MLFlow

Local

See local GUI

mlflow ui

Example

import mlflow

mlflow.set_tracking_uri("http://localhost:5000")

Databricks

Log model

import mlflow

import mlflow.sklearn

mlflow.set_experiment(path)

with mlflow.start_run(run_name="default_skl_random_forest_regressor") as run:

mlflow.sklearn.log_model(model, "DefaultRandomForestRegressor")

mlflow.log_params(params)

mlflow.log_metrics({"r2": r2, "mae": mae, "mse": mse, "rmse": rmse, "mape":_mape})

print("run id {}, experiment id {}".format(run.info.run_uuid, run.info.experiment_id))

model = mlflow.sklearn.load_model("runs:/{}/DefaultRandomForestRegressor".format(run.info.run_uuid))

Auto log model

https://docs.microsoft.com/en-us/azure/databricks/_static/notebooks/mlflow/mlflow-quick-start-python.html

import mlflow

mlflow.set_experiment(path)

import mlflow.sklearn

mlflow.sklearn.autolog()

with mlflow.start_run():

model = RandomForestRegressor(n_jobs=-1, random_state=42)

params = model.get_params()

# Set the model parameters

params["n_estimators"] = 1000

model = RandomForestRegressor(**params)

model.fit(X_train, y_train)

yhat = model.predict(X_test)

Register model

Click on the experiment and click register

The path remains the same

Load a model locally from databricks

https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/access-hosted-tracking-server#step-0-configure-your-environment

export MLFLOW_TRACKING_URI="databricks"

export DATABRICKS_HOST="https://eastus2.azuredatabricks.net/"

export DATABRICKS_TOKEN="..." # https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/authentication#token-management

import mlflow.sklearn

model = mlflow.sklearn.load_model("models:/REGISTERED_MODEL_NAME/Production")