LabML vs Rubicon-ML
LabML
labml.aiRubicon-ML
capitalone.github.ioSide by side
| Figure | LabML | Rubicon-ML |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · ML Experiment Tracking Software | 7.7 (#12) | 8.3 (#7) |
| Founded | 2020 | 1994 |
| Installation | The annotated implementations collection can be installed with `pip install labml-nn`. | The Python package can be installed using pip or conda-forge. |
| License | The LabML repository provides its software under the MIT License, which states that it is provided without warranty. | The project is licensed under Apache License 2.0. |
| Purpose | LabML is an open-source platform for monitoring deep learning experiments and hardware usage. | Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. |
| Support | The LabML app page lists contact email [email protected]. | The project README directs users to open a GitHub issue to report a bug or make a suggestion. |
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.