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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.
"""This module contains code to create and manage SageMaker ``Context``."""
from __future__ import absolute_import
from datetime import datetime
from typing import Iterator, Optional, List
from sagemaker.apiutils import _base_types
from sagemaker.lineage import (
_api_types,
_utils,
association,
)
from sagemaker.lineage._api_types import ContextSummary
from sagemaker.lineage.query import (
LineageQuery,
LineageFilter,
LineageSourceEnum,
LineageEntityEnum,
LineageQueryDirectionEnum,
)
from sagemaker.lineage.artifact import Artifact
from sagemaker.lineage.action import Action
from sagemaker.lineage.lineage_trial_component import LineageTrialComponent
class Context(_base_types.Record):
"""An Amazon SageMaker context, which is part of a SageMaker lineage.
Attributes:
context_arn (str): The ARN of the context.
context_name (str): The name of the context.
context_type (str): The type of the context.
description (str): A description of the context.
source (obj): The source of the context with a URI and type.
properties (dict): Dictionary of properties.
tags (List[dict[str, str]]): A list of tags to associate with the context.
creation_time (datetime): When the context was created.
created_by (obj): Contextual info on which account created the context.
last_modified_time (datetime): When the context was last modified.
last_modified_by (obj): Contextual info on which account created the context.
"""
context_arn: str = None
context_name: str = None
context_type: str = None
properties: dict = None
tags: list = None
creation_time: datetime = None
created_by: str = None
last_modified_time: datetime = None
last_modified_by: str = None
_boto_load_method: str = "describe_context"
_boto_create_method: str = "create_context"
_boto_update_method: str = "update_context"
_boto_delete_method: str = "delete_context"
_custom_boto_types = {
"source": (_api_types.ContextSource, False),
}
_boto_update_members = [
"context_name",
"description",
"properties",
"properties_to_remove",
]
_boto_delete_members = ["context_name"]
def save(self) -> "Context":
"""Save the state of this Context to SageMaker.
Returns:
obj: boto API response.
"""
return self._invoke_api(self._boto_update_method, self._boto_update_members)
def delete(self, disassociate: bool = False):
"""Delete the context object.
Args:
disassociate (bool): When set to true, disassociate incoming and outgoing association.
Returns:
obj: boto API response.
"""
if disassociate:
_utils._disassociate(
source_arn=self.context_arn, sagemaker_session=self.sagemaker_session
)
_utils._disassociate(
destination_arn=self.context_arn,
sagemaker_session=self.sagemaker_session,
)
return self._invoke_api(self._boto_delete_method, self._boto_delete_members)
def set_tag(self, tag=None):
"""Add a tag to the object.
Args:
tag (obj): Key value pair to set tag.
Returns:
list({str:str}): a list of key value pairs
"""
return self._set_tags(resource_arn=self.context_arn, tags=[tag])
def set_tags(self, tags=None):
"""Add tags to the object.
Args:
tags ([{key:value}]): list of key value pairs.
Returns:
list({str:str}): a list of key value pairs
"""
return self._set_tags(resource_arn=self.context_arn, tags=tags)
@classmethod
def load(cls, context_name: str, sagemaker_session=None) -> "Context":
"""Load an existing context and return an ``Context`` object representing it.
Examples:
.. code-block:: python
from sagemaker.lineage import context
my_context = context.Context.create(
context_name='MyContext',
context_type='Endpoint',
source_uri='arn:aws:...')
my_context.properties["added"] = "property"
my_context.save()
for ctx in context.Context.list():
print(ctx)
my_context.delete()
Args:
context_name (str): Name of the context
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, one is created using the
default AWS configuration chain.
Returns:
Context: A SageMaker ``Context`` object
"""
context = cls._construct(
cls._boto_load_method,
context_name=context_name,
sagemaker_session=sagemaker_session,
)
return context
@classmethod
def create(
cls,
context_name: str = None,
source_uri: str = None,
source_type: str = None,
context_type: str = None,
description: str = None,
properties: dict = None,
tags: dict = None,
sagemaker_session=None,
) -> "Context":
"""Create a context and return a ``Context`` object representing it.
Args:
context_name (str): The name of the context.
source_uri (str): The source URI of the context.
source_type (str): The type of the source.
context_type (str): The type of the context.
description (str): Description of the context.
properties (dict): Metadata associated with the context.
tags (dict): Tags to add to the context.
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, one is created using the
default AWS configuration chain.
Returns:
Context: A SageMaker ``Context`` object.
"""
return super(Context, cls)._construct(
cls._boto_create_method,
context_name=context_name,
source=_api_types.ContextSource(source_uri=source_uri, source_type=source_type),
context_type=context_type,
description=description,
properties=properties,
tags=tags,
sagemaker_session=sagemaker_session,
)
@classmethod
def list(
cls,
source_uri: Optional[str] = None,
context_type: Optional[str] = None,
created_after: Optional[datetime] = None,
created_before: Optional[datetime] = None,
sort_by: Optional[str] = None,
sort_order: Optional[str] = None,
max_results: Optional[int] = None,
next_token: Optional[str] = None,
sagemaker_session=None,
) -> Iterator[ContextSummary]:
"""Return a list of context summaries.
Args:
source_uri (str, optional): A source URI.
context_type (str, optional): An context type.
created_before (datetime.datetime, optional): Return contexts created before this
instant.
created_after (datetime.datetime, optional): Return contexts created after this instant.
sort_by (str, optional): Which property to sort results by.
One of 'SourceArn', 'CreatedBefore', 'CreatedAfter'
sort_order (str, optional): One of 'Ascending', or 'Descending'.
max_results (int, optional): maximum number of contexts to retrieve
next_token (str, optional): token for next page of results
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, one is created using the
default AWS configuration chain.
Returns:
collections.Iterator[ContextSummary]: An iterator
over ``ContextSummary`` objects.
"""
return super(Context, cls)._list(
"list_contexts",
_api_types.ContextSummary.from_boto,
"ContextSummaries",
source_uri=source_uri,
context_type=context_type,
created_before=created_before,
created_after=created_after,
sort_by=sort_by,
sort_order=sort_order,
max_results=max_results,
next_token=next_token,
sagemaker_session=sagemaker_session,
)
def actions(self, direction: LineageQueryDirectionEnum) -> List[Action]:
"""Use the lineage query to retrieve actions that use this context.
Args:
direction (LineageQueryDirectionEnum): The query direction.
Returns:
list of Actions: Actions.
"""
query_filter = LineageFilter(entities=[LineageEntityEnum.ACTION])
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
class EndpointContext(Context):
"""An Amazon SageMaker endpoint context, which is part of a SageMaker lineage."""
def models(self) -> List[association.Association]:
"""Use Lineage API to get all models deployed by this endpoint.
Returns:
list of Associations: Associations that destination represents an endpoint's model.
"""
endpoint_actions: Iterator = association.Association.list(
sagemaker_session=self.sagemaker_session,
source_arn=self.context_arn,
destination_type="ModelDeployment",
)
model_list: list = [
model
for endpoint_action in endpoint_actions
for model in association.Association.list(
source_arn=endpoint_action.destination_arn,
destination_type="Model",
sagemaker_session=self.sagemaker_session,
)
]
return model_list
def models_v2(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.DESCENDANTS
) -> List[Artifact]:
"""Use the lineage query to retrieve downstream model artifacts that use this endpoint.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of Artifacts: Artifacts representing a model.
"""
# Firstly query out the model_deployment vertices
query_filter = LineageFilter(
entities=[LineageEntityEnum.ACTION], sources=[LineageSourceEnum.MODEL_DEPLOYMENT]
)
model_deployment_query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
if not model_deployment_query_result:
return []
model_deployment_vertices: [] = model_deployment_query_result.vertices
# Secondary query model based on model deployment
model_vertices = []
for vertex in model_deployment_vertices:
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[vertex.arn],
query_filter=LineageFilter(
entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.MODEL]
),
direction=LineageQueryDirectionEnum.DESCENDANTS,
include_edges=False,
)
model_vertices.extend(query_result.vertices)
if not model_vertices:
return []
model_artifacts = []
for vertex in model_vertices:
lineage_object = vertex.to_lineage_object()
model_artifacts.append(lineage_object)
return model_artifacts
def dataset_artifacts(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[Artifact]:
"""Use the lineage query to retrieve datasets that use this endpoint.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of Artifacts: Artifacts representing a dataset.
"""
query_filter = LineageFilter(
entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.DATASET]
)
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
def training_job_arns(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[str]:
"""Get ARNs for all training jobs that appear in the endpoint's lineage.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of str: Training job ARNs.
"""
query_filter = LineageFilter(
entities=[LineageEntityEnum.TRIAL_COMPONENT], sources=[LineageSourceEnum.TRAINING_JOB]
)
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
training_job_arns = []
for vertex in query_result.vertices:
trial_component_name = _utils.get_resource_name_from_arn(vertex.arn)
trial_component = self.sagemaker_session.sagemaker_client.describe_trial_component(
TrialComponentName=trial_component_name
)
training_job_arns.append(trial_component["Source"]["SourceArn"])
return training_job_arns
def processing_jobs(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[LineageTrialComponent]:
"""Use the lineage query to retrieve processing jobs that use this endpoint.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of LineageTrialComponent: Lineage trial component that represent Processing jobs.
"""
query_filter = LineageFilter(
entities=[LineageEntityEnum.TRIAL_COMPONENT], sources=[LineageSourceEnum.PROCESSING_JOB]
)
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
def transform_jobs(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[LineageTrialComponent]:
"""Use the lineage query to retrieve transform jobs that use this endpoint.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of LineageTrialComponent: Lineage trial component that represent Transform jobs.
"""
query_filter = LineageFilter(
entities=[LineageEntityEnum.TRIAL_COMPONENT], sources=[LineageSourceEnum.TRANSFORM_JOB]
)
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
def trial_components(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[LineageTrialComponent]:
"""Use the lineage query to retrieve trial components that use this endpoint.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of LineageTrialComponent: Lineage trial component.
"""
query_filter = LineageFilter(entities=[LineageEntityEnum.TRIAL_COMPONENT])
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.context_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
def pipeline_execution_arn(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> str:
"""Get the ARN for the pipeline execution associated with this endpoint (if any).
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
str: A pipeline execution ARN.
"""
training_job_arns = self.training_job_arns(direction=direction)
for training_job_arn in training_job_arns:
tags = self.sagemaker_session.sagemaker_client.list_tags(ResourceArn=training_job_arn)[
"Tags"
]
for tag in tags:
if tag["Key"] == "sagemaker:pipeline-execution-arn":
return tag["Value"]
return None
class ModelPackageGroup(Context):
"""An Amazon SageMaker model package group context, which is part of a SageMaker lineage."""
def pipeline_execution_arn(self) -> str:
"""Get the ARN for the pipeline execution associated with this model package group (if any).
Returns:
str: A pipeline execution ARN.
"""
return self.properties.get("PipelineExecutionArn")
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