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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 ``LineageTrialComponent``."""
from __future__ import absolute_import
import logging
from typing import List
from sagemaker.apiutils import _base_types
from sagemaker.lineage.query import (
LineageQuery,
LineageFilter,
LineageSourceEnum,
LineageEntityEnum,
LineageQueryDirectionEnum,
)
from sagemaker.lineage.artifact import Artifact
LOGGER = logging.getLogger("sagemaker")
class LineageTrialComponent(_base_types.Record):
"""An Amazon SageMaker, lineage trial component, which is part of a SageMaker lineage.
A trial component is a stage in a trial.
Trial components are created automatically within the SageMaker runtime and also can be
created directly. To automatically associate trial components with a trial and experiment
supply an experiment config when creating a job.
For example: https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html
Attributes:
trial_component_name (str): The name of the trial component. Generated by SageMaker from the
name of the source job with a suffix specific to the type of source job.
trial_component_arn (str): The ARN of the trial component.
display_name (str): The name of the trial component that will appear in UI,
such as SageMaker Studio.
source (obj): A TrialComponentSource object with a source_arn attribute.
status (str): Status of the source job.
start_time (datetime): When the source job started.
end_time (datetime): When the source job ended.
creation_time (datetime): When the source job was created.
created_by (obj): Contextual info on which account created the trial component.
last_modified_time (datetime): When the trial component was last modified.
last_modified_by (obj): Contextual info on which account last modified the trial component.
parameters (dict): Dictionary of parameters to the source job.
input_artifacts (dict): Dictionary of input artifacts.
output_artifacts (dict): Dictionary of output artifacts.
metrics (obj): Aggregated metrics for the job.
parameters_to_remove (list): The hyperparameters to remove from the component.
input_artifacts_to_remove (list): The input artifacts to remove from the component.
output_artifacts_to_remove (list): The output artifacts to remove from the component.
tags (List[dict[str, str]]): A list of tags to associate with the trial component.
"""
trial_component_name = None
trial_component_arn = None
display_name = None
source = None
status = None
start_time = None
end_time = None
creation_time = None
created_by = None
last_modified_time = None
last_modified_by = None
parameters = None
input_artifacts = None
output_artifacts = None
metrics = None
parameters_to_remove = None
input_artifacts_to_remove = None
output_artifacts_to_remove = None
tags = None
_boto_create_method: str = "create_trial_component"
_boto_load_method: str = "describe_trial_component"
_boto_update_method: str = "update_trial_component"
_boto_delete_method: str = "delete_trial_component"
_boto_update_members = [
"trial_component_name",
"display_name",
"status",
"start_time",
"end_time",
"parameters",
"input_artifacts",
"output_artifacts",
"parameters_to_remove",
"input_artifacts_to_remove",
"output_artifacts_to_remove",
]
_boto_delete_members = ["trial_component_name"]
@classmethod
def load(cls, trial_component_name: str, sagemaker_session=None) -> "LineageTrialComponent":
"""Load an existing trial component and return an ``TrialComponent`` object representing it.
Args:
trial_component_name (str): Name of the trial component
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:
LineageTrialComponent: A SageMaker ``LineageTrialComponent`` object
"""
trial_component = cls._construct(
cls._boto_load_method,
trial_component_name=trial_component_name,
sagemaker_session=sagemaker_session,
)
return trial_component
def pipeline_execution_arn(self) -> str:
"""Get the ARN for the pipeline execution associated with this trial component (if any).
Returns:
str: A pipeline execution ARN.
"""
trial_component = self.load(
trial_component_name=self.trial_component_name, sagemaker_session=self.sagemaker_session
)
if trial_component.source is None or trial_component.source["SourceArn"] is None:
return None
tags = self.sagemaker_session.sagemaker_client.list_tags(
ResourceArn=trial_component.source["SourceArn"]
)["Tags"]
for tag in tags:
if tag["Key"] == "sagemaker:pipeline-execution-arn":
return tag["Value"]
return None
def dataset_artifacts(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.ASCENDANTS
) -> List[Artifact]:
"""Use the lineage query to retrieve datasets that use this trial component.
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.trial_component_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
def models(
self, direction: LineageQueryDirectionEnum = LineageQueryDirectionEnum.DESCENDANTS
) -> List[Artifact]:
"""Use the lineage query to retrieve models that use this trial component.
Args:
direction (LineageQueryDirectionEnum, optional): The query direction.
Returns:
list of Artifacts: Artifacts representing a dataset.
"""
query_filter = LineageFilter(
entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.MODEL]
)
query_result = LineageQuery(self.sagemaker_session).query(
start_arns=[self.trial_component_arn],
query_filter=query_filter,
direction=direction,
include_edges=False,
)
return [vertex.to_lineage_object() for vertex in query_result.vertices]
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