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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"
)