A comprehensive toolkit for data management, model training, and project scaffolding
npm install polintbash
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
`
$3
#### 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/
āāā models/ # Trained models storage
āāā databases/ # Local database files
āāā secrets/ # Encrypted credentials and keys
āāā config/ # System configuration
āāā logs/ # Application logs
āāā backups/ # Automatic backups
āāā 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
`
š¦ Installation
$3
`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"
`
$3
`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
`
š ļø Usage
$3
`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
`
$3
`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
`
š§ Configuration
$3
`bash
Anchor5 system path
export ANCHOR5_HOME="./anchor5"
Model storage location
export ANCHOR5_MODELS="./models"
Log level
export LOG_LEVEL="info"
`
$3
- anchor5/config/anchor5.json - System configuration
- anchor5/config/installation.json - Installation metadata
- models/config.json - Model training configuration
š 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
š¤ 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`