scalarstop 0.1.1

A framework for managing machine learning experiments.

ScalarStop is a framework written in Python that helps you keep track of datasets, models, hyperparameters, and training metrics in machine learning experiments.

Installation

ScalarStop is available on PyPI. You can install by running the command pip3 install scalarstop.

Usage

Read the ScalarStop Tutorial to learn the core concepts behind ScalarStop and how to structure your datasets and models.

Afterwards, you might want to dig deeper into the ScalarStop Documentation. In general, a typical ScalarStop workflow involves four steps:

1. Organize your datasets with scalarstop.datablob.

2. Describe your machine learning model architectures using scalarstop.model_template.

3. Load, train, and save machine learning models with scalarstop.model.

4. Save hyperparameters and training metrics to a SQLite or PostgreSQL database using scalarstop.train_store.