Rubicon-ML vs Sacred
Rubicon-ML
capitalone.github.ioSacred
github.comSide by side
| Figure | Rubicon-ML | Sacred |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · ML Experiment Tracking Software | 8.3 (#7) | 6.5 (#33) |
| Installation | The Python package can be installed using pip or conda-forge. | The README says Sacred can be installed from PyPI using pip or installed from its Git repository. |
| Integrations | The documentation includes integrations for Git, Prefect, and Scikit-learn. | The README describes a Neptune integration that logs Sacred experiments through an observer. |
| License | The project is licensed under Apache License 2.0. | The project is released under the MIT license. |
| Purpose | Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. | Sacred helps configure, organize, log, and reproduce computational experiments. |
| Support | The project README directs users to open a GitHub issue to report a bug or make a suggestion. | The README directs usage questions to Stack Overflow under the python-sacred tag and invites bug reports and feature requests through GitHub issues. |
Plans
In their own words
Rubicon-ML
Rubicon-ML is a free, open-source Python package for capturing model training and execution details. It records inputs, outputs, parameters, and outcomes in searchable projects and experiments, while Git integration can attach repository origin, branch, and commit information for auditability. Its dashboard and widgets let users explore, filter, visualize, compare, and share logged work. Multiple experiments can be logged in parallel, and asynchronous S3 communication keeps network reads and writes from blocking. Storage backends include local files, Amazon S3, in-memory storage, and Weights & Biases. Integrations are provided for Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Installations use pip or conda-forge. Visualization is an optional pip extra, while conda installs optional extras by default. The dashboard is a Dash app that can be dockerized, and remote logging does not require a hosted server. It is licensed under Apache License 2.0.