mlflow_skinny_quickstart_log_param_and_metric.py

1import mlflow
2
3# Note: mlflow-skinny provides the same 'mlflow' interface but without 
4# heavy dependencies like pandas, numpy, or scikit-learn.
5
6# Set the tracking URI if using a remote server
7# mlflow.set_tracking_uri("http://127.0.0.1:5000")
8
9def main():
10    # Start an MLflow run
11    with mlflow.start_run():
12        # Log a parameter (key-value pair)
13        mlflow.log_param("optimization_level", 1)
14
15        # Log a metric (key-value pair)
16        mlflow.log_metric("loss", 0.01)
17
18        print("Run logged successfully with mlflow-skinny.")
19
20if __name__ == "__main__":
21    main()