Organize your machine learning experiments with ScalarStop¶
ScalarStop helps you train machine learning models by:
creating a system to uniquely name datasets, model architectures, trained models, and their hyperparameters.
saving and loading datasets and models to/from the filesystem in a consistent way.
recording dataset and model names, hyperparameters, and training metrics to a SQLite or PostgreSQL database.
ScalarStop is available on PyPI. You can install it from the command line using:
pip3 install scalarstop
The best way to learn ScalarStop is to follow the Official ScalarStop Tutorial.
Afterwards, you might want to dig deeper into the ScalarStop documentation. In general, a typical ScalarStop workflow involves four steps:
Organize your datasets with
Describe your machine learning model architectures using
Load, train, and save machine learning models with
Save hyperparameters and training metrics to a SQLite or PostgreSQL database using