I ran this in Databricks and it worked with no issues. I suggest you make sure your wget path is correct, because the one you posted downloads HTML, not the raw csv. That may cause the problem.

%sh
wget https://raw.githubusercontent.com/mlflow/mlflow-example/master/wine-quality.csv

import h2o import random import mlflow import mlflow.h2o from h2o.estimators.random_forest import H2ORandomForestEstimator

h2o.init() wine = h2o.import_file(path="./wine-quality.csv") r = wine['quality'].runif() train = wine[r < 0.7] test = wine[0.3 <= r]

def train_random_forest(ntrees): with mlflow.start_run(): rf = H2ORandomForestEstimator(ntrees=ntrees) train_cols = [n for n in wine.col_names if n != "quality"] rf.train(train_cols, "quality", training_frame=train, validation_frame=test)

mlflow.log_param("ntrees", ntrees)

mlflow.log_metric("rmse", rf.rmse()) mlflow.log_metric("r2", rf.r2()) mlflow.log_metric("mae", rf.mae())

mlflow.h2o.log_model(rf, "model")

h2o.save_model(rf)

predict = rf.predict(test)

print(predict.head()) for ntrees in [10, 20, 50, 100]: train_random_forest(ntrees