Metadata-Version: 2.4
Name: venturi
Version: 0.9.0
Summary: A hackable blueprint for training neural networks using PyTorch and Lightning.
Project-URL: Homepage, https://github.com/chcomin/venturi
Project-URL: Repository, https://github.com/chcomin/venturi
Author: Cesar Comin
License: MIT License
        
        Copyright (c) 2026 Cesar Henrique Comin
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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License-File: LICENSE
Keywords: configuration,deep-learning,lightning,pytorch
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.13
Requires-Dist: lightning>=2.6.0
Requires-Dist: matplotlib>=3.10.8
Requires-Dist: numpy>=2.3.5
Requires-Dist: pandas>=3.0.0
Requires-Dist: pillow>=12.1.0
Requires-Dist: pyyaml>=6.0.3
Requires-Dist: torch>=2.9.1
Requires-Dist: torchmetrics>=1.8.2
Requires-Dist: tqdm>=4.67.1
Provides-Extra: dev
Requires-Dist: build>=1.4; extra == 'dev'
Requires-Dist: mypy>=1.11; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest-mock>=3.15; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.4.0; extra == 'dev'
Requires-Dist: twine>=5.0; extra == 'dev'
Requires-Dist: types-pyyaml; extra == 'dev'
Provides-Extra: examples
Requires-Dist: optuna>=4.7.0; extra == 'examples'
Requires-Dist: torchvision>=0.24.1; extra == 'examples'
Requires-Dist: wandb>=0.23.1; extra == 'examples'
Description-Content-Type: text/markdown

# Venturi

A hackable blueprint for training neural networks using PyTorch and Lightning.

## Desigin principles

The configuration is purposely designed to not have pydantic validation. You create your classes and/or custom functions and add their parameters to a yaml file, and that is it. You can add your own pydantic validation before passing the configuration to the experiment object.