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Home <self> API Documentation <autoapi/scalarstop/index>

Keep track of your machine learning experiments.

ScalarStop is an open-source framework for reproducible machine learning research.

ScalarStop was written and open-sourced at Neocrym, where it is used to train thousands of models every week.

ScalarStop can help you:

organize datasets and models with content-addressable names.

ScalarStop datasets and models are given automatically-generated names based on their hyperparameters--making them easy to version and easy to find.

save/load datasets and models to/from the filesystem.

ScalarStop wraps existing dataset and model saving logic in TensorFlow for safety, correctness, and completion.

record hyperparameters and metrics to a relational database.

ScalarStop saves dataset and model names, hyperparameters, and training metrics to a SQLite or PostgreSQL database.

Getting started

System requirements

ScalarStop is a Python package that requires Python 3.8 or newer.

Currently, ScalarStop only supports tracking :pytf.data.Dataset datasets and :pytf.keras.Model models. As such, ScalarStop requires TensorFlow 2.8.0 or newer.

We encourage anybody that would like to add support for other machine learning frameworks to ScalarStop. :)

Installation

ScalarStop is available on PyPI.

If you are using TensorFlow on a CPU, you can install ScalarStop with the command:

python3 -m pip install scalarstop[tensorflow]

If you are using TensorFlow with GPUs, you can install ScalarStop with the command:

python3 -m pip install scalarstop[tensorflow-gpu]

Development

If you would like to make changes to ScalarStop, you can clone the repository from GitHub.

git clone https://github.com/scalarstop/scalarstop.git
cd scalarstop
python3 -m pip install .

Usage

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:

  1. Organize your datasets with :pyscalarstop.datablob.
  2. Describe your machine learning model architectures using :pyscalarstop.model_template.
  3. Load, train, and save machine learning models with :pyscalarstop.model.
  4. Save hyperparameters and training metrics to a SQLite or PostgreSQL database using :pyscalarstop.train_store.