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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 functionality to display lineage data."""
from __future__ import absolute_import
import logging
from typing import Optional, Any, Iterator
import pandas as pd
from pandas import DataFrame
from sagemaker.lineage._api_types import AssociationSummary
from sagemaker.lineage.association import Association
class LineageTableVisualizer(object):
"""Creates a dataframe containing the lineage assoociations of a SageMaker object."""
def __init__(self, sagemaker_session):
"""Init for LineageTableVisualizer.
Args:
sagemaker_session (obj): The sagemaker session used for API requests.
"""
self._session = sagemaker_session
def show(
self,
trial_component_name: Optional[str] = None,
training_job_name: Optional[str] = None,
processing_job_name: Optional[str] = None,
pipeline_execution_step: Optional[object] = None,
model_package_arn: Optional[str] = None,
endpoint_arn: Optional[str] = None,
artifact_arn: Optional[str] = None,
context_arn: Optional[str] = None,
actions_arn: Optional[str] = None,
) -> DataFrame:
"""Generate a dataframe containing all incoming and outgoing lineage entities.
Examples:
.. code-block:: python
viz = LineageTableVisualizer(sagemaker_session)
df = viz.show(training_job_name=training_job_name)
# in a notebook
display(df.to_html())
Args:
trial_component_name (str, optional): Name of a trial component. Defaults to None.
training_job_name (str, optional): Name of a training job. Defaults to None.
processing_job_name (str, optional): Name of a processing job. Defaults to None.
pipeline_execution_step (obj, optional): Pipeline execution step. Defaults to None.
model_package_arn (str, optional): Model package arn. Defaults to None.
endpoint_arn (str, optional): Endpoint arn. Defaults to None.
artifact_arn (str, optional): Artifact arn. Defaults to None.
context_arn (str, optional): Context arn. Defaults to None.
actions_arn (str, optional): Action arn. Defaults to None.
Returns:
DataFrame: Pandas dataframe containing lineage associations.
"""
start_arn: str = None
if trial_component_name:
start_arn = self._get_start_arn_from_trial_component_name(trial_component_name)
elif training_job_name:
trial_component_name = training_job_name + "-aws-training-job"
start_arn = self._get_start_arn_from_trial_component_name(trial_component_name)
elif processing_job_name:
trial_component_name = processing_job_name + "-aws-processing-job"
start_arn = self._get_start_arn_from_trial_component_name(trial_component_name)
elif pipeline_execution_step:
start_arn = self._get_start_arn_from_pipeline_execution_step(pipeline_execution_step)
elif model_package_arn:
start_arn = self._get_start_arn_from_model_package_arn(model_package_arn)
elif endpoint_arn:
start_arn = self._get_start_arn_from_endpoint_arn(endpoint_arn)
elif artifact_arn:
start_arn = artifact_arn
elif context_arn:
start_arn = context_arn
elif actions_arn:
start_arn = actions_arn
return self._get_associations_dataframe(start_arn)
def _get_start_arn_from_pipeline_execution_step(self, pipeline_execution_step: object) -> str:
"""Given a pipeline exection step retrieve the arn of the lineage entity that represents it.
Args:
pipeline_execution_step (obj): Pipeline execution step.
Returns:
str: The arn of the lineage entity
"""
start_arn: str = None
if not pipeline_execution_step["Metadata"]:
return None
metadata: Any = pipeline_execution_step["Metadata"]
jobs: list = ["TrainingJob", "ProcessingJob", "TransformJob"]
for job in jobs:
if job in metadata and metadata[job]:
job_arn = metadata[job]["Arn"]
start_arn = self._get_start_arn_from_job_arn(job_arn)
break
if "RegisterModel" in metadata:
start_arn = self._get_start_arn_from_model_package_arn(metadata["RegisterModel"]["Arn"])
return start_arn
def _get_start_arn_from_job_arn(self, job_arn: str) -> str:
"""Given a job arn return the lineage entity.
Args:
job_arn (str): Arn of a training, processing, or transform job.
Returns:
str: The arn of the job's lineage entity.
"""
start_arn: str = None
response: Any = self._session.sagemaker_client.list_trial_components(SourceArn=job_arn)
trial_components: Any = response["TrialComponentSummaries"]
if trial_components:
start_arn = trial_components[0]["TrialComponentArn"]
else:
logging.warning("No trial components found for %s", job_arn)
return start_arn
def _get_associations_dataframe(self, arn: str) -> DataFrame:
"""Create a data frame containing lineage association information.
Args:
arn (str): The arn of the lineage entity of interest.
Returns:
DataFrame: A dataframe with association information.
"""
if arn is None:
# no associations
return None
upstream_associations: Iterator[AssociationSummary] = self._get_associations(dest_arn=arn)
downstream_associations: Iterator[AssociationSummary] = self._get_associations(src_arn=arn)
inputs: list = list(map(self._convert_input_association_to_df_row, upstream_associations))
outputs: list = list(
map(self._convert_output_association_to_df_row, downstream_associations)
)
df: DataFrame = pd.DataFrame(
inputs + outputs,
columns=[
"Name/Source",
"Direction",
"Type",
"Association Type",
"Lineage Type",
],
)
return df
def _get_start_arn_from_trial_component_name(self, tc_name: str) -> str:
"""Given a trial component name retrieve a start arn.
Args:
tc_name (str): Name of the trial compoonent.
Returns:
str: The arn of the trial component.
"""
response: Any = self._session.sagemaker_client.describe_trial_component(
TrialComponentName=tc_name
)
tc_arn: str = response["TrialComponentArn"]
return tc_arn
def _get_start_arn_from_model_package_arn(self, model_package_arn: str) -> str:
"""Given a model package arn retrieve the arn lineage entity.
Args:
model_package_arn (str): The arn of a model package.
Returns:
str: The arn of the lineage entity that represents the model package.
"""
response: Any = self._session.sagemaker_client.list_artifacts(SourceUri=model_package_arn)
artifacts: Any = response["ArtifactSummaries"]
artifact_arn: str = None
if artifacts:
artifact_arn = artifacts[0]["ArtifactArn"]
else:
logging.debug("No artifacts found for %s.", model_package_arn)
return artifact_arn
def _get_start_arn_from_endpoint_arn(self, endpoint_arn: str) -> str:
"""Given an endpoint arn retrieve the arn of the lineage entity.
Args:
endpoint_arn (str): The arn of an endpoint
Returns:
str: The arn of the lineage entity that represents the model package.
"""
response: Any = self._session.sagemaker_client.list_contexts(SourceUri=endpoint_arn)
contexts: Any = response["ContextSummaries"]
context_arn: str = None
if contexts:
context_arn = contexts[0]["ContextArn"]
else:
logging.debug("No contexts found for %s.", endpoint_arn)
return context_arn
def _get_associations(
self, src_arn: Optional[str] = None, dest_arn: Optional[str] = None
) -> Iterator[AssociationSummary]:
"""Given an arn retrieve all associated lineage entities.
The arn must be one of: experiment, trial, trial component, artifact, action, or context.
Args:
src_arn (str, optional): The arn of the source. Defaults to None.
dest_arn (str, optional): The arn of the destination. Defaults to None.
Returns:
array: An array of associations that are either incoming or outgoing from the lineage
entity of interest.
"""
if src_arn:
associations: Iterator[AssociationSummary] = Association.list(
source_arn=src_arn, sagemaker_session=self._session
)
else:
associations: Iterator[AssociationSummary] = Association.list(
destination_arn=dest_arn, sagemaker_session=self._session
)
return associations
def _convert_input_association_to_df_row(self, association) -> list:
"""Convert an input association to a data frame row.
Args:
association (obj): ``Association``
Returns:
array: Array of column values for the association data frame.
"""
return self._convert_association_to_df_row(
association.source_arn,
association.source_name,
"Input",
association.source_type,
association.association_type,
)
def _convert_output_association_to_df_row(self, association) -> list:
"""Convert an output association to a data frame row.
Args:
association (obj): ``Association``
Returns:
array: Array of column values for the association data frame.
"""
return self._convert_association_to_df_row(
association.destination_arn,
association.destination_name,
"Output",
association.destination_type,
association.association_type,
)
def _convert_association_to_df_row(
self,
arn: str,
name: str,
direction: str,
src_dest_type: str,
association_type: type,
) -> list:
"""Convert association data into a data frame row.
Args:
arn (str): The arn of the associated entity.
name (str): The name of the associated entity.
direction (str): The direction the association is with the entity of interest. Values
are 'Input' or 'Output'.
src_dest_type (str): The type of the entity that is associated with the entity of
interest.
association_type ([type]): The type of the association.
Returns:
[type]: [description]
"""
arn_name = arn.split(":")[5]
entity_type = arn_name.split("/")[0]
name = self._get_friendly_name(name, arn, entity_type)
return [name, direction, src_dest_type, association_type, entity_type]
def _get_friendly_name(self, name: str, arn: str, entity_type: str) -> str:
"""Get a human readable name from the association.
Args:
name (str): The name of the associated entity
arn (str): The arn of the associated entity
entity_type (str): The type of the associated entity (artifact, action, etc...)
Returns:
str: The name for the association that will be displayed in the data frame.
"""
if name:
return name
if entity_type == "artifact":
artifact = self._session.sagemaker_client.describe_artifact(ArtifactArn=arn)
uri = artifact["Source"]["SourceUri"]
# shorten the uri if the length is more than 40,
# e.g s3://flintstone-end-to-end-tests-gamma-us-west-2-069083975568/results/
# canary-auto-1608761252626/preprocessed-data/tuning_data/train.txt
# become s3://.../preprocessed-data/tuning_data/train.txt
if len(uri) > 48:
name = uri[:5] + "..." + uri[len(uri) - 40 :]
# if not then use the full uri
if not name:
name = uri
# if still don't have name derive from arn
if not name:
name = arn.split(":")[5].split("/")[1]
return name
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