Optuna Integration

[mmccarty]

Is your feature request related to a problem? Please describe
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.

We should investigate ways to integrate with Optuna so that it logs studies and trials to Rubicon experiments automatically.

Describe the solution you'd like
Optuna has a storage module for providing a way to persist hp tuning information. We should review the storage API to consider how it overlaps with the Rubicon client or repositories. Optuna storages look to be fairly plug-able.

Here's an example usage:

from rubicon.optuna import RubiconStorage
import optuna

def objective(trial):
    x = trial.suggest_uniform("x", -10, 10)
    return (x - 2) ** 2

storage = RubiconStorage(persistence="filesystem", root_dir="/rubicon-root", auto_git_enabled=True)
study = optuna.create_study(storage=storage)
study.optimize(study.optimize(objective, n_trials=100)

project = storage.get_project()
print(f"{len(project.experiments)} logged.") 

Additional context
Dask Optuna implements a DaskStorage backend.