class PipelineModel(object):
"""A pipeline of SageMaker ``Model``s that can be deployed to an ``Endpoint``."""
def __init__(self,
models,
role,
predictor_cls=None,
name=None,
vpc_config=None,
sagemaker_session=None):
"""Initialize an SageMaker ``Model`` which can be used to build an Inference Pipeline comprising of multiple
model containers.
Args:
models (list[sagemaker.Model]): For using multiple containers to build an inference pipeline,
you can pass a list of ``sagemaker.Model`` objects in the order you want the inference to happen.
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.
predictor_cls (callable[string, sagemaker.session.Session]): A function to call to create
a predictor (default: None). If not None, ``deploy`` will return the result of invoking
this function on the created endpoint name.
name (str): The model name. If None, a default model name will be selected on each ``deploy``.
vpc_config (dict[str, list[str]]): The VpcConfig set on the model (default: None)
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker
interactions (default: None). If not specified, one is created using the default AWS configuration chain.
"""
self.models = models
self.role = role
self.predictor_cls = predictor_cls
self.name = name
self.vpc_config = vpc_config
self.sagemaker_session = sagemaker_session
self._model_name = None
def pipeline_container_def(self, instance_type):
"""Return a dict created by ``sagemaker.pipeline_container_def()`` for deploying this model to a specified
instance type.
Subclasses can override this to provide custom container definitions for
deployment to a specific instance type. Called by ``deploy()``.
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
Returns:
list[dict[str, str]]: A list of container definition objects usable with the CreateModel API in the scenario
of multiple containers (Inference Pipeline).
"""
return sagemaker.pipeline_container_def(self.models, instance_type)
def deploy(self,
initial_instance_count,
instance_type,
endpoint_name=None,
tags=None):
"""Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
If ``self.predictor_cls`` is not None, this method returns a the result of invoking
``self.predictor_cls`` on the created endpoint name.
The name of the created model is accessible in the ``name`` field of this ``Model`` after deploy returns
The name of the created endpoint is accessible in the ``endpoint_name``
field of this ``Model`` after deploy returns.
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
initial_instance_count (int): The initial number of instances to run in the
``Endpoint`` created from this ``Model``.
endpoint_name (str): The name of the endpoint to create (default: None).
If not specified, a unique endpoint name will be created.
tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
Returns:
callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
the created endpoint name, if ``self.predictor_cls`` is not None. Otherwise, return None.
"""
if not self.sagemaker_session:
self.sagemaker_session = Session()
containers = self.pipeline_container_def(instance_type)
self.name = self.name or name_from_image(containers[0]['Image'])
self.sagemaker_session.create_model(self.name,
self.role,
containers,
vpc_config=self.vpc_config)
production_variant = sagemaker.production_variant(
self.name, instance_type, initial_instance_count)
self.endpoint_name = endpoint_name or self.name
self.sagemaker_session.endpoint_from_production_variants(
self.endpoint_name, [production_variant], tags)
if self.predictor_cls:
return self.predictor_cls(self.endpoint_name,
self.sagemaker_session)
def delete_model(self):
"""Delete the SageMaker model backing this pipeline model. This does not delete the list of SageMaker models used
in multiple containers to build the inference pipeline.
"""
if self.name is None:
raise ValueError(
'The SageMaker model must be created before attempting to delete.'
)
self.sagemaker_session.delete_model(self.name)
class PipelineModel(object):
"""A pipeline of SageMaker
``Model``s that can be deployed to an ``Endpoint``.
"""
def __init__(self,
models,
role,
predictor_cls=None,
name=None,
vpc_config=None,
sagemaker_session=None):
"""Initialize an SageMaker ``Model`` which can be used to build an
Inference Pipeline comprising of multiple model containers.
Args:
models (list[sagemaker.Model]): For using multiple containers to
build an inference pipeline, you can pass a list of ``sagemaker.Model`` objects
in the order you want the inference to happen.
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.
predictor_cls (callable[string, sagemaker.session.Session]): A
function to call to create a predictor (default: None). If not
None, ``deploy`` will return the result of invoking this
function on the created endpoint name.
name (str): The model name. If None, a default model name will be
selected on each ``deploy``.
vpc_config (dict[str, list[str]]): The VpcConfig set on the model
(default: None)
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
"""
self.models = models
self.role = role
self.predictor_cls = predictor_cls
self.name = name
self.vpc_config = vpc_config
self.sagemaker_session = sagemaker_session
self._model_name = None
self.endpoint_name = None
def pipeline_container_def(self, instance_type):
"""Return a dict created by ``sagemaker.pipeline_container_def()`` for
deploying this model to a specified instance type.
Subclasses can override this to provide custom container definitions
for deployment to a specific instance type. Called by ``deploy()``.
Args:
instance_type (str): The EC2 instance type to deploy this Model to.
For example, 'ml.p2.xlarge'.
Returns:
list[dict[str, str]]: A list of container definition objects usable
with the CreateModel API in the scenario of multiple containers
(Inference Pipeline).
"""
return sagemaker.pipeline_container_def(self.models, instance_type)
def deploy(
self,
initial_instance_count,
instance_type,
endpoint_name=None,
tags=None,
wait=True,
update_endpoint=False,
):
"""Deploy this ``Model`` to an ``Endpoint`` and optionally return a
``Predictor``.
Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None,
this method returns a the result of invoking ``self.predictor_cls`` on
the created endpoint name.
The name of the created model is accessible in the ``name`` field of
this ``Model`` after deploy returns
The name of the created endpoint is accessible in the
``endpoint_name`` field of this ``Model`` after deploy returns.
Args:
initial_instance_count (int): The initial number of instances to run
in the ``Endpoint`` created from this ``Model``.
instance_type (str): The EC2 instance type to deploy this Model to.
For example, 'ml.p2.xlarge'.
endpoint_name (str): The name of the endpoint to create (default:
None). If not specified, a unique endpoint name will be created.
tags (List[dict[str, str]]): The list of tags to attach to this
specific endpoint.
wait (bool): Whether the call should wait until the deployment of
model completes (default: True).
update_endpoint (bool): Flag to update the model in an existing
Amazon SageMaker endpoint. If True, this will deploy a new
EndpointConfig to an already existing endpoint and delete
resources corresponding to the previous EndpointConfig. If
False, a new endpoint will be created. Default: False
Returns:
callable[string, sagemaker.session.Session] or None: Invocation of
``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls``
is not None. Otherwise, return None.
"""
if not self.sagemaker_session:
self.sagemaker_session = Session()
containers = self.pipeline_container_def(instance_type)
self.name = self.name or name_from_image(containers[0]["Image"])
self.sagemaker_session.create_model(self.name,
self.role,
containers,
vpc_config=self.vpc_config)
production_variant = sagemaker.production_variant(
self.name, instance_type, initial_instance_count)
self.endpoint_name = endpoint_name or self.name
if update_endpoint:
endpoint_config_name = self.sagemaker_session.create_endpoint_config(
name=self.name,
model_name=self.name,
initial_instance_count=initial_instance_count,
instance_type=instance_type,
tags=tags,
)
self.sagemaker_session.update_endpoint(self.endpoint_name,
endpoint_config_name)
else:
self.sagemaker_session.endpoint_from_production_variants(
self.endpoint_name, [production_variant], tags, wait=wait)
if self.predictor_cls:
return self.predictor_cls(self.endpoint_name,
self.sagemaker_session)
return None
def _create_sagemaker_pipeline_model(self, instance_type):
"""Create a SageMaker Model Entity
Args:
instance_type (str): The EC2 instance type that this Model will be
used for, this is only used to determine if the image needs GPU
support or not.
"""
if not self.sagemaker_session:
self.sagemaker_session = Session()
containers = self.pipeline_container_def(instance_type)
self.name = self.name or name_from_image(containers[0]["Image"])
self.sagemaker_session.create_model(self.name,
self.role,
containers,
vpc_config=self.vpc_config)
def transformer(
self,
instance_count,
instance_type,
strategy=None,
assemble_with=None,
output_path=None,
output_kms_key=None,
accept=None,
env=None,
max_concurrent_transforms=None,
max_payload=None,
tags=None,
volume_kms_key=None,
):
"""Return a ``Transformer`` that uses this Model.
Args:
instance_count (int): Number of EC2 instances to use.
instance_type (str): Type of EC2 instance to use, for example,
'ml.c4.xlarge'.
strategy (str): The strategy used to decide how to batch records in
a single request (default: None). Valid values: 'MULTI_RECORD'
and 'SINGLE_RECORD'.
assemble_with (str): How the output is assembled (default: None).
Valid values: 'Line' or 'None'.
output_path (str): S3 location for saving the transform result. If
not specified, results are stored to a default bucket.
output_kms_key (str): Optional. KMS key ID for encrypting the
transform output (default: None).
accept (str): The content type accepted by the endpoint deployed
during the transform job.
env (dict): Environment variables to be set for use during the
transform job (default: None).
max_concurrent_transforms (int): The maximum number of HTTP requests
to be made to each individual transform container at one time.
max_payload (int): Maximum size of the payload in a single HTTP
request to the container in MB.
tags (list[dict]): List of tags for labeling a transform job. If
none specified, then the tags used for the training job are used
for the transform job.
volume_kms_key (str): Optional. KMS key ID for encrypting the volume
attached to the ML compute instance (default: None).
"""
self._create_sagemaker_pipeline_model(instance_type)
return Transformer(
self.name,
instance_count,
instance_type,
strategy=strategy,
assemble_with=assemble_with,
output_path=output_path,
output_kms_key=output_kms_key,
accept=accept,
max_concurrent_transforms=max_concurrent_transforms,
max_payload=max_payload,
env=env,
tags=tags,
base_transform_job_name=self.name,
volume_kms_key=volume_kms_key,
sagemaker_session=self.sagemaker_session,
)
def delete_model(self):
"""Delete the SageMaker model backing this pipeline model. This does not
delete the list of SageMaker models used in multiple containers to build
the inference pipeline.
"""
if self.name is None:
raise ValueError(
"The SageMaker model must be created before attempting to delete."
)
self.sagemaker_session.delete_model(self.name)
class PipelineModel(object):
"""A pipeline of SageMaker `Model` instances.
This pipeline can be deployed as an `Endpoint` on SageMaker.
"""
def __init__(
self,
models,
role,
predictor_cls=None,
name=None,
vpc_config=None,
sagemaker_session=None,
enable_network_isolation=False,
):
"""Initialize a SageMaker `Model` instance.
The `Model` can be used to build an Inference Pipeline comprising of
multiple model containers.
Args:
models (list[sagemaker.Model]): For using multiple containers to
build an inference pipeline, you can pass a list of
``sagemaker.Model`` objects in the order you want the inference
to happen.
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.
predictor_cls (callable[string, sagemaker.session.Session]): A
function to call to create a predictor (default: None). If not
None, ``deploy`` will return the result of invoking this
function on the created endpoint name.
name (str): The model name. If None, a default model name will be
selected on each ``deploy``.
vpc_config (dict[str, list[str]]): The VpcConfig set on the model
(default: None)
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
enable_network_isolation (bool): Default False. if True, enables
network isolation in the endpoint, isolating the model
container. No inbound or outbound network calls can be made to
or from the model container.Boolean
"""
self.models = models
self.role = role
self.predictor_cls = predictor_cls
self.name = name
self.vpc_config = vpc_config
self.sagemaker_session = sagemaker_session
self.enable_network_isolation = enable_network_isolation
self.endpoint_name = None
def pipeline_container_def(self, instance_type):
"""The pipeline definition for deploying this model.
This is the dict created by ``sagemaker.pipeline_container_def()``.
The instance type to be used may be specified.
Subclasses can override this to provide custom container definitions
for deployment to a specific instance type. Called by ``deploy()``.
Args:
instance_type (str): The EC2 instance type to deploy this Model to.
For example, 'ml.p2.xlarge'.
Returns:
list[dict[str, str]]: A list of container definition objects usable
with the CreateModel API in the scenario of multiple containers
(Inference Pipeline).
"""
return sagemaker.pipeline_container_def(self.models, instance_type)
def deploy(
self,
initial_instance_count,
instance_type,
serializer=None,
deserializer=None,
endpoint_name=None,
tags=None,
wait=True,
update_endpoint=False,
data_capture_config=None,
kms_key=None,
):
"""Deploy the ``Model`` to an ``Endpoint``.
It optionally return a ``Predictor``.
Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None,
this method returns a the result of invoking ``self.predictor_cls`` on
the created endpoint name.
The name of the created model is accessible in the ``name`` field of
this ``Model`` after deploy returns
The name of the created endpoint is accessible in the
``endpoint_name`` field of this ``Model`` after deploy returns.
Args:
initial_instance_count (int): The initial number of instances to run
in the ``Endpoint`` created from this ``Model``.
instance_type (str): The EC2 instance type to deploy this Model to.
For example, 'ml.p2.xlarge'.
serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
serializer object, used to encode data for an inference endpoint
(default: None). If ``serializer`` is not None, then
``serializer`` will override the default serializer. The
default serializer is set by the ``predictor_cls``.
deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
deserializer object, used to decode data from an inference
endpoint (default: None). If ``deserializer`` is not None, then
``deserializer`` will override the default deserializer. The
default deserializer is set by the ``predictor_cls``.
endpoint_name (str): The name of the endpoint to create (default:
None). If not specified, a unique endpoint name will be created.
tags (List[dict[str, str]]): The list of tags to attach to this
specific endpoint.
wait (bool): Whether the call should wait until the deployment of
model completes (default: True).
update_endpoint (bool): Flag to update the model in an existing
Amazon SageMaker endpoint. If True, this will deploy a new
EndpointConfig to an already existing endpoint and delete
resources corresponding to the previous EndpointConfig. If
False, a new endpoint will be created. Default: False
data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies
configuration related to Endpoint data capture for use with
Amazon SageMaker Model Monitoring. Default: None.
kms_key (str): The ARN, Key ID or Alias of the KMS key that is used to
encrypt the data on the storage volume attached to the instance hosting
the endpoint.
Returns:
callable[string, sagemaker.session.Session] or None: Invocation of
``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls``
is not None. Otherwise, return None.
"""
if not self.sagemaker_session:
self.sagemaker_session = Session()
containers = self.pipeline_container_def(instance_type)
self.name = self.name or name_from_image(containers[0]["Image"])
self.sagemaker_session.create_model(
self.name,
self.role,
containers,
vpc_config=self.vpc_config,
enable_network_isolation=self.enable_network_isolation,
)
production_variant = sagemaker.production_variant(
self.name, instance_type, initial_instance_count)
self.endpoint_name = endpoint_name or self.name
data_capture_config_dict = None
if data_capture_config is not None:
data_capture_config_dict = data_capture_config._to_request_dict()
if update_endpoint:
endpoint_config_name = self.sagemaker_session.create_endpoint_config(
name=self.name,
model_name=self.name,
initial_instance_count=initial_instance_count,
instance_type=instance_type,
tags=tags,
kms_key=kms_key,
data_capture_config_dict=data_capture_config_dict,
)
self.sagemaker_session.update_endpoint(self.endpoint_name,
endpoint_config_name,
wait=wait)
else:
self.sagemaker_session.endpoint_from_production_variants(
name=self.endpoint_name,
production_variants=[production_variant],
tags=tags,
kms_key=kms_key,
wait=wait,
data_capture_config_dict=data_capture_config_dict,
)
if self.predictor_cls:
predictor = self.predictor_cls(self.endpoint_name,
self.sagemaker_session)
if serializer:
predictor.serializer = serializer
if deserializer:
predictor.deserializer = deserializer
return predictor
return None
@runnable_by_pipeline
def create(self, instance_type: str):
"""Create a SageMaker Model Entity
Args:
instance_type (str): The EC2 instance type that this Model will be
used for, this is only used to determine if the image needs GPU
support or not.
"""
self._create_sagemaker_pipeline_model(instance_type)
def _create_sagemaker_pipeline_model(self, instance_type):
"""Create a SageMaker Model Entity
Args:
instance_type (str): The EC2 instance type that this Model will be
used for, this is only used to determine if the image needs GPU
support or not.
"""
if not self.sagemaker_session:
self.sagemaker_session = Session()
containers = self.pipeline_container_def(instance_type)
self.name = self.name or name_from_image(containers[0]["Image"])
create_model_args = dict(
name=self.name,
role=self.role,
container_defs=containers,
vpc_config=self.vpc_config,
enable_network_isolation=self.enable_network_isolation,
)
self.sagemaker_session.create_model(**create_model_args)
@runnable_by_pipeline
def register(
self,
content_types: list,
response_types: list,
inference_instances: list,
transform_instances: list,
model_package_name: Optional[str] = None,
model_package_group_name: Optional[str] = None,
image_uri: Optional[str] = None,
model_metrics: Optional[ModelMetrics] = None,
metadata_properties: Optional[MetadataProperties] = None,
marketplace_cert: bool = False,
approval_status: Optional[str] = None,
description: Optional[str] = None,
drift_check_baselines: Optional[DriftCheckBaselines] = None,
customer_metadata_properties: Optional[Dict[str, str]] = None,
):
"""Creates a model package for creating SageMaker models or listing on Marketplace.
Args:
content_types (list): The supported MIME types for the input data.
response_types (list): The supported MIME types for the output data.
inference_instances (list): A list of the instance types that are used to
generate inferences in real-time.
transform_instances (list): A list of the instance types on which a transformation
job can be run or on which an endpoint can be deployed.
model_package_name (str): 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): Model Package Group name, exclusive to
`model_package_name`, using `model_package_group_name` makes the Model Package
versioned (default: None).
image_uri (str): 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 object (default: None).
marketplace_cert (bool): A boolean value indicating if the Model Package is certified
for AWS Marketplace (default: False).
approval_status (str): 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]): A dictionary of key-value paired
metadata properties (default: None).
Returns:
A `sagemaker.model.ModelPackage` instance.
"""
for model in self.models:
if model.model_data is None:
raise ValueError(
"SageMaker Model Package cannot be created without model data."
)
if model_package_group_name is not None:
container_def = self.pipeline_container_def(inference_instances[0])
else:
container_def = [{
"Image": image_uri or model.image_uri,
"ModelDataUrl": model.model_data
} for model in self.models]
model_pkg_args = sagemaker.get_model_package_args(
content_types,
response_types,
inference_instances,
transform_instances,
model_package_name=model_package_name,
model_package_group_name=model_package_group_name,
model_metrics=model_metrics,
metadata_properties=metadata_properties,
marketplace_cert=marketplace_cert,
approval_status=approval_status,
description=description,
container_def_list=container_def,
drift_check_baselines=drift_check_baselines,
customer_metadata_properties=customer_metadata_properties,
)
self.sagemaker_session.create_model_package_from_containers(
**model_pkg_args)
def transformer(
self,
instance_count,
instance_type,
strategy=None,
assemble_with=None,
output_path=None,
output_kms_key=None,
accept=None,
env=None,
max_concurrent_transforms=None,
max_payload=None,
tags=None,
volume_kms_key=None,
):
"""Return a ``Transformer`` that uses this Model.
Args:
instance_count (int): Number of EC2 instances to use.
instance_type (str): Type of EC2 instance to use, for example,
'ml.c4.xlarge'.
strategy (str): The strategy used to decide how to batch records in
a single request (default: None). Valid values: 'MultiRecord'
and 'SingleRecord'.
assemble_with (str): How the output is assembled (default: None).
Valid values: 'Line' or 'None'.
output_path (str): S3 location for saving the transform result. If
not specified, results are stored to a default bucket.
output_kms_key (str): Optional. KMS key ID for encrypting the
transform output (default: None).
accept (str): The accept header passed by the client to
the inference endpoint. If it is supported by the endpoint,
it will be the format of the batch transform output.
env (dict): Environment variables to be set for use during the
transform job (default: None).
max_concurrent_transforms (int): The maximum number of HTTP requests
to be made to each individual transform container at one time.
max_payload (int): Maximum size of the payload in a single HTTP
request to the container in MB.
tags (list[dict]): List of tags for labeling a transform job. If
none specified, then the tags used for the training job are used
for the transform job.
volume_kms_key (str): Optional. KMS key ID for encrypting the volume
attached to the ML compute instance (default: None).
"""
self._create_sagemaker_pipeline_model(instance_type)
return Transformer(
self.name,
instance_count,
instance_type,
strategy=strategy,
assemble_with=assemble_with,
output_path=output_path,
output_kms_key=output_kms_key,
accept=accept,
max_concurrent_transforms=max_concurrent_transforms,
max_payload=max_payload,
env=env,
tags=tags,
base_transform_job_name=self.name,
volume_kms_key=volume_kms_key,
sagemaker_session=self.sagemaker_session,
)
def delete_model(self):
"""Delete the SageMaker model backing this pipeline model.
This does not delete the list of SageMaker models used in multiple containers to build
the inference pipeline.
"""
if self.name is None:
raise ValueError(
"The SageMaker model must be created before attempting to delete."
)
self.sagemaker_session.delete_model(self.name)