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!
To ćwiczenie jest częścią kursu
Designing Forecasting Pipelines for Production
Instrukcje do ćwiczenia
- Set the experiment name as
"hyperparameter_tuning". - Loop over the index and rows of
df. - Start an MLflow run.
Interaktywne ćwiczenie praktyczne
Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.
# 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"])