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AutoMLModel

See API Documentation

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 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

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)