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

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#### 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

``bash

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

`bash

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

`bash

Create ML project structure

polint crsmr ./my-ml-project --template ml
`

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#### 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.)

`bash

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
`

`bash

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

`bash

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

`bash

Create distributable package

polint package --name MyApp --output ./dist/myapp.exe --include-runtime
`

📦 Installation

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`bash

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"
`

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`bash

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

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`bash

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
`

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`bash

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

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`bash

Anchor5 system path

export ANCHOR5_HOME="./anchor5"

Model storage location

export ANCHOR5_MODELS="./models"

Log level

export LOG_LEVEL="info"
`

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- 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.

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Made with ❤️ by the Plazma Development Team