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Rubicon-ML

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What Rubicon-ML is

Rubicon-ML is a free, open-source Python package for recording and reviewing model training and execution information. It stores model inputs, outputs, parameters, and outcomes in a searchable, repeatable form. Its Git integration can attach repository origin, branch, and commit details to projects and experiments, helping connect recorded results with the code that produced them. A dashboard and widgets let users filter, visualize, compare, and share logged work with reviewers or stakeholders. The package supports parallel experiment logging and asynchronous communication with S3. Storage options include a local filesystem, Amazon S3, in-memory storage, and Weights & Biases. Integrations cover Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Install with pip or conda-forge. Visualization tools are 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. The Weights & Biases backend is experimental and lacks project-level artifacts and dataframes, as well as get_projects() listing. Rubicon-ML is licensed under Apache License 2.0.

Who it suits

Rubicon-ML suits developers and science or research users who want searchable experiment records tied to model code. It supports local and remote storage without requiring a hosted server.

What it does well

Records inputs, outputs, parameters, and outcomes.
Connects experiments with Git branch and commit details.
Supports local filesystem, S3, and in-memory storage.
Integrates with Prefect and Scikit-learn.
Logs experiments in parallel.

What to know first

Weights & Biases backend is experimental.
W&B lacks project-level artifacts and dataframes.
Visualization tools are an optional pip extra.

Limits

The Weights & Biases backend is experimental and does not support project-level artifacts or dataframes, or listing all projects with get_projects(). With pip, visualization tools require an optional extra.

Plans

Plan	Billed and limits	Price
rubicon-ml	Open-source Python package · install with pip or conda	Free

As capitalone.github.io lists them, read 9 Oct 2026. The maker's own page has the final word.

Getting started

Install the Python package with pip or conda-forge.
Choose a documented storage backend: local filesystem, Amazon S3, in-memory storage or Weights & Biases.
For visualization with pip, install the optional visualization extra; conda installs optional extras by default.
Use the dashboard as a Dash app, which can be dockerized.
For EC2 S3 logging, grant the IAM role s3:GetObject, s3:PutObject and s3:DeleteObject permissions for the bucket.

Verdict

Rubicon-ML provides experiment logging, storage choices, and review tools in a Python package that can work without a hosted server. Users considering its Weights & Biases backend should account for the documented missing capabilities.

Questions

How much does Rubicon-ML cost?

The listed plan is free. Rubicon-ML is an open-source package.

Which storage backends does it support?

Documented backends are a local filesystem, Amazon S3, in-memory storage and Weights & Biases. The Weights & Biases backend is experimental and has documented limitations.

Which integrations are included?

The documentation includes Git, Prefect and Scikit-learn integrations. RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored.

Does it require a hosted server for remote logging?

No. It supports local, S3 or in-memory data storage without a hosted server requirement.

How can I install it?

Install the Python package with pip or conda-forge. Visualization tools are an optional pip extra, while conda installs optional extras by default.

What license does Rubicon-ML use?

It is licensed under Apache License 2.0.

Facts from Rubicon-ML's own pages

Auditability: Its Git integration associates model inputs and outputs with the model code that produced them to support auditability and reproducibility. · capitalone.github.io, 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. · capitalone.github.io, 9 Oct 2026
Concurrent logging: Rubicon-ML supports logging multiple experiments in parallel and asynchronous communication with S3. · capitalone.github.io, 9 Oct 2026
Dashboard deployment: The dashboard is a Dash app that can be dockerized like other Dash apps. · capitalone.github.io, 9 Oct 2026
Experiment review: The dashboard lets users explore, filter, visualize, and share logged work with reviewers and stakeholders. · capitalone.github.io, 9 Oct 2026
Founded: 1994 · capitalone.github.io, 28 Sept 2026
Git audit trail: Its Git integration can record repository origin, branch names, and commit hashes alongside projects and experiments. · capitalone.github.io, 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. · capitalone.github.io, 9 Oct 2026
Headquarters: McLean, Virginia, United States · capitalone.github.io, 28 Sept 2026
Installation: The Python package can be installed using pip or conda-forge. · capitalone.github.io, 9 Oct 2026
Integrations: The documentation includes integrations for Git, Prefect, and Scikit-learn. · capitalone.github.io, 9 Oct 2026
Intended users: The package metadata identifies developers and science or research users as its intended audience. · github.com, 9 Oct 2026
License: The project is licensed under Apache License 2.0. · github.com, 9 Oct 2026
Logging: The logging component organizes, stores, and retrieves model inputs and outputs using pluggable storage backends powered by fsspec. · capitalone.github.io, 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. · capitalone.github.io, 9 Oct 2026
Prefect integration: The Prefect integration adds rubicon-ml logging tasks to Prefect workflows to persist experimentation metadata. · capitalone.github.io, 9 Oct 2026
Purpose: Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. · capitalone.github.io, 9 Oct 2026
Scikit-learn integration: RubiconPipeline can automatically log parameters and metrics when Scikit-learn pipelines are fit and scored. · capitalone.github.io, 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. · capitalone.github.io, 9 Oct 2026
Storage backends: Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases. · capitalone.github.io, 9 Oct 2026
Storage options: Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases. · capitalone.github.io, 9 Oct 2026
Support: The project README directs users to open a GitHub issue to report a bug or make a suggestion. · github.com, 9 Oct 2026
Visualization: The dashboard and widgets let users explore and compare logged model metadata. · capitalone.github.io, 9 Oct 2026
Visualization dependencies: The visualization tools are an optional pip extra, while conda installs the optional extras by default. · capitalone.github.io, 9 Oct 2026

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