Metadata-Version: 2.1
Name: scalarstop
Version: 0.1.1
Summary: A framework for managing machine learning experiments.
Home-page: https://www.scalarstop.com
License: MIT
Author: Neocrym Records Inc
Author-email: engineering@neocrym.com
Requires-Python: >=3.8,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: SQLAlchemy (>=1.4.5,<2.0.0)
Requires-Dist: cloudpickle (>=1.6.0,<2.0.0)
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: psycopg2-binary (>=2.8.6,<3.0.0)
Requires-Dist: tensorflow (>=2.3.0)
Project-URL: Repository, https://github.com/scalarstop/scalarstop
Description-Content-Type: text/x-rst

Organize your machine learning experiments with ScalarStop
==========================================================

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 <https://github.com/scalarstop/scalarstop/blob/main/notebooks/tutorial.ipynb>`_ 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 <https://docs.scalarstop.com>`_. In general, a typical ScalarStop workflow involves four steps:

1. Organize your datasets with `scalarstop.datablob <https://www.scalarstop.com/en/latest/autoapi/scalarstop/datablob/#module-scalarstop.datablob>`_.
2. Describe your machine learning model architectures using `scalarstop.model_template <https://www.scalarstop.com/en/latest/autoapi/scalarstop/model_template/#module-scalarstop.model_template>`_.
3. Load, train, and save machine learning models with `scalarstop.model <https://www.scalarstop.com/en/latest/autoapi/scalarstop/model/#module-scalarstop.model>`_.
4. Save hyperparameters and training metrics to a SQLite or PostgreSQL database using `scalarstop.train_store <https://www.scalarstop.com/en/latest/autoapi/scalarstop/train_store/#module-scalarstop.train_store>`_.

