Oríon vs Rubicon-ML
Oríon
orion.readthedocs.ioRubicon-ML
capitalone.github.ioSide by side
| Figure | Oríon | Rubicon-ML |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · ML Experiment Tracking Software | 7.1 (#25) | 8.3 (#7) |
| Installation | The installation guide directs users to install Oríon from PyPI with pip install orion. | The Python package can be installed using pip or conda-forge. |
| Intended users | The documentation describes Oríon as useful for machine learning researchers and engineers, while saying it is not itself a machine learning framework, pipeline, or automated machine learning product. | The package metadata identifies developers and science or research users as its intended audience. |
| License | The project documentation says Oríon is licensed under the BSD license. | The project is licensed under Apache License 2.0. |
| Purpose | Oríon is an asynchronous framework for black-box function optimization, including hyperparameter optimization for machine learning. | Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. |
| Support | The project directs users to report questions, issues, bugs, or feature suggestions through its GitHub issue tracker. | The project README directs users to open a GitHub issue to report a bug or make a suggestion. |
| Visualization | The documentation includes experiment visualizations such as regret curves, parallel coordinates, local parameter importance, and partial dependencies. | The dashboard and widgets let users explore and compare logged model metadata. |
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.