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container_entrypoint.py
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container_entrypoint.py
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# Lint as: python2, python3
# Copyright 2019 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
"""Main entrypoint for containers with Kubeflow TFX component executors."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import json
import logging
import os
import sys
import textwrap
from typing import Dict, List, Text, Union
import absl
from google.protobuf import json_format
from ml_metadata.proto import metadata_store_pb2
from tfx.components.base import base_node
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow import kubeflow_metadata_adapter
from tfx.orchestration.kubeflow.proto import kubeflow_pb2
from tfx.orchestration.launcher import base_component_launcher
from tfx.types import artifact
from tfx.types import channel
from tfx.utils import import_utils
from tfx.utils import json_utils
from tfx.utils import telemetry_utils
def _get_config_value(config_value: kubeflow_pb2.ConfigValue) -> Text:
value_from = config_value.WhichOneof('value_from')
if value_from is None:
raise ValueError('No value set in config value: {}'.format(config_value))
if value_from == 'value':
return config_value.value
return os.getenv(config_value.environment_variable)
def _get_metadata_connection_config(
kubeflow_metadata_config: kubeflow_pb2.KubeflowMetadataConfig
) -> Union[metadata_store_pb2.ConnectionConfig,
metadata_store_pb2.MetadataStoreClientConfig]:
"""Constructs a metadata connection config.
Args:
kubeflow_metadata_config: Configuration parameters to use for constructing a
valid metadata connection config in a Kubeflow cluster.
Returns:
A Union of metadata_store_pb2.ConnectionConfig and
metadata_store_pb2.MetadataStoreClientConfig object.
"""
config_type = kubeflow_metadata_config.WhichOneof('connection_config')
if config_type is None:
absl.logging.warning(
'Providing mysql configuration through KubeflowMetadataConfig will be '
'deprecated soon. Use one of KubeflowGrpcMetadataConfig or'
'KubeflowMySqlMetadataConfig instead')
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.mysql.host = _get_config_value(
kubeflow_metadata_config.mysql_db_service_host)
connection_config.mysql.port = int(
_get_config_value(kubeflow_metadata_config.mysql_db_service_port))
connection_config.mysql.database = _get_config_value(
kubeflow_metadata_config.mysql_db_name)
connection_config.mysql.user = _get_config_value(
kubeflow_metadata_config.mysql_db_user)
connection_config.mysql.password = _get_config_value(
kubeflow_metadata_config.mysql_db_password)
return connection_config
assert config_type == 'grpc_config', ('expected oneof grpc_config')
return _get_grpc_metadata_connection_config(
kubeflow_metadata_config.grpc_config)
def _get_grpc_metadata_connection_config(
kubeflow_metadata_config: kubeflow_pb2.KubeflowGrpcMetadataConfig
) -> metadata_store_pb2.MetadataStoreClientConfig:
"""Constructs a metadata grpc connection config.
Args:
kubeflow_metadata_config: Configuration parameters to use for constructing a
valid metadata connection config in a Kubeflow cluster.
Returns:
A metadata_store_pb2.MetadataStoreClientConfig object.
"""
connection_config = metadata_store_pb2.MetadataStoreClientConfig()
connection_config.host = _get_config_value(
kubeflow_metadata_config.grpc_service_host)
connection_config.port = int(
_get_config_value(kubeflow_metadata_config.grpc_service_port))
return connection_config
def _sanitize_underscore(name: Text) -> Text:
"""Sanitize the underscore in pythonic name for markdown visualization."""
if name:
return str(name).replace('_', '\\_')
else:
return None
def _render_channel_as_mdstr(input_channel: channel.Channel) -> Text:
"""Render a Channel as markdown string with the following format.
**Type**: input_channel.type_name
**Artifact: artifact1**
**Properties**:
**key1**: value1
**key2**: value2
......
Args:
input_channel: the channel to be rendered.
Returns:
a md-formatted string representation of the channel.
"""
md_str = '**Type**: {}\n\n'.format(
_sanitize_underscore(input_channel.type_name))
rendered_artifacts = []
# List all artifacts in the channel.
for single_artifact in input_channel.get():
rendered_artifacts.append(_render_artifact_as_mdstr(single_artifact))
return md_str + '\n\n'.join(rendered_artifacts)
# TODO(b/147097443): clean up and consolidate rendering code.
def _render_artifact_as_mdstr(single_artifact: artifact.Artifact) -> Text:
"""Render an artifact as markdown string with the following format.
**Artifact: artifact1**
**Properties**:
**key1**: value1
**key2**: value2
......
Args:
single_artifact: the artifact to be rendered.
Returns:
a md-formatted string representation of the artifact.
"""
span_str = 'None'
split_names_str = 'None'
if single_artifact.PROPERTIES:
if 'span' in single_artifact.PROPERTIES:
span_str = str(single_artifact.span)
if 'split_names' in single_artifact.PROPERTIES:
split_names_str = str(single_artifact.split_names)
return textwrap.dedent("""\
**Artifact: {name}**
**Properties**:
**uri**: {uri}
**id**: {id}
**span**: {span}
**type_id**: {type_id}
**type_name**: {type_name}
**state**: {state}
**split_names**: {split_names}
**producer_component**: {producer_component}
""".format(
name=_sanitize_underscore(single_artifact.name) or 'None',
uri=_sanitize_underscore(single_artifact.uri) or 'None',
id=str(single_artifact.id),
span=_sanitize_underscore(span_str),
type_id=str(single_artifact.type_id),
type_name=_sanitize_underscore(single_artifact.type_name),
state=_sanitize_underscore(single_artifact.state) or 'None',
split_names=_sanitize_underscore(split_names_str),
producer_component=_sanitize_underscore(
single_artifact.producer_component) or 'None'))
def _dump_ui_metadata(component: base_node.BaseNode,
execution_info: data_types.ExecutionInfo) -> None:
"""Dump KFP UI metadata json file for visualization purpose.
For general components we just render a simple Markdown file for
exec_properties/inputs/outputs.
Args:
component: associated TFX component.
execution_info: runtime execution info for this component, including
materialized inputs/outputs/execution properties and id.
"""
exec_properties_list = [
'**{}**: {}'.format(
_sanitize_underscore(name), _sanitize_underscore(exec_property))
for name, exec_property in execution_info.exec_properties.items()
]
src_str_exec_properties = '# Execution properties:\n{}'.format(
'\n\n'.join(exec_properties_list) or 'No execution property.')
def _dump_populated_artifacts(
name_to_channel: Dict[Text, channel.Channel],
name_to_artifacts: Dict[Text, List[artifact.Artifact]]) -> List[Text]:
"""Dump artifacts markdown string.
Args:
name_to_channel: maps from channel name to channel object.
name_to_artifacts: maps from channel name to list of populated artifacts.
Returns:
A list of dumped markdown string, each of which represents a channel.
"""
rendered_list = []
for name, chnl in name_to_channel.items():
# Need to look for materialized artifacts in the execution decision.
rendered_artifacts = ''.join([
_render_artifact_as_mdstr(single_artifact)
for single_artifact in name_to_artifacts.get(name, [])
])
rendered_list.append(
'## {name}\n\n**Type**: {channel_type}\n\n{artifacts}'.format(
name=_sanitize_underscore(name),
channel_type=_sanitize_underscore(chnl.type_name),
artifacts=rendered_artifacts))
return rendered_list
src_str_inputs = '# Inputs:\n{}'.format(''.join(
_dump_populated_artifacts(
name_to_channel=component.inputs.get_all(),
name_to_artifacts=execution_info.input_dict)) or 'No input.')
src_str_outputs = '# Outputs:\n{}'.format(''.join(
_dump_populated_artifacts(
name_to_channel=component.outputs.get_all(),
name_to_artifacts=execution_info.output_dict)) or 'No output.')
outputs = [{
'storage':
'inline',
'source':
'{exec_properties}\n\n{inputs}\n\n{outputs}'.format(
exec_properties=src_str_exec_properties,
inputs=src_str_inputs,
outputs=src_str_outputs),
'type':
'markdown',
}]
# Add Tensorboard view for Trainer.
# TODO(b/142804764): Visualization based on component type seems a bit of
# arbitrary and fragile. We need a better way to improve this. See also
# b/146594754
if component.type == 'tfx.components.trainer.component.Trainer':
output_model = execution_info.output_dict['model'][0]
# Add Tensorboard view.
tensorboard_output = {'type': 'tensorboard', 'source': output_model.uri}
outputs.append(tensorboard_output)
metadata = {'outputs': outputs}
with open('/mlpipeline-ui-metadata.json', 'w') as f:
json.dump(metadata, f)
def main():
# Log to the container's stdout so Kubeflow Pipelines UI can display logs to
# the user.
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().setLevel(logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument('--pipeline_name', type=str, required=True)
parser.add_argument('--pipeline_root', type=str, required=True)
parser.add_argument('--kubeflow_metadata_config', type=str, required=True)
parser.add_argument('--beam_pipeline_args', type=str, required=True)
parser.add_argument('--additional_pipeline_args', type=str, required=True)
parser.add_argument(
'--component_launcher_class_path', type=str, required=True)
parser.add_argument('--enable_cache', action='store_true')
parser.add_argument('--serialized_component', type=str, required=True)
parser.add_argument('--component_config', type=str, required=True)
args = parser.parse_args()
component = json_utils.loads(args.serialized_component)
component_config = json_utils.loads(args.component_config)
component_launcher_class = import_utils.import_class_by_path(
args.component_launcher_class_path)
if not issubclass(component_launcher_class,
base_component_launcher.BaseComponentLauncher):
raise TypeError(
'component_launcher_class "%s" is not subclass of base_component_launcher.BaseComponentLauncher'
% component_launcher_class)
kubeflow_metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
json_format.Parse(args.kubeflow_metadata_config, kubeflow_metadata_config)
metadata_connection = kubeflow_metadata_adapter.KubeflowMetadataAdapter(
_get_metadata_connection_config(kubeflow_metadata_config))
driver_args = data_types.DriverArgs(enable_cache=args.enable_cache)
beam_pipeline_args = json.loads(args.beam_pipeline_args)
additional_pipeline_args = json.loads(args.additional_pipeline_args)
launcher = component_launcher_class.create(
component=component,
pipeline_info=data_types.PipelineInfo(
pipeline_name=args.pipeline_name,
pipeline_root=args.pipeline_root,
run_id=os.environ['WORKFLOW_ID']),
driver_args=driver_args,
metadata_connection=metadata_connection,
beam_pipeline_args=beam_pipeline_args,
additional_pipeline_args=additional_pipeline_args,
component_config=component_config)
# Attach necessary labels to distinguish different runner and DSL.
# TODO(zhitaoli): Pass this from KFP runner side when the same container
# entrypoint can be used by a different runner.
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_RUNNER: 'kfp',
}):
execution_info = launcher.launch()
# Dump the UI metadata.
_dump_ui_metadata(component, execution_info)
if __name__ == '__main__':
main()