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#
# 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.
"""Placeholder docstring"""
from __future__ import absolute_import
import logging
from typing import Union, Optional, List, Dict
import packaging.version
import sagemaker
from sagemaker import image_uris, ModelMetrics
from sagemaker.deserializers import JSONDeserializer
from sagemaker.drift_check_baselines import DriftCheckBaselines
from sagemaker.fw_utils import (
model_code_key_prefix,
python_deprecation_warning,
validate_version_or_image_args,
)
from sagemaker.metadata_properties import MetadataProperties
from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
from sagemaker.mxnet import defaults
from sagemaker.predictor import Predictor
from sagemaker.serializers import JSONSerializer
from sagemaker.utils import to_string
from sagemaker.workflow import is_pipeline_variable
from sagemaker.workflow.entities import PipelineVariable
logger = logging.getLogger("sagemaker")
class MXNetPredictor(Predictor):
"""A Predictor for inference against MXNet Endpoints.
This is able to serialize Python lists, dictionaries, and numpy arrays to
multidimensional tensors for MXNet inference.
"""
def __init__(
self,
endpoint_name,
sagemaker_session=None,
serializer=JSONSerializer(),
deserializer=JSONDeserializer(),
):
"""Initialize an ``MXNetPredictor``.
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.
serializer (callable): Optional. Default serializes input data to
json. Handles dicts, lists, and numpy arrays.
deserializer (callable): Optional. Default parses the response using
``json.load(...)``.
"""
super(MXNetPredictor, self).__init__(
endpoint_name,
sagemaker_session,
serializer=serializer,
deserializer=deserializer,
)
class MXNetModel(FrameworkModel):
"""An MXNet SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``."""
_framework_name = "mxnet"
_LOWEST_MMS_VERSION = "1.4.0"
def __init__(
self,
model_data: Union[str, PipelineVariable],
role: str,
entry_point: str,
framework_version: str = _LOWEST_MMS_VERSION,
py_version: Optional[str] = None,
image_uri: Optional[Union[str, PipelineVariable]] = None,
predictor_cls: callable = MXNetPredictor,
model_server_workers: Optional[Union[int, PipelineVariable]] = None,
**kwargs
):
"""Initialize an MXNetModel.
Args:
model_data (str or PipelineVariable): 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. If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
framework_version (str): MXNet version you want to use for executing
your model training code. Defaults to ``1.4.0``. Required unless
``image_uri`` is provided.
py_version (str): Python version you want to use for executing your
model training code. Defaults to ``None``. Required unless
``image_uri`` is provided.
image_uri (str or PipelineVariable): A Docker image URI (default: None).
If not specified, a default image for MXNet will be used.
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If ``image_uri`` is also ``None``, then a ``ValueError``
will be raised.
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 or PipelineVariable): 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 superclass
:class:`~sagemaker.model.FrameworkModel` and, subsequently, its
superclass :class:`~sagemaker.model.Model`.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.model.FrameworkModel` and
:class:`~sagemaker.model.Model`.
"""
validate_version_or_image_args(framework_version, py_version, image_uri)
if py_version == "py2":
logger.warning(
python_deprecation_warning(self._framework_name, defaults.LATEST_PY2_VERSION)
)
self.framework_version = framework_version
self.py_version = py_version
super(MXNetModel, self).__init__(
model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs
)
self.model_server_workers = model_server_workers
def register(
self,
content_types: List[Union[str, PipelineVariable]],
response_types: List[Union[str, PipelineVariable]],
inference_instances: Optional[List[Union[str, PipelineVariable]]] = None,
transform_instances: Optional[List[Union[str, PipelineVariable]]] = None,
model_package_name: Optional[Union[str, PipelineVariable]] = None,
model_package_group_name: Optional[Union[str, PipelineVariable]] = None,
image_uri: Optional[Union[str, PipelineVariable]] = None,
model_metrics: Optional[ModelMetrics] = None,
metadata_properties: Optional[MetadataProperties] = None,
marketplace_cert: bool = False,
approval_status: Optional[Union[str, PipelineVariable]] = None,
description: Optional[str] = None,
drift_check_baselines: Optional[DriftCheckBaselines] = None,
customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
domain: Optional[Union[str, PipelineVariable]] = None,
sample_payload_url: Optional[Union[str, PipelineVariable]] = None,
task: Optional[Union[str, PipelineVariable]] = None,
framework: Optional[Union[str, PipelineVariable]] = None,
framework_version: Optional[Union[str, PipelineVariable]] = None,
nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
):
"""Creates a model package for creating SageMaker models or listing on Marketplace.
Args:
content_types (list[str] or list[PipelineVariable]): The supported MIME types for
the input data.
response_types (list[str] or list[PipelineVariable]): The supported MIME types for
the output data.
inference_instances (list[str] or list[PipelineVariable]): A list of the instance types
that are used to generate inferences in real-time (default: None).
transform_instances (list[str] or list[PipelineVariable]): A list of the instance types
on which a transformation job can be run or on which an endpoint can be deployed
(default: None).
model_package_name (str or PipelineVariable): Model Package name, exclusive to
`model_package_group_name`, using `model_package_name` makes the Model Package
un-versioned (default: None).
model_package_group_name (str or PipelineVariable): Model Package Group name, exclusive
to `model_package_name`, using `model_package_group_name` makes the Model Package
versioned (default: None).
image_uri (str or PipelineVariable): Inference image uri for the container. Model class'
self.image will be used if it is None (default: None).
model_metrics (ModelMetrics): ModelMetrics object (default: None).
metadata_properties (MetadataProperties): MetadataProperties (default: None).
marketplace_cert (bool): A boolean value indicating if the Model Package is certified
for AWS Marketplace (default: False).
approval_status (str or PipelineVariable): Model Approval Status, values can be
"Approved", "Rejected", or "PendingManualApproval"
(default: "PendingManualApproval").
description (str): Model Package description (default: None).
drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
A dictionary of key-value paired metadata properties (default: None).
domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
"NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
sample_payload_url (str or PipelineVariable): The S3 path where the sample payload
is stored (default: None).
task (str or PipelineVariable): Task values which are supported by Inference Recommender
are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
"IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
framework (str or PipelineVariable): Machine learning framework of the model package
container image (default: None).
framework_version (str or PipelineVariable): Framework version of the Model Package
Container Image (default: None).
nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
benchmarked by Amazon SageMaker Inference Recommender (default: None).
data_input_configuration (str or PipelineVariable): Input object for the model
(default: None).
Returns:
A `sagemaker.model.ModelPackage` instance.
"""
instance_type = inference_instances[0] if inference_instances else None
self._init_sagemaker_session_if_does_not_exist(instance_type)
if image_uri:
self.image_uri = image_uri
if not self.image_uri:
self.image_uri = self.serving_image_uri(
region_name=self.sagemaker_session.boto_session.region_name,
instance_type=instance_type,
)
if not is_pipeline_variable(framework):
framework = (framework or self._framework_name).upper()
return super(MXNetModel, self).register(
content_types,
response_types,
inference_instances,
transform_instances,
model_package_name,
model_package_group_name,
image_uri,
model_metrics,
metadata_properties,
marketplace_cert,
approval_status,
description,
drift_check_baselines=drift_check_baselines,
customer_metadata_properties=customer_metadata_properties,
domain=domain,
sample_payload_url=sample_payload_url,
task=task,
framework=framework,
framework_version=framework_version or self.framework_version,
nearest_model_name=nearest_model_name,
data_input_configuration=data_input_configuration,
)
def prepare_container_def(
self, instance_type=None, accelerator_type=None, serverless_inference_config=None
):
"""Return a container definition with framework configuration.
Framework configuration is 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'.
serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
Specifies configuration related to serverless endpoint. Instance type is
not provided in serverless inference. So this is used to find image URIs.
Returns:
dict[str, str]: A container definition object usable with the
CreateModel API.
"""
deploy_image = self.image_uri
if not deploy_image:
if instance_type is None and serverless_inference_config is None:
raise ValueError(
"Must supply either an instance type (for choosing CPU vs GPU) or an image URI."
)
region_name = self.sagemaker_session.boto_session.region_name
deploy_image = self.serving_image_uri(
region_name,
instance_type,
accelerator_type=accelerator_type,
serverless_inference_config=serverless_inference_config,
)
deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
self._upload_code(deploy_key_prefix, self._is_mms_version())
deploy_env = dict(self.env)
deploy_env.update(self._script_mode_env_vars())
if self.model_server_workers:
deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = to_string(
self.model_server_workers
)
return sagemaker.container_def(
deploy_image, self.repacked_model_data or self.model_data, deploy_env
)
def serving_image_uri(
self, region_name, instance_type, accelerator_type=None, serverless_inference_config=None
):
"""Create a URI for the serving image.
Args:
region_name (str): AWS region where the image is uploaded.
instance_type (str): SageMaker instance type. Used to determine device type
(cpu/gpu/family-specific optimized).
accelerator_type (str): The Elastic Inference accelerator type to
deploy to the instance for loading and making inferences to the
model (default: None). For example, 'ml.eia1.medium'.
serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
Specifies configuration related to serverless endpoint. Instance type is
not provided in serverless inference. So this is used to determine device type.
Returns:
str: The appropriate image URI based on the given parameters.
"""
return image_uris.retrieve(
self._framework_name,
region_name,
version=self.framework_version,
py_version=self.py_version,
instance_type=instance_type,
accelerator_type=accelerator_type,
image_scope="inference",
serverless_inference_config=serverless_inference_config,
)
def _is_mms_version(self):
"""Whether the framework version corresponds to an inference image using the MMS.
MMS Server: (https://github.com/awslabs/multi-model-server).
Returns:
bool: If the framework version corresponds to an image using MMS.
"""
lowest_mms_version = packaging.version.Version(self._LOWEST_MMS_VERSION)
framework_version = packaging.version.Version(self.framework_version)
return framework_version >= lowest_mms_version
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