Github Actions for Continuous Integration of AI Pipelines

Question

Pulumi · Accepted Answer

Continuous Integration (CI) is a development practice where developers integrate code into a shared repository frequently. Each integration can then be verified by an automated build and automated tests. GitHub Actions is a CI/CD service provided by GitHub that helps automate tasks within the software development life cycle. GitHub Actions is flexible and allows you to create custom workflows that can build, test, package, release, or deploy any code project on GitHub.

In the context of AI pipelines, CI can be utilized to automatically run tests whenever changes are made to the codebase, ensuring that new code doesn't break the existing application. Additionally, AI workflows may include steps like data validation, model training, evaluation, and deployment, which can be automated using GitHub Actions.

Here is an example of a GitHub Actions workflow for a Python-based AI project that uses Pulumi for infrastructure as code. This workflow includes steps for installing dependencies, linting the code, running tests, and if tests pass on the main branch, deploying the application using Pulumi.

```python
# This example demonstrates configuring GitHub Actions for continuous integration
# with Pulumi to deploy AI pipelines. We assume that you have a Pulumi project
# with an existing Pulumi.yaml and Pulumi.<stack-name>.yaml file for your AI
# project and a `requirements.txt` for your Python dependencies.

# Import the Pulumi GitHub package for setting up GitHub resources.
import pulumi_github as github

# First, you will need to create a GitHub token and provide it to Pulumi
# so it can make changes on your behalf. You should store your GitHub token
# as a secret in your Pulumi stack configuration or in your CI system.

# Create a new GitHub repository where your AI pipeline's code will be stored.
repo = github.Repository("ai-pipeline-repo",
                         description="Repository for AI pipeline",
                         visibility="public", # or "private", depending on your needs
                         )

# Then, you can add secrets to your GitHub repository that will be used by
# the GitHub Actions. For instance, you might add a secret for accessing your
# Pulumi Access Token, which is necessary for deploying infrastructure changes.

# Add a Pulumi Access Token secret for GitHub Actions to be able to deploy infrastructure changes.
pulumi_access_token = github.ActionsSecret("pulumi-access-token",
                                           repository=repo.name,
                                           plaintext_value="your-pulumi-access-token",  # Replace with your Pulumi Access Token
                                           )

# Here is an example GitHub Actions workflow for CI.
# In your repository, you would typically put this in a file named
# `.github/workflows/ci.yaml`.

ci_workflow = f"""
name: CI
on:
  push:
    branches:
      - main
  pull_request:
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.8
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
      - name: Lint with flake8
        run: |
          pip install flake8
          # stop the build if there are Python syntax errors or undefined names
          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
      - name: Test with pytest
        run: |
          pip install pytest
          pytest

deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.8
      - name: Install Pulumi CLI
        run: |
          curl -fsSL https://get.pulumi.com | sh
          export PATH=$PATH:$HOME/.pulumi/bin
      - name: Deploy with Pulumi
        run: |
          pulumi login
          pulumi up --yes
        env:
          PULUMI_ACCESS_TOKEN: ${{ secrets.PULUMI_ACCESS_TOKEN }}
"""

# Create a GitHub workflow file in the repository to store the CI workflow.
github_actions_ci = github.ActionsWorkflow("ci-pipeline",
                                           repository=repo.name,
                                           path=".github/workflows/ci.yaml",
                                           workflow=ci_workflow,
                                           )
```

This code does the following:
- Imports the Pulumi GitHub package which allows you to configure resources within your GitHub repository.
- Creates a new GitHub repository for your AI pipeline's code.
- Adds a Pulumi Access Token as a GitHub Secret to allow GitHub Actions to deploy infrastructure changes using Pulumi.
- Defines a GitHub Actions workflow that includes jobs for building and testing code on every push to the main branch as well as on each pull request. For simplicity, this example includes generic Python linting with `flake8` and testing with `pytest`.
- In the deploy step, it sets up the Pulumi CLI and runs `pulumi up` only if the tests pass and the changes are made to the main branch. It uses the Pulumi Access Token stored as a GitHub Secret.

Please note that the Pulumi Access Token and other sensitive information should be stored as GitHub Secrets and not included in plain text in the workflow file or the Pulumi code for security reasons.

This workflow is a basic starting point, and you might need to customize it according to the specifics of your AI pipelines. For example, additional steps may include data validation, running training scripts, evaluating model performance, and deploying trained models.