Template Pipeline

This project is a template pipeline for data processing, model training, testing, and deployment. It provides a structured setup for developing, testing, and deploying data science or machine learning projects with a focus on automation, code quality, and documentation.

Table of Contents

• Template Pipeline
  • Table of Contents
  • Project Structure
  • Installation
  • How It Works
  • Security Considerations
  • Contributing

Project Structure

The project is organized into several directories, each serving a specific purpose:

• .github/workflows/: Contains GitHub Actions workflows for CI/CD automation.

  • test.yml: Runs unit and integration tests automatically.
  • extract.yml: Automates data extraction workflow.
  • push.yml: Handles deployment tasks.

• raw_data/: Contains raw data files and instructions.

  • README.md: Instructions for handling raw data.

• derived_data/: Contains processed data files and instructions.

  • README.md: Instructions for handling processed data.

• test/: Test scripts for unit and integration testing.

  • unit/test_example.py: Example unit test.
  • integration/test_example.py: Example integration test.

• scripts/: Core scripts for pipeline operations.

  • processing.py: Script for data processing tasks.
  • training.py: Script for model training tasks.
  • validation.py: Script for data validation tasks.
  • utils.py: Utility functions for the pipeline.

• docs/: Documentation and configuration files.

  • requirements.txt: Lists project dependencies.
  • CONTRIBUTING.md: Guidelines for contributing to the project.

• Root Files:

  • .gitignore: Specifies which files and directories to ignore in version control.
  • .env.example: Template for environment variables.
  • main.py: Entry point for pipeline execution.
  • README.md: Main readme for project overview.

Installation

To set up this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/Template_Pipeline.git
   cd Template_Pipeline

2. Create a virtual enviroment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

    pip install -r ./docs/requirements.txt

4. Set up enviroment variables:

   • Copy .env.example to .env and fill in the required environment variables

5. Run the pipeline:

   • Execute the main pipeline script:

   python main.py

How It Works

The Template_Pipeline is designed to automate data processing, model training, testing, deployment, and documentation generation. Here’s a breakdown of the core workflows:

1. Data Processing: The processing.py script in the scripts/ directory handles data extraction, transformation, and loading (ETL). This script processes the raw data located in raw_data/ and generates processed data in derived_data/.

2. Model Training: The training.py script is responsible for training machine learning models using the processed data. It can be customized to fit different algorithms and model architectures.

3. Testing: The test/ directory contains unit and integration tests to ensure the code quality and functionality. The tests are run automatically using GitHub Actions (see .github/workflows/test.yml).

4. Deployment: The push.yml workflow defines the steps for deploying the trained models or applications.

Security Considerations

• Environment Variables: Sensitive information such as database credentials, API keys, and secret keys should be stored in environment variables. Use the .env.example file as a template, and do not commit the .env file to version control.

• Access Control: Ensure that only authorized users have access to the repository, especially when it contains sensitive data or credentials.

• Data Security: Be mindful of the size and sensitivity of data files. Avoid committing large files directly into the repository. Consider using external storage solutions for large datasets.

Contributing

We welcome contributions from the community! Please refer to docs/CONTRIBUTING.md for guidelines on how to contribute to this project!