13 ci cd Implementation - Jeremy-Feytens/IT-Landscape GitHub Wiki

Implementation of a CI/CD Pipeline

Goal

Automate the building and publishing of a Docker image for my PyTorch application to the GitHub Container Registry (GHCR) every time there is a change pushed to the main branch.

Steps

1. Repository Setup

  • My PyTorch script app.py and requirements.txt are stored in the GitHub repository.
import torch
import torch.nn as nn
import torch.optim as optim

torch.manual_seed(0)
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
y = 2 * x + 1 + 0.1 * torch.randn_like(x)

model = nn.Sequential(
    nn.Linear(in_features=1, out_features=1)
)

criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

num_epochs = 50
for epoch in range(1, num_epochs + 1):
    preds = model(x)
    loss = criterion(preds, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0 or epoch == 1:
        w, b = model[0].weight.item(), model[0].bias.item()
        print(f"Epoch {epoch:2d}: loss={loss.item():.4f},  weight={w:.3f}, bias={b:.3f}")

test_x = torch.tensor([[4.0]])
pred_y = model(test_x)
print(f"\nModel prediction: for x=4.0 → y≈{pred_y.item():.3f} (true y=9.0)")
  • A Dockerfile was added to build a container image with Python and all required dependencies.
FROM python:3.13-alpine

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

EXPOSE 5000

CMD ["python", "app.py"]

2. GitHub Actions Workflow

  • Created a workflow file at .github/workflows/docker-build.yml.
  • This workflow is triggered automatically on every push to the main branch.

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3. Workflow Steps Breakdown

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Step Description
Checkout repository Uses actions/checkout@v4 to fetch the latest code.
Log in to GHCR Logs in to GitHub Container Registry using a Personal Access Token stored as secret CR_PAT.
Set variables Converts repository owner name to lowercase and defines the Docker image name for consistent tagging.
Build Docker image Builds the Docker image tagged as ghcr.io/<owner>/my-pytorch-app:<commit-sha>.
Push Docker image Pushes the Docker image to the GitHub Container Registry.
Save image tarball Saves the built image locally as image.tar.
Upload artifact Uploads image.tar as a workflow artifact for easy downloading.

4. Result

  • On each push to main, the workflow is triggered.
  • A new Docker image is built and published to GHCR.
  • The image is available at ghcr.io/<repo-owner>/my-pytorch-app:<commit-sha>.
  • The workflow completes successfully and can be viewed in the GitHub Actions tab.

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Conclusion

By implementing this CI/CD pipeline, the process of building and publishing my application is fully automated. This enables rapid deployment of new versions without manual intervention, saving time and reducing the risk of errors.

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