Could you please tell me how to set up CI/CD on GitHub?

[masterAI359]

Why are you starting this discussion?

Question

What GitHub Actions topic or product is this about?

Misc

Discussion Details

Hi everyone.
I'm a developer who just started developing.
I've been trying to implement CI/CD on GitHub, but I don't know about the method, so I'm asking for help.
I appreciate your support.

[JagreetDasgupta]

Hey, welcome to development and great that you’re exploring CI/CD early on—it’s a really valuable skill!

On GitHub, the most common way to set up CI/CD is by using GitHub Actions. It lets you define workflows in a .github/workflows/ folder in your repo. These workflows are just YAML files where you can describe what should happen when certain events occur (for example: when you push code, open a pull request, or create a release).

A basic example is:

Trigger the workflow on push or pull_request

Run your build, tests, and maybe linting

Optionally, deploy somewhere if tests pass

If you let us know what kind of project you’re working on (language, framework, and where you want to deploy), we can suggest a tailored example workflow file to get you started. GitHub also has a bunch of starter templates you can use directly from the Actions tab.

Don’t worry if YAML looks overwhelming at first—you’ll get the hang of it quickly.

[jfullstackdev]

steps to get started :

1. create a Workflow File

Inside your repo, create this folder and file:

.github/workflows/ci.yml

---

2. add a basic CI Workflow

Paste in something like this (for a Node.js project as an example):

name: CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: 20

      - name: Install dependencies
        run: npm install

      - name: Run tests
        run: npm test

This will:

1. Run on every push and pull request to main.
2. Install dependencies.
3. Run your tests.

---

3. (Optional) Add Deployment (CD)

If you want CD (deploy automatically), you can add another job after tests, for example:

• Deploy to GitHub Pages (for static sites).
• Push to Docker Hub.
• Deploy to cloud providers (Vercel, AWS, Azure, etc.).

Most providers already have ready-made Actions you can reuse.

---

4. Learn by Examples

check these resources:

https://docs.github.com/en/actions/quickstart

https://github.com/marketplace?type=actions → prebuilt actions you can drop in

---

start with a CI workflow that runs your tests, once that works, add CD (deployment)

build it step by step instead of all at once, same rule in Dev that every step must be working before proceeding to the next one

and in my first time doing this, AI helped me a lot ! I can proceed while learning at the same time I'm doing my task ! even standalone ChatGPT is fine but Copilot, particularly the Agent modes are way too powerful since they have context on what you are working with because they have the access to your codebase,

GPT-4.1 Agent Mode or the more powerful but limited Claude Sonnet 4 but this is Premium Request, so use in those critical / heavy tasks

[masterAI359]

Thank you for the detailed explanation.
I will proceed with implementation by this method.

[MMunim90]

1. Create a Workflow File

In your repository, create a folder called .github/workflows/. Inside it, add a file, for example:

.github/workflows/ci.yml

name: CI Pipeline

on:
push:
branches: [ main ]
pull_request:
branches: [ main ]

jobs:
build:
runs-on: ubuntu-latest

steps:
  # Get your code
  - uses: actions/checkout@v3

  # Set up Python
  - name: Set up Python
    uses: actions/setup-python@v4
    with:
      python-version: "3.11"

  # Install dependencies
  - name: Install dependencies
    run: |
      python -m pip install --upgrade pip
      pip install -r requirements.txt

  # Run tests
  - name: Run tests
    run: |
      pytest

This example assumes you’re working with Python, but the structure is similar for other languages.

2. Add Deployment (CD)

If you want continuous deployment, add another job (or step) that runs only after tests pass. For example, to deploy a static site to GitHub Pages:

deploy:
needs: build
runs-on: ubuntu-latest

steps:
- uses: actions/checkout@v3
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: ./public

For cloud platforms (Heroku, AWS, Azure, etc.), you’d replace this with their official or community deployment action.

3. Manage Secrets

If deployment requires secrets (like API keys), store them in your repo’s Settings → Secrets and variables → Actions. Then use them in your workflow as secrets.MY_KEY.

4. Observe and Debug

Whenever you push, go to the Actions tab in your repo. You’ll see the pipeline running live. If it fails, logs are available for each step.

👉 So, in short:

Put a YAML workflow file in .github/workflows/.

Define when it runs (on:).

Add jobs (build, test, deploy).

Use official actions for setup and deployment.

[a-ariff]

Excellent foundational guidance from @JagreetDasgupta, @jfullstackdev, and @MMunim90! Since you're just starting development, here are some advanced patterns to grow into:

🔒 Security-First CI/CD (Critical for Production)

Building on the basic examples, add security scanning:

name: Secure CI/CD Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run security audit
        run: npm audit --audit-level=high
      
      - name: SAST with CodeQL
        uses: github/codeql-action/analyze@v3
        with:
          languages: javascript

🔄 Enterprise Integration Patterns

As mentioned by @jfullstackdev about AI helping - here's Azure DevOps integration for hybrid environments:

- name: Trigger Azure DevOps Pipeline
  uses: Azure/pipelines@v1.2
  with:
    azure-devops-project-url: '${{ secrets.AZURE_DEVOPS_URL }}'
    azure-pipeline-name: 'production-deploy'

📄 Advanced Monitoring & Observability

Beyond basic deployment, add application insights:

- name: Deploy with telemetry
  run: |
    # Deploy your app
    npm run deploy
    
    # Report deployment success to monitoring
    curl -X POST "${{ secrets.WEBHOOK_URL }}" \
      -d '{"deployment": "success", "commit": "${{ github.sha }}}"

🎯 Multi-Environment Strategy

The examples showed single deployments. For professional development:

jobs:
  deploy-staging:
    if: github.ref == 'refs/heads/develop'
    environment: staging
    
  deploy-production:
    if: github.ref == 'refs/heads/main'
    environment: production
    needs: [test, security-scan]

Key insight: Start with @MMunim90's basic structure, then progressively add these enterprise patterns as your project grows. The modular approach mentioned by @jfullstackdev is spot-on - build working foundations first!

[masterAI359]

thank you so much!
I implemented CI/CD using above method.
best regard!

[RouseRo]

What is a python decorator attribute

[a-ariff]

Python doesn't have a specific "decorator attribute" - decorators are functions that take another function/class and return a modified version, applied using @ syntax.

Common use cases: logging, timing, access control, caching, validation, registering functions, and built-ins like @Property, @staticmethod, @classmethod.

Simple example:

def my_decorator(func):
    def wrapper():
        print("Before function")
        func()
        print("After function")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()  # Prints: Before function, Hello!, After function

@Property creates attribute-like access:

class Circle:
    def __init__(self, radius):
        self._radius = radius
    
    @property
    def area(self):
        return 3.14159 * self._radius ** 2

c = Circle(5)
print(c.area)  # Access like an attribute, not c.area()

Decorators work on both functions and classes, providing a clean way to modify behavior without changing the original code!