![]() Note that while log_model saves environment-specifying files such as conda.yaml and requirements.txt, load_model does not automatically recreate that environment. Import mlflow from sklearn.model_selection import train_test_split from sklearn.datasets import load_diabetes db = load_diabetes () X_train, X_test, y_train, y_test = train_test_split ( db. ( mlflow.start_run() API reference) For example: You get that object by wrapping all of your logging code in a with mlflow.start_run() as run: block. For that, you’ll need the object of type mlflow.ActiveRun for the current run. In addition, if you wish to load the model soon, it may be convenient to output the run’s ID directly to the console. Import os from random import random, randint from mlflow import log_metric, log_param, log_params, log_artifacts if _name_ = "_main_" : # Log a parameter (key-value pair) log_param ( "config_value", randint ( 0, 100 )) # Log a dictionary of parameters log_params (.log_model. This example demonstrates the use of these functions: ![]() Mlflow.log_artifacts, mlflow.log_image, mlflow.log_text Values updated during the run (for instance, accuracy)įiles produced by the run (for instance, model weights) ![]() In addition, or if you are using a library for which autolog is not yet supported, you may use key-value pairs to track:Ĭonstant values (for instance, configuration parameters) fit ( X_train, y_train ) # Use the model to make predictions on the test dataset. rf = RandomForestRegressor ( n_estimators = 100, max_depth = 6, max_features = 3 ) rf. ![]() autolog () db = load_diabetes () X_train, X_test, y_train, y_test = train_test_split ( db. Import mlflow from sklearn.model_selection import train_test_split from sklearn.datasets import load_diabetes from sklearn.ensemble import RandomForestRegressor mlflow. ![]()
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