A cowsay clone for python in one file.
Project description
Maté 🧉
Maté is a deep learning framework compatible with pytorch(lightning), tensorflow(keras), and jax(flax). It is a package and experiment manager for deep learning. As a package manager you can add AI models, trainers and data loaders to your projects. As a project manager, Maté evaluates, trains, and keeps track of your experiments. Maté adds the source code of the dependencies to your project, making it fully customizable and reproducible.
Installation 🔌
pip install yerbamate
Examples
Please check out the examples repo for examples of pytorch lightning, keras and jax.
What is the Maté standard?
Mate enforces modularity and seperation of three basic components of a deep learning project: models, trainers, and data loaders. Each model, data loader and trainer should be a module inside its respective folder. This allows for out-of-the-box sharing of models, data loaders, and trainers.
An example of a the foolder structure of a mate project is shown below:
├── root_project_folder
│ ├── data
│ │ ├── __init__.py
│ │ ├── cifar
│ │ │ ├── __init__.py
│ │ │ ├── cifar10.py
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│ ├── models
│ │ ├── __init__.py
│ │ ├── resnet
│ │ │ ├── __init__.py
│ │ │ ├── resnet.py
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│ ├── trainers
│ │ ├── __init__.py
│ │ ├── classifier
│ │ │ ├── __init__.py
│ │ │ ├── classifier.py
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│ ├── experiments
│ │ ├── resnet18_cifar10.json
│ │ ├── resnet34_cifar10.json
For Coders
Dear coders, we try our best to not get in your way and in fact, you do not have to integrate or import any mate class to your projects. Mate simply parses the configuration. To make your project mate compatible, you need to move a few files and make a Bombilla configuration file.
Mate configuration (AKA Bombilla🧉)
Mate defines an experiment with a configuration file, aka Bombilla, that is a ordered dictionary describing arguments and python objects in plain json. Bombilla supports any python module; including all the local project level modules and installed py packages (eg., tensorflow, pytorch, x_transformers, torchvision, vit_pytorch).
Quick Start ⚡
Train a model
mate train my_experiment
Evaluate a model
mate test my_experiment
Run a model
mate run feature_extraction my_experiment
Clone a model
mate clone resnet my_resnet
More tutorials, features and examples will be added soon!!
FAQ
Q: Does Maté work with colab?
A: Yes! Maté works with colab as any Maté project is exportable to a juypter notebook.
Contact 🤝
For questions please contact:
yerba.mate.dl(at)proton.me
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