AutoMLModel

Overview

The ads.model.framework.automl_model.AutoMLModel class in ADS is designed to rapidly get your AutoML model into production. The .prepare() method creates the model artifacts needed to deploy the model without you having to configure it or write code. The .prepare() method serializes the model and generates a runtime.yaml and a score.py file that you can later customize.

The .verify() method simulates a model deployment by calling the load_model() and predict() methods in the score.py file. With the .verify() method, you can debug your score.py file without deploying any models. The .save() method deploys a model artifact to the model catalog. The .deploy() method deploys a model to a REST endpoint.

The following steps take your trained AutoML model and deploy it into production with a few lines of code.

Creating an Oracle Labs AutoML Model

Create an OracleAutoMLProvider object and use it to define how an Oracle Labs AutoML model is trained.

import logging
from ads.automl.driver import AutoML
from ads.automl.provider import OracleAutoMLProvider
from ads.dataset.dataset_browser import DatasetBrowser

ds = DatasetBrowser.sklearn().open("wine").set_target("target")
train, test = ds.train_test_split(test_size=0.1, random_state = 42)

ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR)
oracle_automl = AutoML(train, provider=ml_engine)
model, baseline = oracle_automl.train(
                      model_list=['LogisticRegression', 'DecisionTreeClassifier'],
                      random_state = 42, time_budget = 500)

Initialize

Instantiate an AutoMLModel() object with an AutoML model. Each instance accepts the following parameters:

artifact_dir: str: Artifact directory to store the files needed for deployment.
auth: (Dict, optional): Defaults to None. The default authentication is set using the ads.set_auth API. To override the default, use ads.common.auth.api_keys() or ads.common.auth.resource_principal() and create the appropriate authentication signer and the **kwargs required to instantiate the IdentityClient object.
estimator: (Callable): Trained AutoML model.
properties: (ModelProperties, optional): Defaults to None. The ModelProperties object required to save and deploy a model.

The properties is an instance of the ModelProperties class and has the following predefined fields:

bucket_uri: str
compartment_id: str
deployment_access_log_id: str
deployment_bandwidth_mbps: int
deployment_instance_count: int
deployment_instance_shape: str
deployment_log_group_id: str
deployment_predict_log_id: str
deployment_memory_in_gbs: Union[float, int]
deployment_ocpus: Union[float, int]
inference_conda_env: str
inference_python_version: str
overwrite_existing_artifact: bool
project_id: str
remove_existing_artifact: bool
training_conda_env: str
training_id: str
training_python_version: str
training_resource_id: str
training_script_path: str

By default, properties is populated from the environment variables when not specified. For example, in notebook sessions the environment variables are preset and stored in project id (PROJECT_OCID) and compartment id (NB_SESSION_COMPARTMENT_OCID). So properties populates these environment variables, and uses the values in methods such as .save() and .deploy(). Pass in values to overwrite the defaults. When you use a method that includes an instance of properties, then properties records the values that you pass in. For example, when you pass inference_conda_env into the .prepare() method, then properties records the value. To reuse the properties file in different places, you can export the properties file using the .to_yaml() method then reload it into a different machine using the .from_yaml() method.

Summary Status

You can call the .summary_status() method after a model serialization instance such as AutoMLModel, GenericModel, SklearnModel, TensorFlowModel, or PyTorchModel is created. The .summary_status() method returns a Pandas dataframe that guides you through the entire workflow. It shows which methods are available to call and which ones aren’t. Plus it outlines what each method does. If extra actions are required, it also shows those actions.

The following image displays an example summary status table created after a user initiates a model instance. The table’s Step column displays a Status of Done for the initiate step. And the Details column explains what the initiate step did such as generating a score.py file. The Step column also displays the prepare(), verify(), save(), deploy(), and predict() methods for the model. The Status column displays which method is available next. After the initiate step, the prepare() method is available. The next step is to call the prepare() method.

Example

import logging
import tempfile

from ads.automl.driver import AutoML
from ads.automl.provider import OracleAutoMLProvider
from ads.common.model_metadata import UseCaseType
from ads.dataset.dataset_browser import DatasetBrowser
from ads.model.framework.automl_model import AutoMLModel

ds = DatasetBrowser.sklearn().open("wine").set_target("target")
train, test = ds.train_test_split(test_size=0.1, random_state = 42)

ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR)
oracle_automl = AutoML(train, provider=ml_engine)
model, baseline = oracle_automl.train(
            model_list=['LogisticRegression', 'DecisionTreeClassifier'],
            random_state = 42,
            time_budget = 500
    )

artifact_dir = tempfile.mkdtemp()
automl_model = AutoMLModel(estimator=model, artifact_dir=artifact_dir)
automl_model.prepare(
        inference_conda_env="generalml_p37_cpu_v1",
        training_conda_env="generalml_p37_cpu_v1",
        use_case_type=UseCaseType.BINARY_CLASSIFICATION,
        X_sample=test.X,
        force_overwrite=True,
        training_id=None
    )
automl_model.verify(test.X.iloc[:10])
model_id = automl_model.save(display_name='Demo AutoMLModel model')
deploy = automl_model.deploy(display_name='Demo AutoMLModel deployment')
automl_model.predict(test.X.iloc[:10])
automl_model.delete_deployment(wait_for_completion=True)