Logging tuned models
You have been experimenting with different model hyperparameters and need to log your latest round of experiment results to MLflow, let's do it!
이 연습은 강의의 일부입니다
Designing Forecasting Pipelines for Production
연습 안내
- Set the experiment name as
"hyperparameter_tuning". - Loop over the index and rows of
df. - Start an MLflow run.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
# Set the experiment name
experiment_name = "____"
experiment_id = mlflow.create_experiment(experiment_name)
# Loop through the DataFrame
for idx, row in df.____():
# Start a run
with mlflow.____(experiment_id=____):
model_params = ml_models[row["model_label"]].get_params()
model_params["model_name"] = row["model_name"]
model_params["model_label"] = row["model_label"]
model_params["partition"] = row["partition"]
model_params["lags"] = list(range(1, 24))
model_params["date_features"] = ["month", "day", "dayofweek", "week", "hour"]
mlflow.log_params(model_params)
mlflow.log_metric("mape", row["mape"])
mlflow.log_metric("rmse", row["rmse"])