genericmodel.rst

Other Frameworks

See API Documentation

Overview

The ads.model.generic_model.GenericModel class in ADS provides an efficient way to serialize almost any model class. This section demonstrates how to use the GenericModel class to prepare model artifacts, verify models, save models to the model catalog, deploy models, and perform predictions on model deployment endpoints.

The GenericModel class works with any unsupported model framework that has a .predict() method. For the most common model classes such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, and AutoML, we recommend that you use the ADS provided, framework-specific serializations models. For example, for a scikit-learn model, use SKLearnmodel. For other models, use the GenericModel class.

These simple steps take your trained model and will deploy it into production with just a few lines of code.

Prepare Model Artifact

Instantiate a GenericModel() object by giving it any model object. It 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 model.
• properties: (ModelProperties, optional): Defaults to None. ModelProperties object required to save and deploy the model.
• serialize: (bool, optional): Defaults to True. If True the model will be serialized into a pickle file. If False, you must set the model_file_name in the .prepare() method, serialize the model manually, and save it in the artifact_dir. You will also need to update the score.py file to work with this model.

Summary Status

Example

By default, the GenericModel serializes to a pickle file. The following example, the user creates a model. In the prepare step, the user saves the model as a pickle file with the name toy_model.pkl. Then the user verifies the model, saves it to the model catalog, deploys the model and makes a prediction. Finally, the user deletes the model deployment and then deletes the model.

import tempfile
from ads.model.generic_model import GenericModel

class Toy:
    def predict(self, x):
        return x ** 2
model = Toy()

generic_model = GenericModel(estimator=model, artifact_dir=tempfile.mkdtemp())
generic_model.summary_status()

generic_model.prepare(
        inference_conda_env="dbexp_p38_cpu_v1",
        model_file_name="toy_model.pkl",
        force_overwrite=True
     )

# Check if the artifacts are generated correctly.
# The verify method invokes the ``predict`` function defined inside ``score.py`` in the artifact_dir
generic_model.verify(2)

# Register the model
model_id = generic_model.save(display_name="Custom Model")

# Deploy and create an endpoint for the XGBoost model
generic_model.deploy(
    display_name="My Custom Model",
    deployment_log_group_id="ocid1.loggroup.oc1.xxx.xxxxx",
    deployment_access_log_id="ocid1.log.oc1.xxx.xxxxx",
    deployment_predict_log_id="ocid1.log.oc1.xxx.xxxxx",
)

print(f"Endpoint: {generic_model.model_deployment.url}")

# Generate prediction by invoking the deployed endpoint
generic_model.predict(2)

# To delete the deployed endpoint uncomment the line below
# generic_model.delete_deployment(wait_for_completion=True)

You can also use the shortcut .prepare_save_deploy() instead of calling .prepare(), .save() and .deploy() seperately.

import tempfile
from ads.model.generic_model import GenericModel

class Toy:
    def predict(self, x):
        return x ** 2
estimator = Toy()

model = GenericModel(estimator=estimator)
model.summary_status()

# If you are running the code inside a notebook session and using a service pack, `inference_conda_env` can be omitted.
model.prepare_save_deploy(inference_conda_env="dbexp_p38_cpu_v1")
model.verify(2)

# Generate prediction by invoking the deployed endpoint
model.predict(2)

# To delete the deployed endpoint uncomment the line below
# model.delete_deployment(wait_for_completion=True)