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!
Bu egzersiz
Designing Forecasting Pipelines for Production
kursunun bir parçasıdırEgzersiz talimatları
- Set the experiment name as
"hyperparameter_tuning". - Loop over the index and rows of
df. - Start an MLflow run.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# 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"])