Python Benchmark

In this tutorial, we will walk you through setting up your first Python-based workflow.

Step 1: Create a GitHub Repository

Start by creating a new GitHub repository. For this guide, we’ll name the repository matmul-benchmark-python.

• Go to GitHub and create a repository named matmul-benchmark-python.
• Clone the repository locally:

git clone https://github.com/<your-username>/matmul-benchmark-python.git
cd matmul-benchmark-python

Step 2: Add a Benchmark (Python Example)

We will benchmark a NumPy matrix multiplication. Create a file named matmul.py at the root of your repository with the following content:

import numpy as np

def matmul(A, B):
    return np.dot(A, B)

if __name__ == '__main__':
    A = np.random.rand(64, 64)
    B = np.random.rand(64, 64)

    C = matmul(A, B)
    print(C)

Step 3: Define a Workflow

Create a directory called .daisy/ at the root of your repository:

mkdir .daisy

Inside .daisy/, create a workflow file named benchmark-python.yml with the following configuration:

on:
  push:
    branches:
      - main
  pull_request:
    types: [opened, reopened, synchronize, ready_for_review]

parameters:
  timeout: 30
  partitions:
    - bellis5

steps:
  build: |
    sudo apt-get install -y python3-numpy

  run:
    matmul:
      command: python3 matmul.py
      measurements: 1

This workflow:

• Triggers automatically upon every push to the main branch.
• Runs your benchmark on the bellis5 partition of our cluster.

Step 4: Commit and Push the Changes

Commit the files and push them to GitHub:

git add matmul.py .daisy/benchmark-python.yml
git commit -m "Add matmul benchmark and workflow"
git push origin main

Step 5: Run Your Python Benchmark

After pushing your changes, Daisytuner will automatically detect your workflow and execute your benchmark on our cluster.

You can track progress in two places:

• GitHub: Status checks will appear directly in your commits.

• Web App: Real-time logs and status updates can be found in the Web App. Make sure you are logged into the app with your Github account.

Congratulations! 🎉 You have successfully run your first benchmark using Daisytuner.

Extension: venv

You can also use a virtual environment for your workflow.

steps:
  build: |
    python3 -m venv venv
    . venv/bin/activate

    pip install numpy

Extension: Conda

If you prefer using conda, you can set the conda parameter and the default is activated automatically for the build and run stages.

parameters:
  timeout: 30
  conda: "3.12"
  partitions:
    - bellis5

steps:
  build: |
    pip install numpy