mlflow gcp experimentation

train.py

import os 
os.environ['GOOGLE_APPLICATION_CREDENTIALS']='<<path_to_service_account_json'
# set tracking uri to mlflow server running on cloud
mlflow.set_tracking_uri('https://mlflowtracker-gcp-XXXXXX-uc.a.run.app')
mlflow.set_experiment('term_deposit')

with mlflow.start_run():
    params = {
        "n_estimator": 120,
        "learning_rate": 0.3
    }
    #log experiment parameters
    mlflow.log_param(key='model_params',value=params)
    
    model = GradientBoostingClassifier(n_estimators=120, learning_rate=0.3, random_state=7)
    model.fit(X_train, y_train)
    # Get predictions
    y_predict = model.predict(X_test)
    metrics = {
        'precision_score': precision_score(y_true=y_test, y_pred=y_predict),
        'recall_score': recall_score(y_true=y_test, y_pred=y_predict),
        'f1_score': f1_score(y_true=y_test, y_pred=y_predict),
        'accuracy_score': accuracy_score(y_true=y_test, y_pred=y_predict)
    }
    
    #log model metrics
    mlflow.log_metrics(metrics)
    
    # Save model
    mlflow.sklearn.log_model(model, 'term_deposit')