model.py

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import

import logging

import sagemaker
from sagemaker.fw_utils import model_code_key_prefix, python_deprecation_warning
from sagemaker.fw_registry import default_framework_uri
from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
from sagemaker.sklearn.defaults import SKLEARN_VERSION, SKLEARN_NAME

logger = logging.getLogger('sagemaker')


class SKLearnPredictor(RealTimePredictor):
    """A RealTimePredictor for inference against Scikit-learn Endpoints.

    This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Scikit-learn
    inference."""

    def __init__(self, endpoint_name, sagemaker_session=None):
        """Initialize an ``SKLearnPredictor``.

        Args:
            endpoint_name (str): The name of the endpoint to perform inference on.
            sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one
                using the default AWS configuration chain.
        """
        super(SKLearnPredictor, self).__init__(endpoint_name, sagemaker_session, npy_serializer, numpy_deserializer)


class SKLearnModel(FrameworkModel):
    """An Scikit-learn SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``."""

    __framework_name__ = SKLEARN_NAME

    def __init__(self, model_data, role, entry_point, image=None, py_version='py3', framework_version=SKLEARN_VERSION,
                 predictor_cls=SKLearnPredictor, model_server_workers=None, **kwargs):
        """Initialize an SKLearnModel.

        Args:
            model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file.
            role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
                that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
                After the endpoint is created, the inference code might use the IAM role,
                if it needs to access an AWS resource.
            entry_point (str): Path (absolute or relative) to the Python source file which should be executed
                as the entry point to model hosting. This should be compatible with either Python 2.7 or Python 3.5.
            image (str): A Docker image URI (default: None). If not specified, a default image for Scikit-learn
                will be used.
            py_version (str): Python version you want to use for executing your model training code (default: 'py2').
            framework_version (str): Scikit-learn version you want to use for executing your model training code.
            predictor_cls (callable[str, sagemaker.session.Session]): A function to call to create a predictor
                with an endpoint name and SageMaker ``Session``. If specified, ``deploy()`` returns the result of
                invoking this function on the created endpoint name.
            model_server_workers (int): Optional. The number of worker processes used by the inference server.
                If None, server will use one worker per vCPU.
            **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer.
        """
        super(SKLearnModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls,
                                           **kwargs)

        if py_version == 'py2':
            logger.warning(python_deprecation_warning(self.__framework_name__))

        self.py_version = py_version
        self.framework_version = framework_version
        self.model_server_workers = model_server_workers

    def prepare_container_def(self, instance_type, accelerator_type=None):
        """Return a container definition with framework configuration set in model environment variables.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and
                making inferences to the model. For example, 'ml.eia1.medium'. Note: accelerator types are not
                supported by SKLearnModel.

        Returns:
            dict[str, str]: A container definition object usable with the CreateModel API.
        """
        if accelerator_type:
            raise ValueError("Accelerator types are not supported for Scikit-Learn.")

        deploy_image = self.image
        if not deploy_image:
            image_tag = "{}-{}-{}".format(self.framework_version, "cpu", self.py_version)
            deploy_image = default_framework_uri(
                self.__framework_name__,
                self.sagemaker_session.boto_region_name,
                image_tag)

        deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
        self._upload_code(deploy_key_prefix)
        deploy_env = dict(self.env)
        deploy_env.update(self._framework_env_vars())

        if self.model_server_workers:
            deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
        return sagemaker.container_def(deploy_image, self.model_data, deploy_env)