Modular Reproducible Minimal Extensible Insightful
moai: Accelerating modern data-driven workflows¶
moai is a PyTorch-based AI Model Development Kit (MDK) that aims to improve data-driven model workflows, design and understanding. Since it is based on established open-source packages, it can be readily used to improve most AI workflows. To explore moai, simply install the package and follow the examples, having in mind that it is in early development alpha version, thus new features will be available soon.
Features & Design Goals¶
- Modularity via Monads: Use moai's existing pool of modular model building blocks.
- Reproducibility via Configuration: moai manages the hyper-parameter sensitive AI R&D workflows via its built-in configuration-based design.
- Productivity via Minimizing Coding: moai offers a data-driven domain modelling language (DML) that can facilitates quick & easy model design.
- Extensibility via Plugins: Easily integrate external code using moai's built-in metaprogramming and external code integration.
- Understanding via Analysis: moai supports inter-model performance and design aggregation actions to consolidate knowledge between models and query differences.
moai stands on the shoulders of giants as it relies on various large scale open-source projects:
> 1.7.0needs to be customly installed on your system/environment.
> 1.0.0is the currently supported training backend.
> 1.0drives moai's DML that sets up model configurations, and additionally manages the hyper-parameter complexity of modern AI models.
- Visdom is the currently supported visualization engine.
- HiPlot drives moai's inter-model analytics.
The Wider Open Source Community that conducts accessible R&D and drives most of moai's capabilities.
To install the currently released moai version package run:
pip install moai-mdk
Download the master branch source and install it by opening a command line on the source directory and running:
pip install . or
pip install -e . (in editable form)
Visit the documentation site to learn about moai's DML and the overall MDK design and usage.
The examples found at conf/examples will be documented soon.
Documentation is currently a work-in-progress.
The API documentation is currently a work-in-progress.