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 toNone
. The default authentication is set using theads.set_auth
API. To override the default, useads.common.auth.api_keys()
orads.common.auth.resource_principal()
and create the appropriate authentication signer and the**kwargs
required to instantiate theIdentityClient
object.estimator: (Callable)
: Trained AutoML model.properties: (ModelProperties, optional)
: Defaults toNone
. TheModelProperties
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
: strcompartment_id
: strdeployment_access_log_id
: strdeployment_bandwidth_mbps
: intdeployment_instance_count
: intdeployment_instance_shape
: strdeployment_log_group_id
: strdeployment_predict_log_id
: strdeployment_memory_in_gbs
: Union[float, int]deployment_ocpus
: Union[float, int]inference_conda_env
: strinference_python_version
: stroverwrite_existing_artifact
: boolproject_id
: strremove_existing_artifact
: booltraining_conda_env
: strtraining_id
: strtraining_python_version
: strtraining_resource_id
: strtraining_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_p38_cpu_v1",
training_conda_env="generalml_p38_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)