MLflow UI shows the tracking result of the experiments. When we use the log_param or log_metric in ModelClient API, we could view the result in MLflow UI. Below is the example of the usage of MLflow UI.
from submarine import ModelsClient import random import time if __name__ == "__main__": modelClient = ModelsClient() with modelClient.start() as run: modelClient.log_param("learning_rate", random.random()) for i in range(100): time.sleep(1) modelClient.log_metric("mse", random.random() * 100, i) modelClient.log_metric("acc", random.random(), i)