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AutoMLModel

Overview

The 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 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.

Summary Status

Model Deployment

Prepare

The prepare step is performed by the .prepare() method. It creates several customized files that are used to run the model once it is deployed. These include:

  • input_schema.json: A JSON file that defines the nature of the feature data. It includes information about the features. This includes metadata such as the data type, name, constraints, summary statistics, and feature type.
  • model.pkl: The default file name of the serialized model. You can change the file name with the model_file_name attribute. By default, the model is stored in a pickle file. To save your file in an ONNX format, use the as_onnx parameter.
  • output_schema.json: A JSON file that defines the dependent variable. This file includes metadata for the dependent variable, such as the data type, name, constraints, summary statistics, and feature type.
  • runtime.yaml: This file contains information needed to set up the runtime environment on the deployment server. It includes information about the conda environment used to train the model, the environment for deploying the model, and the Python version to use.
  • score.py: This script contains the load_model() and predict() functions. The load_model() function understands the format of the saved model and loads it into memory. The predict() function makes inferences for the deployed model. You can add hooks to perform operations before and after the inference. You can also modify this script with your specifics.

To create the model artifacts, use the .prepare() method. The .prepare() method includes parameters for storing model provenance information.

Verify

  • data (Union[dict, str]): The data is used to test if deployment works in the local environment.

Save

Deploy

Predict

  • data: Any: JSON serializable data to used for making inferences.

The .predict() and .verify() methods take the same data formats. You must ensure that the data passed into and returned by the predict() function in the score.py file is JSON serializable.

Load

You can restore serialization models from model artifacts, from model deployments or from models in the model catalog. This section provides details on how to restore serialization models.

from ads.model.framework.automl_model import AutoMLModel

model = AutoMLModel.from_model_artifact(
                uri="/folder_to_your/artifact.zip",
                model_file_name="model.pkl",
                artifact_dir="/folder_store_artifact"
            )
from ads.model.framework.automl_model import AutoMLModel

model = AutoMLModel.from_model_catalog(model_id="<model_id>",
                    model_file_name="model.pkl",
                    artifact_dir="/folder_store_artifact")
from ads.model.generic_model import AutoMLModel

model = AutoMLModel.from_model_deployment(
    model_deployment_id="<model_deployment_id>",
    model_file_name="model.pkl",
    artifact_dir=tempfile.mkdtemp())

Delete a Deployment

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
from ads.catalog.model import ModelCatalog

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)
ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model_id)