Rubicon-ML
Rubicon-ML is an open-source Python package for capturing and reviewing model training and execution information.
What Rubicon-ML is
Rubicon-ML is an open-source Python package for capturing and reviewing model training and execution information. It stores inputs, outputs, parameters and outcomes in a repeatable, searchable form, and its Git integration can connect experiments with a repository origin, branch and commit. A dashboard and widgets let users explore, filter, visualize, compare and share logged work. The package supports parallel experiment logging and asynchronous communication with S3. Documented storage backends include a local filesystem, Amazon S3, in-memory storage and Weights & Biases. Integrations include Git, Prefect and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Install it with pip or conda-forge. Visualization tools are an optional pip extra, while conda installs optional extras by default. Its dashboard is a Dash app that can be dockerized, and the FAQ says remote logging does not require a hosted server. Rubicon-ML is licensed under Apache License 2.0 and is listed as free.
Good
- Records experiment inputs, outputs and parameters.
- Git integration supports auditability and reproducibility.
- Supports parallel experiment logging.
- Includes Prefect and Scikit-learn integrations.
- Does not require a hosted server for remote logging.
Watch out
- The Weights & Biases backend is experimental.
- That backend lacks project-level artifacts and dataframes.
- Visualization tools are an optional pip extra.
Who it suits
Rubicon-ML suits developers and science or research users who want to log and review model experiments. It may fit teams that need Git-linked records and pluggable storage choices.
Verdict
Rubicon-ML combines experiment logging with Git context, storage choices and review tools. Note the stated limitations of its experimental Weights & Biases backend.
Plans and prices
From Rubicon-ML's own pricing page, checked 9 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| rubicon-ml | Free | Open-source Python package · install with pip or conda |
The facts
Each one read from Rubicon-ML's own pages, with the date it was checked.
What it does
- Auditability
- Its Git integration associates model inputs and outputs with the model code that produced them to support auditability and reproducibility.checked 9 Oct 2026
- Concurrent and asynchronous logging
- The documentation says rubicon-ml supports logging experiments in parallel and asynchronous communication with S3 so network reads and writes do not block.checked 9 Oct 2026
- Concurrent logging
- Rubicon-ML supports logging multiple experiments in parallel and asynchronous communication with S3.checked 9 Oct 2026
- Dashboard deployment
- The dashboard is a Dash app that can be dockerized like other Dash apps.checked 9 Oct 2026
- Experiment review
- The dashboard lets users explore, filter, visualize, and share logged work with reviewers and stakeholders.checked 9 Oct 2026
- Git audit trail
- Its Git integration can record repository origin, branch names, and commit hashes alongside projects and experiments.checked 9 Oct 2026
- Git integration
- With auto_git_enabled=True, projects can record the GitHub origin URL and experiments can record the active branch and last commit hash.checked 9 Oct 2026
- Installation
- The Python package can be installed using pip or conda-forge.checked 9 Oct 2026
- Integrations
- The documentation includes integrations for Git, Prefect, and Scikit-learn.checked 9 Oct 2026
- Intended users
- The package metadata identifies developers and science or research users as its intended audience.checked 9 Oct 2026
- License
- The project is licensed under Apache License 2.0.checked 9 Oct 2026
- Logging
- The logging component organizes, stores, and retrieves model inputs and outputs using pluggable storage backends powered by fsspec.checked 9 Oct 2026
- No hosted server requirement
- The FAQ says rubicon-ml does not require a hosted server for remote logging and supports local, S3, or in-memory data storage.checked 9 Oct 2026
- Prefect integration
- The Prefect integration adds rubicon-ml logging tasks to Prefect workflows to persist experimentation metadata.checked 9 Oct 2026
- Purpose
- Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way.checked 9 Oct 2026
- Scikit-learn integration
- RubiconPipeline can automatically log parameters and metrics when Scikit-learn pipelines are fit and scored.checked 9 Oct 2026
- Security guidance
- For EC2 S3 logging, the documentation says the IAM role needs s3:GetObject, s3:PutObject, and s3:DeleteObject permissions for the bucket.checked 9 Oct 2026
- Storage backends
- Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases.checked 9 Oct 2026
- Storage options
- Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases.checked 9 Oct 2026
- Support
- The project README directs users to open a GitHub issue to report a bug or make a suggestion.checked 9 Oct 2026
- Visualization
- The dashboard and widgets let users explore and compare logged model metadata.checked 9 Oct 2026
- Visualization dependencies
- The visualization tools are an optional pip extra, while conda installs the optional extras by default.checked 9 Oct 2026
The company
- Founded
- 1994checked 28 Sep 2026
- Headquarters
- McLean, Virginia, United Stateschecked 28 Sep 2026
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