An end-to-end machine learning pipeline for generating traffic data. This repository provides a modular framework for training, benchmarking, and evaluating traffic generation models.
dataset.py: Contains data loaders, preprocessing scripts, and dataset class definitions.model.py: Defines the neural network architectures used for traffic generation.train.py: The primary training loop, handling optimization, backpropagation, and checkpointing.bench.py: Benchmarking script to evaluate model performance, inference speed, and generation quality.experiment.yaml: Configuration file to manage hyperparameters, dataset paths, and training parameters.logger.py: Logging utility to track training metrics such as loss and accuracy.utils.py: Helper functions and common utilities used across the project.
Ensure you have Python 3.8 or later installed. It is highly recommended to use a virtual environment to manage dependencies.
# Clone the repository
git clone [https://github.com/Zayn-Rekhi/TrafficGeneration.git](https://github.com/Zayn-Rekhi/TrafficGeneration.git)
cd TrafficGeneration
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
# Install dependencies (requires a requirements.txt file)
# pip install -r requirements.txt