Lightweight framework for structuring arbitrary reproducible neural network learning procedures using PyTorch.
PyTorch code can easily become complex, unwieldy, and difficult to understand as a project develops. Luz aims to provide a common scaffold for PyTorch code in order to minimize boilerplate, maximize readability, and maintain the flexibility of PyTorch itself.
The basis of Luz is the Runner, an abstraction representing batch-wise processing of data over multiple epochs. Runner has predefined hooks to which code can be attached and a State which can be manipulated to define essentially arbitrary behavior. These hooks can be used to compose multiple Runners into a single algorithm, enabling dataset preprocessing, model testing, and other common tasks.
To further reduce boilerplate, the Learner abstraction is introduced as shorthand for the extremely common Preprocess-Train-Validate-Test algorithm. Simply inherit luz.Learner and define a handful of methods to completely customize your learning algorithm.
Two additional abstractions are provided for convenience: Scorers, which score (i.e. evaluate) a Learner according to some predefined procedure, and Tuners, which tune Learner hyperparameters. These abstractions provide a common interface which makes model selection a two-line process.
Getting Started
Installing
From pip:
pip install luz
From conda:
conda install -c conda-forge -c pytorch -c kijana luz
Documentation
See documentation here.
Examples
See example scripts in Examples.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contributing
See CONTRIBUTING.
1 maintainer
Release files for luz 10.3.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| luz-10.3.0.tar.gz | 24.0 kB | Details |
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| luz-10.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 50.4 kB
Release files / luz-10.3.0.tar.gz
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| Size | 24.0 kB |
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| Tags | Python 3 |
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