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MathCAS: Modular Neural Network Training Framework

A robust, highly modular PyTorch-based framework tailored for training, evaluating, and profiling neural networks on complex tabular datasets.

Originally developed for research in compressible fluid flow analysis, this framework is designed to bridge the gap between rapid academic experimentation and clean, production-ready engineering practices.

Note: This project is an active work in progress. Features and analytical tools may evolve as the research continues.

Inference vs MSE Comparison (Note: Example output generated by the framework analyzing the trade-off between inference time, parameter count, and Mean Squared Error across various model architectures. Specific physical parameters are anonymized for confidentiality).

✨ Key Features

This repository is built with a strong focus on reproducibility, performance, and clear separation of concerns:

  • ⚙️ YAML-Driven Architecture: Define network layers, learning rates, data splits, and early stopping rules entirely via configuration files (no hardcoding).
  • 🚀 NVTX Profiling: Integrated NVIDIA NVTX markers (nvtx_range) in the dataloaders and training loops for deep performance analysis and bottleneck identification.
  • 🧠 Advanced Diagnostics (Dying ReLU): Custom PyTorch forward hooks designed to detect and report "dying" activation neurons across batches during inference.
  • 🔄 Full Reproducibility: Automated experiment directory creation, config hashing, and strict random seed freezing.

📊 Exploratory Data Analysis (EDA)

Before feeding data into the PyTorch models, the framework includes robust Jupyter notebook tooling for deep data inspection to ensure feature quality and prevent data leakage.

Correlation Matrix Data Distribution Analysis

📂 Project Structure

MathCAS/
├── config/             # YAML schema definitions and config loaders
├── datasets/           # Directory for tabular data (gitignored)
├── experiments/        # User-defined YAML configuration files for experiments
├── figures/            # Exported visualizations and plots
├── notebooks/          # Jupyter notebooks for EDA, visualization, and tooling
├── outputs/            # Auto-generated experiment logs, model checkpoints, and hashed configs
├── src/                # Core ML logic (architecture, dataloaders, evaluation)
├── templates/          # Base YAML templates for regression/classification
├── utils/              # Helpers: logger, NVTX profiler, metric computations
└── main.py             # Main entry point for the training pipeline

🚀 Quickstart

  1. Clone the repository and install dependencies:

    git clone https://github.com/cervinka479/MathCAS.git
cd MathCAS
pip install -r requirements.txt

  2. Prepare your dataset: Place your tabular data (.csv) in the datasets/ directory.

  3. Configure your experiment: Edit the templates/regression.yaml file to match your dataset's input/output columns and your desired neural network architecture.

  4. Run the training pipeline:

    python main.py templates/regression.yaml

  5. Check outputs: Trained weights (best_model.pt), logs, hashed configs, and metrics are automatically saved into a uniquely named folder inside the outputs/ directory.

🛠️ Configuration & Advanced Tools

  • Experiment Configuration: The entire training pipeline is controlled via YAML files. You can easily adjust learning rates, architecture, and data splits without modifying the core codebase.
  • Analytical Notebooks: Check out the notebooks/ directory for advanced evaluation tools, including dying ReLU inspection and cross-experiment performance visualizations.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.