https://github.com/cmu-sei/juneberry
Juneberry improves the experience of machine learning experimentation by providing a framework for automating the training, evaluation and comparison of multiple models against multiple datasets, reducing errors and improving reproducibility.
https://github.com/cmu-sei/juneberry
machine-learning
Last synced: about 1 year ago
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Juneberry improves the experience of machine learning experimentation by providing a framework for automating the training, evaluation and comparison of multiple models against multiple datasets, reducing errors and improving reproducibility.
- Host: GitHub
- URL: https://github.com/cmu-sei/juneberry
- Owner: cmu-sei
- License: other
- Created: 2021-01-29T15:18:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2023-04-14T19:19:51.000Z (about 3 years ago)
- Last Synced: 2025-03-27T17:46:51.349Z (over 1 year ago)
- Topics: machine-learning
- Language: Python
- Homepage:
- Size: 2.25 MB
- Stars: 33
- Watchers: 2
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
README
==========
# Introduction
Juneberry improves the experience of machine learning experimentation by providing a framework for automating
the training, evaluation, and comparison of multiple models against multiple datasets, thereby reducing errors and
improving reproducibility.
This README describes how to use the Juneberry framework to execute machine learning tasks. Juneberry follows a (mostly)
declarative programming model composed of sets of config files (dataset, model, and experiment configurations) and
Python plugins for features such as model construction and transformation.
If you're looking for a slightly more in depth description of Juneberry see [What Is Juneberry](docs/whatis.md).
Other resources can be found at the [Juneberry Home Page](https://www.sei.cmu.edu/our-work/projects/display.cfm?customel_datapageid_4050=334902)
# Supporting Documentation
## How to Install Juneberry
The [Getting Started](docs/getting_started.md) documentation explains how to install Juneberry. It also
includes a simple test command you can use to verify the installation.
## Experiment Overview
The [Workspace and Experiment Overview](docs/overview.md) documentation contains information about
the structure of the Juneberry workspace and how to organize experiments.
## Experiment Tutorial
The [Juneberry Basic Tutorial](docs/tutorial.md) describes how to create a model, train the model,
and run an experiment.
## Configuring Juneberry
The [Juneberry Configuration Guide](docs/configuring.md) describes various ways to configure Juneberry.
## Known Warnings
During normal use of Juneberry, you may encounter warning messages. The
[Known Warnings in Juneberry](docs/known_warnings.md) documentation contains information about known warning
messages and what (if anything) should be done about them.
## Further Reading
The [vignettes](docs/vignettes) directory contains detailed walkthroughs of various Juneberry tasks.
The vignettes provide helpful examples of how to construct various Juneberry configuration files,
including datasets, models, and experiments. A good start is
[Replicating a Classic Machine Learning Result with Juneberry](docs/vignettes/vignette1/Replicating_a_Classic_Machine_Learning_Result_with_Juneberry.md).
# Copyright
Copyright 2022 Carnegie Mellon University. See LICENSE.txt file for license terms.