Rubicon

Purpose

Rubicon is a data science tool that captures and stores model training and execution information, like parameters and outcomes, in a repeatable and searchable way. Rubicon’s git integration associates these inputs and outputs directly with the model code that produced them to ensure full auditability and reproducibility for both developers and stakeholders alike. While experimenting, the Rubicon dashboard makes it easy to explore, filter, visualize, and share recorded work.

Components

Rubicon is composed of three parts:

A Python library for storing and retrieving model inputs, outputs, and analyses to filesystems that’s powered by fsspec
A dashboard for exploring, comparing, and visualizing logged data built with dash
And a process for sharing a selected subset of logged data with collaborators or reviewers that leverages intake

Workflow

Use the Rubicon library to capture model inputs and outputs over time. It can be easily integrated into existing Python models or pipelines and supports both concurrent logging (so multiple experiments can be logged in parallel) and asynchronous communication with S3 (so network reads and writes won’t block).

Meanwhile, periodically review the logged data within the Rubicon dashboard to steer the model tweaking process in the right direction. The dashboard lets you quickly spot trends by exploring and filtering your logged results and visualizes how the model inputs impacted the model outputs.

When the model is ready for review, Rubicon makes it easy to share specific subsets of the data with model reviewers and stakeholders, giving them the context necessary for a complete model review and approval.

Use

Here’s a simple example:

from rubicon import Rubicon

rubicon = Rubicon(
    persistence="filesystem", root_dir="/rubicon-root", auto_git_enabled=True
)

project = rubicon.create_project(
    "Hello World", description="Using rubicon to track model results over time."
)

experiment = project.log_experiment(
    training_metadata=[SklearnTrainingMetadata("sklearn.datasets", "my-data-set")],
    model_name="My Model Name",
    tags=["my_model_name"],
)

experiment.log_parameter("n_estimators", n_estimators)
experiment.log_parameter("n_features", n_features)
experiment.log_parameter("random_state", random_state)

accuracy = rfc.score(X_test, y_test)
experiment.log_metric("accuracy", accuracy)

Then explore the project by running the dashboard:

rubicon ui --root-dir /rubicon-root

Documentation

For a full overview, visit the docs. If you have suggestions or find a bug, please open an issue.

Install

rubicon is available on Conda Forge via conda and PyPi via pip.

conda config --add channels conda-forge
conda install rubicon-ml

pip install rubicon-ml

Develop

rubicon uses conda to manage environments. First, install conda. Then use conda to setup a development environment:

conda env create -f ci/environment.yml
conda activate rubicon-dev

Testing

The tests are separated into unit and integration tests. They can be run directly in the activated dev environment via pytest tests/unit or pytest tests/integration. Or by simply running pytest to execute all of them.

Note: some integration tests are intentionally marked to control when they are run (i.e. not during cicd). These tests include:

Integration tests that connect to physical filesystems (local, S3). You’ll want to configure the root_dir appropriately for these tests (tests/integration/test_async_rubicon.py, tests/integration/test_rubicon.py). And they can be run with:
```
pytest -m "physical_filesystem_test"
```
Integration tests for the dashboard. To run these integration tests locally, you’ll need to install one of the WebDrivers. To do so, follow the Install instructions in the Dash Testing Docs or install via brew with brew cask install chromedriver. You may have to update your permissions in Security & Privacy to install with brew.
```
pytest -m "dashboard_test"
```
Note: The --headless flag can be added to run the dashboard tests in headless mode.

Code Formatting

Install and configure pre-commit to automatically run black, flake8, and isort during commits:

install pre-commit
run pre-commit install to set up the git hook scripts

Now pre-commit will run automatically on git commit and will ensure consistent code format throughout the project. You can format without committing via pre-commit run or skip these checks with git commit --no-verify.