def _append_register_model_step(self):
"""Create and append a `_RegisterModelStep`"""
register_model_step = _RegisterModelStep(
name="{}-{}".format(self.name, _REGISTER_MODEL_NAME_BASE),
step_args=self._register_model_args,
display_name=self.display_name,
retry_policies=self._register_model_retry_policies,
description=self.description,
)
if not self._need_runtime_repack:
register_model_step.add_depends_on(self.depends_on)
self.steps.append(register_model_step)
def __init__(
self,
name: str,
estimator: EstimatorBase,
model_data,
content_types,
response_types,
inference_instances,
transform_instances,
depends_on: List[str] = None,
model_package_group_name=None,
model_metrics=None,
approval_status=None,
image_uri=None,
compile_model_family=None,
description=None,
**kwargs,
):
"""Construct steps `_RepackModelStep` and `_RegisterModelStep` based on the estimator.
Args:
name (str): The name of the training step.
estimator: The estimator instance.
model_data: The S3 uri to the model data from training.
content_types (list): The supported MIME types for the input data (default: None).
response_types (list): The supported MIME types for the output data (default: None).
inference_instances (list): A list of the instance types that are used to
generate inferences in real-time (default: None).
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 (default: None).
depends_on (List[str]): The list of step names the first step in the collection
depends on
model_package_group_name (str): The Model Package Group name, exclusive to
`model_package_name`, using `model_package_group_name` makes the Model Package
versioned (default: None).
model_metrics (ModelMetrics): ModelMetrics object (default: None).
approval_status (str): Model Approval Status, values can be "Approved", "Rejected",
or "PendingManualApproval" (default: "PendingManualApproval").
image_uri (str): The container image uri for Model Package, if not specified,
Estimator's training container image is used (default: None).
compile_model_family (str): The instance family for the compiled model. If
specified, a compiled model is used (default: None).
description (str): Model Package description (default: None).
**kwargs: additional arguments to `create_model`.
"""
steps: List[Step] = []
repack_model = False
if "entry_point" in kwargs:
repack_model = True
entry_point = kwargs["entry_point"]
source_dir = kwargs.get("source_dir")
dependencies = kwargs.get("dependencies")
repack_model_step = _RepackModelStep(
name=f"{name}RepackModel",
depends_on=depends_on,
estimator=estimator,
model_data=model_data,
entry_point=entry_point,
source_dir=source_dir,
dependencies=dependencies,
)
steps.append(repack_model_step)
model_data = repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
# remove kwargs consumed by model repacking step
kwargs.pop("entry_point", None)
kwargs.pop("source_dir", None)
kwargs.pop("dependencies", None)
register_model_step = _RegisterModelStep(
name=name,
estimator=estimator,
model_data=model_data,
content_types=content_types,
response_types=response_types,
inference_instances=inference_instances,
transform_instances=transform_instances,
model_package_group_name=model_package_group_name,
model_metrics=model_metrics,
approval_status=approval_status,
image_uri=image_uri,
compile_model_family=compile_model_family,
description=description,
**kwargs,
)
if not repack_model:
register_model_step.add_depends_on(depends_on)
steps.append(register_model_step)
self.steps = steps
def __init__(
self,
name: str,
content_types,
response_types,
inference_instances,
transform_instances,
estimator: EstimatorBase = None,
model_data=None,
depends_on: Optional[List[Union[str, Step, StepCollection]]] = None,
repack_model_step_retry_policies: List[RetryPolicy] = None,
register_model_step_retry_policies: List[RetryPolicy] = None,
model_package_group_name=None,
model_metrics=None,
approval_status=None,
image_uri=None,
compile_model_family=None,
display_name=None,
description=None,
tags=None,
model: Union[Model, PipelineModel] = None,
drift_check_baselines=None,
customer_metadata_properties=None,
**kwargs,
):
"""Construct steps `_RepackModelStep` and `_RegisterModelStep` based on the estimator.
Args:
name (str): The name of the training step.
estimator: The estimator instance.
model_data: The S3 uri to the model data from training.
content_types (list): The supported MIME types for the input data (default: None).
response_types (list): The supported MIME types for the output data (default: None).
inference_instances (list): A list of the instance types that are used to
generate inferences in real-time (default: None).
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 (default: None).
depends_on (List[Union[str, Step, StepCollection]]): The list of `Step`/`StepCollection`
names or `Step` instances or `StepCollection` instances that the first step
in the collection depends on (default: None).
repack_model_step_retry_policies (List[RetryPolicy]): The list of retry policies
for the repack model step
register_model_step_retry_policies (List[RetryPolicy]): The list of retry policies
for register model step
model_package_group_name (str): The Model Package Group name or Arn, exclusive to
`model_package_name`, using `model_package_group_name` makes the Model Package
versioned (default: None).
model_metrics (ModelMetrics): ModelMetrics object (default: None).
approval_status (str): Model Approval Status, values can be "Approved", "Rejected",
or "PendingManualApproval" (default: "PendingManualApproval").
image_uri (str): The container image uri for Model Package, if not specified,
Estimator's training container image is used (default: None).
compile_model_family (str): The instance family for the compiled model. If
specified, a compiled model is used (default: None).
description (str): Model Package description (default: None).
tags (List[dict[str, str]]): The list of tags to attach to the model package group. Note
that tags will only be applied to newly created model package groups; if the
name of an existing group is passed to "model_package_group_name",
tags will not be applied.
model (object or Model): A PipelineModel object that comprises a list of models
which gets executed as a serial inference pipeline or a Model object.
drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
customer_metadata_properties (dict[str, str]): A dictionary of key-value paired
metadata properties (default: None).
**kwargs: additional arguments to `create_model`.
"""
self.name = name
steps: List[Step] = []
repack_model = False
self.model_list = None
self.container_def_list = None
subnets = None
security_group_ids = None
if estimator is not None:
subnets = estimator.subnets
security_group_ids = estimator.security_group_ids
elif model is not None and model.vpc_config is not None:
subnets = model.vpc_config["Subnets"]
security_group_ids = model.vpc_config["SecurityGroupIds"]
if "entry_point" in kwargs:
repack_model = True
entry_point = kwargs.pop("entry_point", None)
source_dir = kwargs.pop("source_dir", None)
dependencies = kwargs.pop("dependencies", None)
kwargs = dict(**kwargs, output_kms_key=kwargs.pop("model_kms_key", None))
repack_model_step = _RepackModelStep(
name=f"{name}RepackModel",
depends_on=depends_on,
retry_policies=repack_model_step_retry_policies,
sagemaker_session=estimator.sagemaker_session,
role=estimator.role,
model_data=model_data,
entry_point=entry_point,
source_dir=source_dir,
dependencies=dependencies,
tags=tags,
subnets=subnets,
security_group_ids=security_group_ids,
description=description,
display_name=display_name,
**kwargs,
)
steps.append(repack_model_step)
model_data = repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
# remove kwargs consumed by model repacking step
kwargs.pop("output_kms_key", None)
elif model is not None:
if isinstance(model, PipelineModel):
self.model_list = model.models
elif isinstance(model, Model):
self.model_list = [model]
for model_entity in self.model_list:
if estimator is not None:
sagemaker_session = estimator.sagemaker_session
role = estimator.role
else:
sagemaker_session = model_entity.sagemaker_session
role = model_entity.role
if hasattr(model_entity, "entry_point") and model_entity.entry_point is not None:
repack_model = True
entry_point = model_entity.entry_point
source_dir = model_entity.source_dir
dependencies = model_entity.dependencies
kwargs = dict(**kwargs, output_kms_key=model_entity.model_kms_key)
model_name = model_entity.name or model_entity._framework_name
repack_model_step = _RepackModelStep(
name=f"{model_name}RepackModel",
depends_on=depends_on,
retry_policies=repack_model_step_retry_policies,
sagemaker_session=sagemaker_session,
role=role,
model_data=model_entity.model_data,
entry_point=entry_point,
source_dir=source_dir,
dependencies=dependencies,
tags=tags,
subnets=subnets,
security_group_ids=security_group_ids,
description=description,
display_name=display_name,
**kwargs,
)
steps.append(repack_model_step)
model_entity.model_data = (
repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
)
# remove kwargs consumed by model repacking step
kwargs.pop("output_kms_key", None)
if isinstance(model, PipelineModel):
self.container_def_list = model.pipeline_container_def(inference_instances[0])
elif isinstance(model, Model):
self.container_def_list = [model.prepare_container_def(inference_instances[0])]
register_model_step = _RegisterModelStep(
name=name,
estimator=estimator,
model_data=model_data,
content_types=content_types,
response_types=response_types,
inference_instances=inference_instances,
transform_instances=transform_instances,
model_package_group_name=model_package_group_name,
model_metrics=model_metrics,
drift_check_baselines=drift_check_baselines,
approval_status=approval_status,
image_uri=image_uri,
compile_model_family=compile_model_family,
description=description,
display_name=display_name,
tags=tags,
container_def_list=self.container_def_list,
retry_policies=register_model_step_retry_policies,
customer_metadata_properties=customer_metadata_properties,
**kwargs,
)
if not repack_model:
register_model_step.add_depends_on(depends_on)
steps.append(register_model_step)
self.steps = steps
# TODO: add public document link here once ready
warnings.warn(
(
"We are deprecating the use of RegisterModel. "
"Instead, please use the ModelStep, which simply takes in the step arguments "
"generated by model.register()."
),
DeprecationWarning,
)