Polint - Comprehensive Data Management and ML Toolkit

A powerful Node.js toolkit for data management, machine learning model training, and project scaffolding with advanced automation capabilities.

🚀 Features

Core Modules

PRMTS - Dataset Discovery and Fetching

• Intelligent dataset search from multiple sources (Kaggle, Hugging Face, UCI ML Repository, GitHub)
• Interactive confirmation before downloading datasets
• Multiple format support (JSON, CSV, XML)
• Smart filtering and categorization

# Search and download datasets
polint prmts "machine learning datasets" --format json --directory ./data

EXG GitHub - GitHub Dataset Integration

• Direct GitHub repository access for datasets
• Branch-specific downloads with flexible targeting
• Repository information retrieval and validation
• Error handling for private repositories and missing files

# Download dataset from GitHub
polint exg facebookresearch/ImageNet data/train.csv --branch main --directory ./datasets

CR SMR - Project Structure Creation

• Smart project scaffolding with template support
• Multiple project types: Default, Machine Learning, Web
• Non-destructive file creation (preserves existing files)
• Template-based structure generation

# Create ML project structure
polint crsmr ./my-ml-project --template ml

Advanced Features

Anchor.js - Advanced Model Training System

• Multi-framework model training (TensorFlow, PyTorch, scikit-learn)
• Flexible architecture configuration with keyword-based customization
• Multiple model formats (.anchor5, .h5, .pkl)
• Training pipeline management with progress tracking
• Model serialization and deployment ready formats

Supported Model Types:

• Neural Networks (Dense, CNN, RNN/LSTM)
• Text Classification (BERT, GPT-based)
• Image Classification (ResNet, VGG, custom CNNs)
• Traditional ML (Random Forest, SVM, etc.)

# Train a model with custom configuration
polint anchor train --data ./data/train.csv --output ./models --keywords nlp text

Anchor5.js - Local Installation Management

• Self-contained system installation with organized structure
• Automatic updates and version management
• Backup and restore capabilities
• Environment configuration and path management

Installation Structure:

anchor5/
├── models/          # Trained models storage
├── databases/       # Local database files
├── secrets/         # Encrypted credentials and keys
├── config/          # System configuration
├── logs/           # Application logs
├── backups/        # Automatic backups
└── temp/           # Temporary files

# Install Anchor5 system
polint anchor5 install --directory ./anchor5-system --force

Anchor5-loader.js - Remote Model Hub

• Remote model registry integration
• One-click model installation from curated repositories
• Model caching for faster subsequent loads
• Version management and update notifications
• Model search and discovery

Available Models:

• BERT variants (bert-base-uncased, bert-large-uncased)
• GPT models (gpt2, gpt2-medium, gpt2-large)
• Computer Vision models (ResNet, VGG, EfficientNet)
• Custom trained models

# Load remote model
polint loader load bert-base-uncased

# Search for models
polint loader search "text classification"

# Install model locally
polint loader install resnet50 --path ./models

DIS Package - Application Packaging

• Cross-platform packaging (Windows .exe, macOS .app, Linux AppImage)
• Self-contained applications with embedded runtime
• Installation automation with desktop shortcuts
• Custom branding and metadata

# Create distributable package
polint package --name MyApp --output ./dist/myapp.exe --include-runtime

📦 Installation

Global Installation (Recommended)

# Install globally for CLI access from anywhere
npm install -g polint

# Now you can use polint command from any directory
polint --help
polint prmts "machine learning datasets"

Local Installation

# Install locally in your project
npm install polint

# Use with npx (recommended for local installation)
npx polint --help
npx polint prmts "machine learning datasets"

# Or use node directly
node bin/polint.js --help

🛠️ Usage

Basic Commands

# Show help
polint --help

# Check version
polint --version

# Dataset Operations
polint prmts "sentiment analysis" --directory ./datasets
polint exg microsoft/CNTK data/train.txt

# Project Management
polint crsmr ./my-project --template web

# Model Training
polint anchor train --data ./data/train.csv --output ./models

# System Management
polint anchor5 install --directory ./anchor5
polint loader list --type nlp

# Packaging
polint package --name MyApplication --output ./dist/app.exe

Advanced Examples

# Complete ML pipeline
polint prmts "image classification" --format csv --directory ./data
polint crsmr ./ml-project --template ml
polint anchor train --data ./data/dataset.csv --output ./models --keywords vision cnn
polint loader install resnet50 --path ./models
polint package --name MLApp --output ./dist/ml-app.exe

🔧 Configuration

Environment Variables

# Anchor5 system path
export ANCHOR5_HOME="./anchor5"

# Model storage location
export ANCHOR5_MODELS="./models"

# Log level
export LOG_LEVEL="info"

Configuration Files

• anchor5/config/anchor5.json - System configuration
• anchor5/config/installation.json - Installation metadata
• models/config.json - Model training configuration

📋 Requirements

• Node.js: 14.0.0 or higher
• Memory: 2GB+ recommended for model training
• Storage: 5GB+ recommended for datasets and models
• Internet: Required for dataset fetching and remote models

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Built with Node.js and modern JavaScript
• Uses Commander.js for CLI interface
• Axios for HTTP requests
• fs-extra for enhanced file operations
• Ora for beautiful progress indicators

📞 Support

For support, join our discord community.

Made with ❤️ by the Plazma Development Team