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File Scripts Project

A collection of tools for managing machine learning workflows, file organization, and environment setup.

Quick Links

  • tools
    • Launch tools menu from any directory
  • source ~/.bashrc
    • Reload shell after installation

Available Tools

Environment

  • Setup (st)
    • Environment configuration and setup
    • Git synchronization
    • Command shortcuts installation
    • WANDB configuration (RunPod)

Model Management

  • Config Manager (cm)

    • Training configuration management
    • Template-based configuration
    • Version control integration

  • LoRA Mover (lm)

    • Process and organize LoRA models
    • Automated file organization
    • Version management

Cleanup

  • Remove Configs (rc)

    • Remove configuration files
    • Batch cleanup support

  • Dataset Cache (rd)

    • Clear dataset cache
    • Free up disk space

  • Dataset JSON (rj)

    • Clear dataset JSON files
    • Reset training metadata

  • Checkpoints (cp)

    • Delete .ipynb_checkpoints directories
    • Clean workspace structure

  • Delete Models (dm)

    • Remove model files and data
    • Selective cleanup options

Utilities

  • Download Configs (dc)

    • Sync configurations with Dropbox
    • Cloud backup integration

  • Debug Crops (db)

    • Debug image preparation issues
    • Visual feedback system

Installation

# Clone repository
git clone https://github.com/rafstahelin/file-scripts.git /workspace/file-scripts

# Run setup
cd /workspace/file-scripts
python tools.py  # Select 'setup' or 'st'
source ~/.bashrc

Project Structure

/workspace/file-scripts/
├── tools/          # Tool implementations
├── docs/          # Documentation
└── tools.py       # Main menu system

Environment Support

  • ✓ RunPod

    • Full support with cloud integrations
    • WANDB integration
    • Network volume support

  • ✓ Container

    • Standard functionality
    • Local development features

  • ~ WSL

    • Basic functionality
    • Future enhancements planned

Development

Tools follow a standardized pattern:

  • Python module in tools directory
  • Tool class with run() method
  • Rich console interface
  • Error handling and user feedback

Support

For issues and feature requests, please open an issue on the GitHub repository.