pc.create_model

pc.create_model(repository_name, dataset, config, engine=None, repo_description=None, model_description=None)

     Create a model repo and train the first model given a configuration and a dataset.

Parameters:

       repository_name: str
       Name of the model repository to be created.

       dataset: Dataset
       Dataset to train the model on.

       config: dict
       Dictionary config used to train the model.

       engine: Engine, default None
       Engine to use for the training job.
          - If no engine is specified, then the current session set engine is used.

       repo_description: str, default None
       Description for the repository being created.

       model_description: str, default None
       Description for the first model version being trained.

Returns:

       Model

Examples:

Create and train a model with a pandas dataframe using a specific engine.

    import yaml

    with open("~/{path to file}/titanic_config.yaml", 'r') as f:
        try:
            config=yaml.safe_load(f)

    df = pd.read_csv('titanic.csv')
    dataset = pc.create_dataset(df, 'Titanic Dataset')
    engine = pc.get_engine('medium_cpu_engine', activate=True)

    model = pc.create_model(
        "Titanic Repo",
        dataset, config,
        engine=engine,
        repo_description="Predicting Titanic Survivors",
        model_description="Baseline Model"
    )