Machine Learning Research Wizard
Reason this release was yanked:
Bug in pyproject.toml
Project description
MLWiz: the Machine Learning Research Wizard
Documentation
MLWiz is a Python library that aids reproducible machine learning research.
It takes care of the boilerplate code to prepare and run experiments, by providing automatic management of data splitting, loading and common experimental settings. It especially handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU or GPU). It is a generalized version of PyDGN that can handle different kinds of data and models (vectors, images, time-series, graphs).
Installation:
Requires at least Python 3.10. Simply run
pip install mlwiz
Quickstart:
Build dataset and data splits
mlwiz-data --config-file examples/DATA_CONFIGS/config_MNIST.yml [--debug]
Launch experiments
mlwiz-exp --config-file examples/MODEL_CONFIGS/config_MLP.yml [--debug]
Stop experiments
Use CTRL-C, then type ray stop --force to stop all ray processes you have launched.
Using the Trained Models
It's very easy to load the model from the experiments: see the end of the Tutorial for more information!
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