Comments (7)
There was some tables from mlflow.
I will try with a clean database and let you know.
Maybe using table prefix could help.
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I also tried the following command:
# optuna storage upgrade --storage $STORAGE_URL
which raised a warning
workspaces/optuna/cli.py:744: UserWarning: Your optuna version seems outdated against the storage version. Please try updating optuna to the latest version by `$ pip install -U optuna`.
and
# optuna create-study --storage $STORAGE_URL
still raises same RuntimeError
from optuna.
Is there any tables in the database when you ran optuna create-study
? Can you create a database from scratch?
from optuna.
I can confirm that this problem doesn't occur with a separate database just for optuna.
from optuna.
Thanks. I think database for optuna should not contain MLFlow's schema. So I don't think the reported issue is not the bug of optuna.
I had a similar example with SQLite:
rm -rf mlruns.db
export MLFLOW_TRACKING_URI=sqlite:///mlruns.db
python mlflow_example.py
optuna create-study --storage $MLFLOW_TRACKING_URI
# Same error message
The python script for mlflow, which is a copied from https://mlflow.org/docs/latest/tracking/tutorials/local-database.html.
import mlflow
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
from sklearn.ensemble import RandomForestRegressor
mlflow.sklearn.autolog()
db = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(db.data, db.target)
# Create and train models.
rf = RandomForestRegressor(n_estimators=100, max_depth=6, max_features=3)
rf.fit(X_train, y_train)
# Use the model to make predictions on the test dataset.
predictions = rf.predict(X_test)
from optuna.
With table prefix this kind of problem should probably be avoided. Maybe that's something which should be considered. Closing for now
from optuna.
How do we use a table prefix? It doesn't mention it here or here
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