Folders and files
This repository contains all of the SmartShield AI Team’s work for building, training, and deploying machine learning models, as well as the API and tools necessary for end-to-end machine learning workflows.
- Data Collection
- Feature Engineering
- Model Training and Evaluation
- API App For Ml Engine
- Testing
├── LICENSE # License for the project
├── README.md # Project documentation
├── __pycache__/ # Python bytecode cache
│ └── app.cpython-311.pyc
├── api_src/ # Source code for the API application
│ ├── config # Configuration files for the API
│ ├── logger # Logging configuration and utilities
│ ├── routers # API route definitions
│ ├── schemas # API data schemas
│ ├── services # Services used by the API
│ └── tests # API-specific tests
├── app.py # Main application entry point
├── config/ # Configuration files for model training
│ ├── __init__.py
│ └── train # Model training configuration
├── data/ # Directory for datasets
│ ├── UNSW_NB15_data # Dataset related to UNSW_NB15
│ └── __init__.py
├── models/ # Directory for storing trained models
│ ├── UNSW_NB15_models # Serialized UNSW_NB15 models
│ └── __init__.py
├── notebooks/ # Jupyter notebooks for exploration and prototyping
│ ├── DetectionFeatureEngineering.ipynb # Feature engineering notebook
│ ├── UNSW_NB15_notebooks # Notebooks related to UNSW_NB15 data
│ └── __init__.py
├── requirements.txt # List of dependencies
├── src/ # Source code for data processing and model development
│ ├── __init__.py
│ ├── __pycache__
│ ├── data # Scripts for loading and processing data
│ ├── features # Scripts for creating and selecting features
│ ├── models # Scripts for training and evaluating models
│ ├── utils # Utility functions
│ └── visualization # Scripts for generating data visualizations
├── tests/ # Unit tests for various modules
│ ├── UNSW_NB15_tests # Tests related to the UNSW_NB15 data and models
│ └── __init__.py
-
Create a Virtual Environment
In the project root directory, create a virtual environment:python -m venv venv
-
Activate the Virtual Environment
- On macOS/Linux:
source venv/bin/activate - On Windows:
venv\Scripts\activate
- On macOS/Linux:
-
Install the Required Dependencies
Install all required packages by running:pip install -r requirements.txt
-
Run the Application
Start the ML Engine application with Uvicorn using the following command:uvicorn app:app --host 0.0.0.0 --port 8002 --workers 8
- app:app: Specifies the app instance in the main application file.
- --host 0.0.0.0: Makes the app accessible externally.
- --port 8002: Runs the app on port 8002.
- --workers 8: Starts the app with 8 worker processes, allowing concurrent request handling.