def testScopedLabels(self):
"""Test for scoped_labels."""
orig_labels = telemetry_utils.get_labels_dict()
with telemetry_utils.scoped_labels({'foo': 'bar'}):
self.assertDictEqual(telemetry_utils.get_labels_dict(),
dict({'foo': 'bar'}, **orig_labels))
with telemetry_utils.scoped_labels({'inner': 'baz'}):
self.assertDictEqual(
telemetry_utils.get_labels_dict(),
dict({
'foo': 'bar',
'inner': 'baz'
}, **orig_labels))
def run(self, pipeline: pipeline_py.Pipeline) -> None:
"""Runs given logical pipeline locally.
Args:
pipeline: Logical pipeline containing pipeline args and components.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid executing the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
for component in pipeline.components:
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies( # pylint: disable=protected-access
pipeline.pipeline_info.pipeline_root)
c = compiler.Compiler()
pipeline = c.compile(pipeline)
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline, {
constants.PIPELINE_RUN_ID_PARAMETER_NAME:
datetime.datetime.now().isoformat(),
})
deployment_config = runner_utils.extract_local_deployment_config(pipeline)
connection_config = deployment_config.metadata_connection_config
logging.info('Running pipeline:\n %s', pipeline)
logging.info('Using deployment config:\n %s', deployment_config)
logging.info('Using connection config:\n %s', connection_config)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'local'}):
# Run each component. Note that the pipeline.components list is in
# topological order.
#
# TODO(b/171319478): After IR-based execution is used, used multi-threaded
# execution so that independent components can be run in parallel.
for node in pipeline.nodes:
pipeline_node = node.pipeline_node
node_id = pipeline_node.node_info.id
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id)
component_launcher = launcher.Launcher(
pipeline_node=pipeline_node,
mlmd_connection=metadata.Metadata(connection_config),
pipeline_info=pipeline.pipeline_info,
pipeline_runtime_spec=pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec)
logging.info('Component %s is running.', node_id)
component_launcher.launch()
logging.info('Component %s is finished.', node_id)
def testDoBlessed(self, mock_runner, _):
self._model_blessing.uri = os.path.join(self._source_data_dir,
'model_validator/blessed')
self._model_blessing.set_int_custom_property('blessed', 1)
mock_runner.get_service_name_and_api_version.return_value = ('ml',
'v1')
self._executor.Do(self._input_dict, self._output_dict,
self._serialize_custom_config_under_test())
executor_class_path = '%s.%s' % (self._executor.__class__.__module__,
self._executor.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.get_labels_dict()
mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
mock.ANY,
self._model_push.uri,
mock.ANY,
mock.ANY,
job_labels,
)
self.assertPushed()
version = self._model_push.get_string_custom_property('pushed_version')
self.assertEqual(
self._model_push.get_string_custom_property('pushed_destination'),
'projects/project_id/models/model_name/versions/{}'.format(
version))
def testDoSkippedModelCreation(self, mock_runner, mock_run_model_inference,
_):
input_dict = {
'examples': [self._examples],
'model': [self._model],
'model_blessing': [self._model_blessing],
}
output_dict = {
'inference_result': [self._inference_result],
}
ai_platform_serving_args = {
'model_name': 'model_name',
'project_id': 'project_id'
}
# Create exe properties.
exec_properties = {
'data_spec':
proto_utils.proto_to_json(bulk_inferrer_pb2.DataSpec()),
'custom_config':
json_utils.dumps(
{executor.SERVING_ARGS_KEY: ai_platform_serving_args}),
}
mock_runner.get_service_name_and_api_version.return_value = ('ml', 'v1')
mock_runner.create_model_for_aip_prediction_if_not_exist.return_value = False
# Run executor.
bulk_inferrer = executor.Executor(self._context)
bulk_inferrer.Do(input_dict, output_dict, exec_properties)
ai_platform_prediction_model_spec = (
model_spec_pb2.AIPlatformPredictionModelSpec(
project_id='project_id',
model_name='model_name',
version_name=self._model_version))
ai_platform_prediction_model_spec.use_serialization_config = True
inference_endpoint = model_spec_pb2.InferenceSpecType()
inference_endpoint.ai_platform_prediction_model_spec.CopyFrom(
ai_platform_prediction_model_spec)
mock_run_model_inference.assert_called_once_with(mock.ANY, mock.ANY,
mock.ANY, mock.ANY,
mock.ANY,
inference_endpoint)
executor_class_path = '%s.%s' % (bulk_inferrer.__class__.__module__,
bulk_inferrer.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.make_labels_dict()
mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
serving_path=path_utils.serving_model_path(self._model.uri),
model_version_name=mock.ANY,
ai_platform_serving_args=ai_platform_serving_args,
labels=job_labels,
api=mock.ANY,
skip_model_endpoint_creation=True,
set_default=False)
mock_runner.delete_model_from_aip_if_exists.assert_called_once_with(
model_version_name=mock.ANY,
ai_platform_serving_args=ai_platform_serving_args,
api=mock.ANY,
delete_model_endpoint=False)
def testDoBlessed_Vertex(self, mock_runner):
endpoint_uri = 'projects/project_id/locations/us-central1/endpoints/12345'
mock_runner.deploy_model_for_aip_prediction.return_value = endpoint_uri
self._model_blessing.uri = os.path.join(self._source_data_dir,
'model_validator/blessed')
self._model_blessing.set_int_custom_property('blessed', 1)
self._executor.Do(self._input_dict, self._output_dict,
self._serialize_custom_config_under_test_vertex())
executor_class_path = '%s.%s' % (self._executor.__class__.__module__,
self._executor.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.make_labels_dict()
mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
serving_container_image_uri=self._container_image_uri_vertex,
model_version_name=mock.ANY,
ai_platform_serving_args=mock.ANY,
labels=job_labels,
serving_path=self._model_push.uri,
endpoint_region='us-central1',
enable_vertex=True,
)
self.assertPushed()
self.assertEqual(
self._model_push.get_string_custom_property('pushed_destination'),
endpoint_uri)
def __init__(self, context: Optional[Context] = None):
"""Constructs a beam based executor."""
super().__init__(context)
self._beam_pipeline_args = None
if context:
if isinstance(context, BaseBeamExecutor.Context):
self._beam_pipeline_args = context.beam_pipeline_args
else:
raise ValueError(
'BaseBeamExecutor found initialized with '
'BaseExecutorSpec. Please use BeamEecutorSpec for '
'Beam Components instead.')
if self._beam_pipeline_args:
self._beam_pipeline_args = dependency_utils.make_beam_dependency_flags(
self._beam_pipeline_args)
executor_class_path = '%s.%s' % (self.__class__.__module__,
self.__class__.__name__)
# TODO(zhitaoli): Rethink how we can add labels and only normalize them
# if the job is submitted against GCP.
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
self._beam_pipeline_args.extend(
telemetry_utils.make_beam_labels_args())
# TODO(b/174174381): Don't use beam_pipeline_args to set ABSL flags.
flags.FLAGS(sys.argv + self._beam_pipeline_args, known_only=True)
def setUp(self):
super(RunnerTest, self).setUp()
self._output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
self._project_id = '12345'
self._mock_api_client = mock.Mock()
self._inputs = {}
self._outputs = {}
self._training_inputs = {
'project': self._project_id,
}
self._job_id = 'my_jobid'
# Dict format of exec_properties. custom_config needs to be serialized
# before being passed into start_aip_training function.
self._exec_properties = {
'custom_config': {
executor.TRAINING_ARGS_KEY: self._training_inputs,
},
}
self._model_name = 'model_name'
self._ai_platform_serving_args = {
'model_name': self._model_name,
'project_id': self._project_id,
}
self._executor_class_path = 'my.executor.Executor'
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: self._executor_class_path}):
self._job_labels = telemetry_utils.get_labels_dict()
def __init__(self, context: Optional[Context] = None):
"""Constructs a beam based executor."""
self._context = context
self._beam_pipeline_args = context.beam_pipeline_args if context else None
if self._beam_pipeline_args:
if beam:
self._beam_pipeline_args = dependency_utils.make_beam_dependency_flags(
self._beam_pipeline_args)
executor_class_path = '%s.%s' % (self.__class__.__module__,
self.__class__.__name__)
# TODO(zhitaoli): Rethink how we can add labels and only normalize them
# if the job is submitted against GCP.
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
self._beam_pipeline_args.extend(
telemetry_utils.make_beam_labels_args())
# TODO(b/174174381): Don't use beam_pipeline_args to set ABSL flags.
flags.FLAGS(sys.argv + self._beam_pipeline_args,
known_only=True)
else:
# TODO(b/156000550): We should not specialize `Context` to embed beam
# pipeline args. Instead, the `Context` should consists of generic
# purpose `extra_flags` which can be interpreted differently by
# different implementations of executors.
absl.logging.warning(
'Executor context\'s beam_pipeline_args is being ignored because '
'Apache Beam is not installed.')
def testScopedLabels(self):
"""Test for scoped_labels."""
orig_labels = telemetry_utils.make_labels_dict()
with telemetry_utils.scoped_labels({'foo': 'bar'}):
self.assertDictEqual(telemetry_utils.make_labels_dict(),
dict({'foo': 'bar'}, **orig_labels))
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_EXECUTOR:
'custom_component.custom_executor'
}):
self.assertDictEqual(
telemetry_utils.make_labels_dict(),
dict(
{
'foo':
'bar',
telemetry_utils.LABEL_TFX_EXECUTOR:
'third_party_executor'
}, **orig_labels))
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_EXECUTOR:
'tfx.components.example_gen.import_example_gen.executor.Executor'
}):
self.assertDictEqual(
telemetry_utils.make_labels_dict(),
dict(
{
'foo':
'bar',
telemetry_utils.LABEL_TFX_EXECUTOR: # Label is normalized.
'tfx-components-example_gen-import_example_gen-executor-executor'
},
**orig_labels))
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_EXECUTOR:
'tfx.extensions.google_cloud_big_query.example_gen.executor.Executor'
}):
self.assertDictEqual(
telemetry_utils.make_labels_dict(),
dict(
{
'foo':
'bar',
telemetry_utils.LABEL_TFX_EXECUTOR: # Label is normalized.
'tfx-extensions-google_cloud_big_query-example_gen-executor-exec'
},
**orig_labels))
def create_training_job(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any],
executor_class_path: str, job_args: Dict[str, Any],
job_id: Optional[str]) -> Dict[str, Any]:
"""Get training args for runner._launch_aip_training.
The training args contain the inputs/outputs/exec_properties to the
tfx.scripts.run_executor module.
Args:
input_dict: Passthrough input dict for tfx.components.Trainer.executor.
output_dict: Passthrough input dict for tfx.components.Trainer.executor.
exec_properties: Passthrough input dict for
tfx.components.Trainer.executor.
executor_class_path: class path for TFX core default trainer.
job_args: Training input argument for AI Platform training job.
'pythonModule', 'pythonVersion' and 'runtimeVersion' will be inferred.
For the full set of parameters, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput
job_id: Job ID for AI Platform Training job. If not supplied,
system-determined unique ID is given. Refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#resource-job
Returns:
A dict containing the training arguments
"""
training_inputs = job_args.copy()
container_command = self.generate_container_command(input_dict, output_dict,
exec_properties,
executor_class_path)
if not training_inputs.get('masterConfig'):
training_inputs['masterConfig'] = {
'imageUri': _TFX_IMAGE,
}
# Always use our own entrypoint instead of relying on container default.
if 'containerCommand' in training_inputs['masterConfig']:
logging.warn('Overriding custom value of containerCommand')
training_inputs['masterConfig']['containerCommand'] = container_command
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.make_labels_dict()
# 'tfx_YYYYmmddHHMMSS' is the default job ID if not explicitly specified.
job_id = job_id or 'tfx_{}'.format(
datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
caip_job = {
'job_id': job_id,
'training_input': training_inputs,
'labels': job_labels
}
return caip_job
def main():
# Log to the container's stdout so it can be streamed by the orchestrator.
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('--run_id', type=str, required=True)
parser.add_argument('--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)
metadata_config = metadata_store_pb2.ConnectionConfig()
json_format.Parse(args.metadata_config, 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=args.run_id,
),
driver_args=driver_args,
metadata_connection=metadata.Metadata(
connection_config=metadata_config),
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.
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_RUNNER:
'kubernetes',
}):
launcher.launch()
def run(
self,
component: base_node.BaseNode,
enable_cache: bool = True,
beam_pipeline_args: Optional[List[Text]] = None
) -> execution_result.ExecutionResult:
"""Run a given TFX component in the interactive context.
Args:
component: Component instance to be run.
enable_cache: whether caching logic should be enabled in the driver.
beam_pipeline_args: Optional Beam pipeline args for beam jobs within
executor. Executor will use beam DirectRunner as Default. If provided,
will override beam_pipeline_args specified in constructor.
Returns:
execution_result.ExecutionResult object.
"""
run_id = datetime.datetime.now().isoformat()
pipeline_info = data_types.PipelineInfo(
pipeline_name=self.pipeline_name,
pipeline_root=self.pipeline_root,
run_id=run_id)
driver_args = data_types.DriverArgs(enable_cache=enable_cache,
interactive_resolution=True)
metadata_connection = metadata.Metadata(
self.metadata_connection_config)
beam_pipeline_args = beam_pipeline_args or self.beam_pipeline_args
additional_pipeline_args = {}
for name, output in component.outputs.items():
for artifact in output.get():
artifact.pipeline_name = self.pipeline_name
artifact.producer_component = component.id
artifact.name = name
# Special treatment for pip dependencies.
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies(self.pipeline_root) # pylint: disable=protected-access
# TODO(hongyes): figure out how to resolve launcher class in the interactive
# context.
launcher = in_process_component_launcher.InProcessComponentLauncher.create(
component, pipeline_info, driver_args, metadata_connection,
beam_pipeline_args, additional_pipeline_args)
try:
import colab # pytype: disable=import-error # pylint: disable=g-import-not-at-top, unused-import, unused-variable
runner_label = 'interactivecontext-colab'
except ImportError:
runner_label = 'interactivecontext'
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_RUNNER:
runner_label,
}):
execution_id = launcher.launch().execution_id
return execution_result.ExecutionResult(component=component,
execution_id=execution_id)
def run(self,
pipeline: tfx_pipeline.Pipeline,
parameter_values: Optional[Dict[Text, Any]] = None,
write_out: Optional[bool] = True) -> Dict[Text, Any]:
"""Compiles a pipeline DSL object into pipeline file.
Args:
pipeline: TFX pipeline object.
parameter_values: mapping from runtime parameter names to its values.
write_out: set to True to actually write out the file to the place
designated by output_dir and output_filename. Otherwise return the
JSON-serialized pipeline job spec.
Returns:
Returns the JSON pipeline job spec.
Raises:
RuntimeError: if trying to write out to a place occupied by an existing
file.
"""
# TODO(b/166343606): Support user-provided labels.
# TODO(b/169095387): Deprecate .run() method in favor of the unified API
# client.
display_name = (self._config.display_name
or pipeline.pipeline_info.pipeline_name)
pipeline_spec = pipeline_builder.PipelineBuilder(
tfx_pipeline=pipeline,
default_image=self._config.default_image,
default_commands=self._config.default_commands).build()
pipeline_spec.sdk_version = 'tfx-{}'.format(version.__version__)
pipeline_spec.schema_version = _SCHEMA_VERSION
runtime_config = pipeline_builder.RuntimeConfigBuilder(
pipeline_info=pipeline.pipeline_info,
parameter_values=parameter_values).build()
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'kubeflow_v2'}):
result = pipeline_spec_pb2.PipelineJob(
display_name=display_name
or pipeline.pipeline_info.pipeline_name,
labels=telemetry_utils.get_labels_dict(),
runtime_config=runtime_config)
result.pipeline_spec.update(json_format.MessageToDict(pipeline_spec))
pipeline_json_dict = json_format.MessageToDict(result)
if write_out:
if fileio.exists(
self._output_dir) and not fileio.isdir(self._output_dir):
raise RuntimeError('Output path: %s is pointed to a file.' %
self._output_dir)
if not fileio.exists(self._output_dir):
fileio.makedirs(self._output_dir)
with fileio.open(
os.path.join(self._output_dir, self._output_filename),
'wb') as f:
f.write(json.dumps(pipeline_json_dict, sort_keys=True))
return pipeline_json_dict
def run(self, tfx_pipeline: pipeline.Pipeline) -> None:
"""Deploys given logical pipeline on Beam.
Args:
tfx_pipeline: Logical pipeline containing pipeline args and components.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid executing the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
tfx_pipeline.pipeline_info.run_id = datetime.datetime.now().isoformat()
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'beam'}):
with beam.Pipeline(argv=self._beam_orchestrator_args) as p:
# Uses for triggering the component DoFns.
root = p | 'CreateRoot' >> beam.Create([None])
# Stores mapping of component to its signal.
signal_map = {}
# pipeline.components are in topological order.
for component in tfx_pipeline.components:
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies( # pylint: disable=protected-access
tfx_pipeline.pipeline_info.pipeline_root)
component_id = component.id
# Signals from upstream components.
signals_to_wait = []
if component.upstream_nodes:
for upstream_node in component.upstream_nodes:
assert upstream_node in signal_map, (
'Components is not in '
'topological order')
signals_to_wait.append(signal_map[upstream_node])
absl.logging.info(
'Component %s depends on %s.', component_id,
[s.producer.full_label for s in signals_to_wait])
(component_launcher_class, component_config
) = config_utils.find_component_launch_info(
self._config, component)
# Each signal is an empty PCollection. AsIter ensures component will
# be triggered after upstream components are finished.
signal_map[component] = (
root
| 'Run[%s]' % component_id >> beam.ParDo(
_ComponentAsDoFn(component,
component_launcher_class,
component_config, tfx_pipeline), *
[beam.pvalue.AsIter(s) for s in signals_to_wait]))
absl.logging.info('Component %s is scheduled.',
component_id)
def _assertDeployModelMockCalls(self,
expected_models_create_body=None,
expected_versions_create_body=None,
expect_set_default=True):
if not expected_models_create_body:
expected_models_create_body = {
'name': self._model_name,
'regions': [],
}
if not expected_versions_create_body:
with telemetry_utils.scoped_labels({
telemetry_utils.LABEL_TFX_EXECUTOR:
self._executor_class_path
}):
labels = telemetry_utils.get_labels_dict()
expected_versions_create_body = {
'name':
self._model_version,
'deployment_uri':
self._serving_path,
'runtime_version':
runner._get_tf_runtime_version(tf.__version__),
'python_version':
runner._get_caip_python_version(
runner._get_tf_runtime_version(tf.__version__)),
'labels':
labels
}
self._mock_models_create.assert_called_with(
body=mock.ANY,
parent='projects/{}'.format(self._project_id),
)
(_, models_create_kwargs) = self._mock_models_create.call_args
self.assertDictEqual(expected_models_create_body,
models_create_kwargs['body'])
self._mock_versions_create.assert_called_with(
body=mock.ANY,
parent='projects/{}/models/{}'.format(self._project_id,
self._model_name))
(_, versions_create_kwargs) = self._mock_versions_create.call_args
self.assertDictEqual(expected_versions_create_body,
versions_create_kwargs['body'])
if not expect_set_default:
return
self._mock_set_default.assert_called_with(
name='projects/{}/models/{}/versions/{}'.format(
self._project_id, self._model_name, self._model_version))
self._mock_set_default_execute.assert_called_with()
def run(self, pipeline: pipeline_pb2.Pipeline) -> None:
"""Deploys given logical pipeline on Beam.
Args:
pipeline: Logical pipeline in IR format.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid deploying the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
# TODO(b/163003901): Support beam DAG runner args through IR.
# TODO(b/163003901): MLMD connection config should be passed in via IR.
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
mlmd_connection = metadata.Metadata(
connection_config=connection_config)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'beam'}):
with beam.Pipeline() as p:
# Uses for triggering the component DoFns.
root = p | 'CreateRoot' >> beam.Create([None])
# Stores mapping of component to its signal.
signal_map = {}
# pipeline.components are in topological order.
for node in pipeline.nodes:
# TODO(b/160882349): Support subpipeline
pipeline_node = node.pipeline_node
component_id = pipeline_node.node_info.id
# Signals from upstream components.
signals_to_wait = []
for upstream_node in pipeline_node.upstream_nodes:
assert upstream_node in signal_map, ('Components is not in '
'topological order')
signals_to_wait.append(signal_map[upstream_node])
logging.info('Component %s depends on %s.', component_id,
[s.producer.full_label for s in signals_to_wait])
# Each signal is an empty PCollection. AsIter ensures component will
# be triggered after upstream components are finished.
# LINT.IfChange
signal_map[component_id] = (
root
| 'Run[%s]' % component_id >> beam.ParDo(
_PipelineNodeAsDoFn(pipeline_node, mlmd_connection,
pipeline.pipeline_info,
pipeline.runtime_spec), *
[beam.pvalue.AsIter(s) for s in signals_to_wait]))
# LINT.ThenChange(../beam/beam_dag_runner.py)
logging.info('Component %s is scheduled.', component_id)
def __init__(self, context: Optional[Context] = None):
"""Constructs a beam based executor."""
self._context = context
self._beam_pipeline_args = context.beam_pipeline_args if context else None
if self._beam_pipeline_args:
self._beam_pipeline_args = dependency_utils.make_beam_dependency_flags(
self._beam_pipeline_args)
executor_class_path = '%s.%s' % (self.__class__.__module__,
self.__class__.__name__)
# TODO(zhitaoli): Rethink how we can add labels and only normalize them
# if the job is submitted against GCP.
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
self._beam_pipeline_args.extend(telemetry_utils.make_beam_labels_args())
def _airflow_component_launcher(
component: base_node.BaseNode, component_launcher_class: Type[
base_component_launcher.BaseComponentLauncher],
pipeline_info: data_types.PipelineInfo,
driver_args: data_types.DriverArgs,
metadata_connection_config: metadata_store_pb2.ConnectionConfig,
beam_pipeline_args: List[Text], additional_pipeline_args: Dict[Text,
Any],
component_config: base_component_config.BaseComponentConfig,
exec_properties: Dict[Text, Any], **kwargs) -> None:
"""Helper function to launch TFX component execution.
This helper function will be called with Airflow env objects which contains
run_id that we need to pass into TFX ComponentLauncher.
Args:
component: TFX BaseComponent instance. This instance holds all inputs and
outputs placeholders as well as component properties.
component_launcher_class: The class of the launcher to launch the component.
pipeline_info: A data_types.PipelineInfo instance that holds pipeline
properties
driver_args: Component specific args for driver.
metadata_connection_config: Configuration for how to connect to metadata.
beam_pipeline_args: Pipeline arguments for Beam powered Components.
additional_pipeline_args: A dict of additional pipeline args.
component_config: Component config to launch the component.
exec_properties: Execution properties from the ComponentSpec.
**kwargs: Context arguments that will be passed in by Airflow, including:
- ti: TaskInstance object from which we can get run_id of the running
pipeline.
For more details, please refer to the code:
https://github.com/apache/airflow/blob/master/airflow/operators/python_operator.py
"""
component.exec_properties.update(exec_properties)
# Populate run id from Airflow task instance.
pipeline_info.run_id = kwargs['ti'].get_dagrun().run_id
launcher = component_launcher_class.create(
component=component,
pipeline_info=pipeline_info,
driver_args=driver_args,
metadata_connection=metadata.Metadata(metadata_connection_config),
beam_pipeline_args=beam_pipeline_args,
additional_pipeline_args=additional_pipeline_args,
component_config=component_config)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'airflow'}):
launcher.launch()
def testDeployModelForAIPPredictionWithCustomRegion(self, mock_discovery):
mock_discovery.build.return_value = self._mock_api_client
self._setUpPredictionMocks()
self._ai_platform_serving_args['regions'] = ['custom-region']
runner.deploy_model_for_aip_prediction(self._serving_path,
self._model_version,
self._ai_platform_serving_args,
self._executor_class_path)
self._mock_models_create.assert_called_with(
body=mock.ANY,
parent='projects/{}'.format(self._project_id),
)
(_, models_create_kwargs) = self._mock_models_create.call_args
models_create_body = models_create_kwargs['body']
self.assertDictEqual(
{
'name': 'model_name',
'regions': ['custom-region']
}, models_create_body)
self._mock_versions_create.assert_called_with(
body=mock.ANY,
parent='projects/{}/models/{}'.format(self._project_id,
'model_name'))
(_, versions_create_kwargs) = self._mock_versions_create.call_args
versions_create_body = versions_create_kwargs['body']
with telemetry_utils.scoped_labels(
{telemetry_utils.TFX_EXECUTOR: self._executor_class_path}):
labels = telemetry_utils.get_labels_dict()
runtime_version = runner._get_tf_runtime_version(tf.__version__)
self.assertDictEqual(
{
'name': self._model_version,
'deployment_uri': self._serving_path,
'runtime_version': runtime_version,
'python_version':
runner._get_caip_python_version(runtime_version),
'labels': labels,
}, versions_create_body)
self._mock_get.assert_called_with(name='op_name')
self._mock_set_default.assert_called_with(
name='projects/{}/models/{}/versions/{}'.format(
self._project_id, 'model_name', self._model_version))
self._mock_set_default_execute.assert_called_with()
def run(self, tfx_pipeline: pipeline.Pipeline) -> None:
"""Runs given logical pipeline locally.
Args:
tfx_pipeline: Logical pipeline containing pipeline args and components.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid executing the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
tfx_pipeline.pipeline_info.run_id = datetime.datetime.now().isoformat()
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'local'}):
# Run each component. Note that the pipeline.components list is in
# topological order.
#
# TODO(b/171319478): After IR-based execution is used, used multi-threaded
# execution so that independent components can be run in parallel.
for component in tfx_pipeline.components:
# TODO(b/187122662): Pass through pip dependencies as a first-class
# component flag.
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies( # pylint: disable=protected-access
tfx_pipeline.pipeline_info.pipeline_root)
(component_launcher_class, component_config) = (
config_utils.find_component_launch_info(self._config, component))
driver_args = data_types.DriverArgs(
enable_cache=tfx_pipeline.enable_cache)
metadata_connection = metadata.Metadata(
tfx_pipeline.metadata_connection_config)
node_launcher = component_launcher_class.create(
component=component,
pipeline_info=tfx_pipeline.pipeline_info,
driver_args=driver_args,
metadata_connection=metadata_connection,
beam_pipeline_args=tfx_pipeline.beam_pipeline_args,
additional_pipeline_args=tfx_pipeline.additional_pipeline_args,
component_config=component_config)
logging.info('Component %s is running.', component.id)
node_launcher.launch()
logging.info('Component %s is finished.', component.id)
def testMakeBeamLabelsArgs(self):
"""Test for make_beam_labels_args."""
beam_pipeline_args = telemetry_utils.make_beam_labels_args()
expected_beam_pipeline_args = [
'--labels',
'tfx_py_version=%d-%d' %
(sys.version_info.major, sys.version_info.minor),
'--labels',
'tfx_version=%s' % version.__version__.replace('.', '-'),
]
self.assertListEqual(expected_beam_pipeline_args, beam_pipeline_args)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: 'TestExecutor'}):
beam_pipeline_args = telemetry_utils.make_beam_labels_args()
expected_beam_pipeline_args = [
'--labels',
'tfx_executor=third_party_executor',
] + expected_beam_pipeline_args
self.assertListEqual(expected_beam_pipeline_args,
beam_pipeline_args)
def testDoBlessedOnRegionalEndpoint(self, mock_runner, _):
self._exec_properties = {
'custom_config': {
constants.SERVING_ARGS_KEY: {
'model_name': 'model_name',
'project_id': 'project_id'
},
constants.ENDPOINT_ARGS_KEY:
'https://ml-us-west1.googleapis.com',
},
}
self._model_blessing.uri = os.path.join(self._source_data_dir,
'model_validator/blessed')
self._model_blessing.set_int_custom_property('blessed', 1)
mock_runner.get_service_name_and_api_version.return_value = ('ml',
'v1')
version = self._model_push.get_string_custom_property('pushed_version')
mock_runner.deploy_model_for_aip_prediction.return_value = (
'projects/project_id/models/model_name/versions/{}'.format(version)
)
self._executor.Do(self._input_dict, self._output_dict,
self._serialize_custom_config_under_test())
executor_class_path = '%s.%s' % (self._executor.__class__.__module__,
self._executor.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.make_labels_dict()
mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
serving_path=self._model_push.uri,
model_version_name=mock.ANY,
ai_platform_serving_args=mock.ANY,
api=mock.ANY,
labels=job_labels,
)
self.assertPushed()
self.assertEqual(
self._model_push.get_string_custom_property('pushed_destination'),
'projects/project_id/models/model_name/versions/{}'.format(
version))
def testDoBlessedOnRegionalEndpoint_Vertex(self, mock_runner):
endpoint_uri = 'projects/project_id/locations/us-west1/endpoints/12345'
mock_runner.deploy_model_for_aip_prediction.return_value = endpoint_uri
self._exec_properties_vertex = {
'custom_config': {
constants.SERVING_ARGS_KEY: {
'model_name': 'model_name',
'project_id': 'project_id'
},
constants.VERTEX_CONTAINER_IMAGE_URI_KEY:
self._container_image_uri_vertex,
constants.ENABLE_VERTEX_KEY: True,
constants.VERTEX_REGION_KEY: 'us-west1',
},
}
self._model_blessing.uri = os.path.join(self._source_data_dir,
'model_validator/blessed')
self._model_blessing.set_int_custom_property('blessed', 1)
self._executor.Do(self._input_dict, self._output_dict,
self._serialize_custom_config_under_test_vertex())
executor_class_path = '%s.%s' % (self._executor.__class__.__module__,
self._executor.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.make_labels_dict()
mock_runner.deploy_model_for_aip_prediction.assert_called_once_with(
serving_path=self._model_push.uri,
model_version_name=mock.ANY,
ai_platform_serving_args=mock.ANY,
labels=job_labels,
serving_container_image_uri=self._container_image_uri_vertex,
endpoint_region='us-west1',
enable_vertex=True,
)
self.assertPushed()
self.assertEqual(
self._model_push.get_string_custom_property('pushed_destination'),
endpoint_uri)
def testDeployModelForAIPPredictionWithCustomRuntime(self, mock_discovery):
mock_discovery.build.return_value = self._mock_api_client
self._setUpPredictionMocks()
self._ai_platform_serving_args['runtime_version'] = '1.23.45'
runner.deploy_model_for_aip_prediction(self._serving_path,
self._model_version,
self._ai_platform_serving_args,
self._executor_class_path)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: self._executor_class_path}):
labels = telemetry_utils.get_labels_dict()
expected_versions_create_body = {
'name': self._model_version,
'deployment_uri': self._serving_path,
'runtime_version': '1.23.45',
'python_version': runner._get_caip_python_version('1.23.45'),
'labels': labels,
}
self._assertDeployModelMockCalls(
expected_versions_create_body=expected_versions_create_body)
def run(self, pipeline: pipeline_pb2.Pipeline) -> None:
"""Deploys given logical pipeline on Beam.
Args:
pipeline: Logical pipeline in IR format.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid deploying the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
run_id = datetime.datetime.now().isoformat()
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline, {
constants.PIPELINE_RUN_ID_PARAMETER_NAME: run_id,
})
# TODO(b/163003901): Support beam DAG runner args through IR.
deployment_config = self._extract_deployment_config(pipeline)
connection_config = deployment_config.metadata_connection_config
mlmd_connection = metadata.Metadata(
connection_config=connection_config)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'beam'}):
with beam.Pipeline() as p:
# Uses for triggering the component DoFns.
root = p | 'CreateRoot' >> beam.Create([None])
# Stores mapping of component to its signal.
signal_map = {}
# pipeline.components are in topological order.
for node in pipeline.nodes:
# TODO(b/160882349): Support subpipeline
pipeline_node = node.pipeline_node
component_id = pipeline_node.node_info.id
executor_spec = self._extract_executor_spec(
deployment_config, component_id)
custom_driver_spec = self._extract_custom_driver_spec(
deployment_config, component_id)
# Signals from upstream components.
signals_to_wait = []
for upstream_node in pipeline_node.upstream_nodes:
assert upstream_node in signal_map, (
'Components is not in '
'topological order')
signals_to_wait.append(signal_map[upstream_node])
logging.info(
'Component %s depends on %s.', component_id,
[s.producer.full_label for s in signals_to_wait])
# Each signal is an empty PCollection. AsIter ensures component will
# be triggered after upstream components are finished.
# LINT.IfChange
signal_map[component_id] = (
root
| 'Run[%s]' % component_id >> beam.ParDo(
self._PIPELINE_NODE_DO_FN_CLS(
pipeline_node=pipeline_node,
mlmd_connection=mlmd_connection,
pipeline_info=pipeline.pipeline_info,
pipeline_runtime_spec=pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec), *
[beam.pvalue.AsIter(s) for s in signals_to_wait]))
# LINT.ThenChange(../beam/beam_dag_runner.py)
logging.info('Component %s is scheduled.', component_id)
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]):
"""Overrides the tfx_pusher_executor.
Args:
input_dict: Input dict from input key to a list of artifacts, including:
- model_export: exported model from trainer.
- model_blessing: model blessing path from model_validator.
output_dict: Output dict from key to a list of artifacts, including:
- model_push: A list of 'ModelPushPath' artifact of size one. It will
include the model in this push execution if the model was pushed.
exec_properties: Mostly a passthrough input dict for
tfx.components.Pusher.executor. custom_config.bigquery_serving_args is
consumed by this class. For the full set of parameters supported by
Big Query ML, refer to https://cloud.google.com/bigquery-ml/
Returns:
None
Raises:
ValueError:
If bigquery_serving_args is not in exec_properties.custom_config.
If pipeline_root is not 'gs://...'
RuntimeError: if the Big Query job failed.
"""
self._log_startup(input_dict, output_dict, exec_properties)
model_push = artifact_utils.get_single_instance(
output_dict[tfx_pusher_executor.PUSHED_MODEL_KEY])
if not self.CheckBlessing(input_dict):
self._MarkNotPushed(model_push)
return
model_export = artifact_utils.get_single_instance(
input_dict[tfx_pusher_executor.MODEL_KEY])
model_export_uri = model_export.uri
custom_config = json_utils.loads(
exec_properties.get(_CUSTOM_CONFIG_KEY, 'null'))
if custom_config is not None and not isinstance(custom_config, Dict):
raise ValueError('custom_config in execution properties needs to be a '
'dict.')
bigquery_serving_args = custom_config.get(SERVING_ARGS_KEY)
# if configuration is missing error out
if bigquery_serving_args is None:
raise ValueError('Big Query ML configuration was not provided')
bq_model_uri = '.'.join([
bigquery_serving_args[_PROJECT_ID_KEY],
bigquery_serving_args[_BQ_DATASET_ID_KEY],
bigquery_serving_args[_MODEL_NAME_KEY],
])
# Deploy the model.
io_utils.copy_dir(
src=path_utils.serving_model_path(model_export_uri), dst=model_push.uri)
model_path = model_push.uri
if not model_path.startswith(_GCS_PREFIX):
raise ValueError('pipeline_root must be gs:// for BigQuery ML Pusher.')
logging.info('Deploying the model to BigQuery ML for serving: %s from %s',
bigquery_serving_args, model_path)
query = _BQML_CREATE_OR_REPLACE_MODEL_QUERY_TEMPLATE.format(
model_uri=bq_model_uri, model_path=model_path)
# TODO(zhitaoli): Refactor the executor_class_path creation into a common
# utility function.
executor_class_path = '%s.%s' % (self.__class__.__module__,
self.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
default_query_job_config = bigquery.job.QueryJobConfig(
labels=telemetry_utils.get_labels_dict())
client = bigquery.Client(default_query_job_config=default_query_job_config)
try:
query_job = client.query(query)
query_job.result() # Waits for the query to finish
except Exception as e:
raise RuntimeError('BigQuery ML Push failed: {}'.format(e))
logging.info('Successfully deployed model %s serving from %s', bq_model_uri,
model_path)
# Setting the push_destination to bigquery uri
self._MarkPushed(model_push, pushed_destination=bq_model_uri)
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
"""Runs batch inference on a given model with given input examples.
This function creates a new model (if necessary) and a new model version
before inference, and cleans up resources after inference. It provides
re-executability as it cleans up (only) the model resources that are created
during the process even inference job failed.
Args:
input_dict: Input dict from input key to a list of Artifacts.
- examples: examples for inference.
- model: exported model.
- model_blessing: model blessing result
output_dict: Output dict from output key to a list of Artifacts.
- output: bulk inference results.
exec_properties: A dict of execution properties.
- data_spec: JSON string of bulk_inferrer_pb2.DataSpec instance.
- custom_config: custom_config.ai_platform_serving_args need to contain
the serving job parameters sent to Google Cloud AI Platform. For the
full set of parameters, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
Returns:
None
"""
self._log_startup(input_dict, output_dict, exec_properties)
if 'examples' not in input_dict:
raise ValueError('\'examples\' is missing in input dict.')
if 'inference_result' not in output_dict:
raise ValueError('\'inference_result\' is missing in output dict.')
output = artifact_utils.get_single_instance(output_dict['inference_result'])
if 'model' not in input_dict:
raise ValueError('Input models are not valid, model '
'need to be specified.')
if 'model_blessing' in input_dict:
model_blessing = artifact_utils.get_single_instance(
input_dict['model_blessing'])
if not model_utils.is_model_blessed(model_blessing):
output.set_int_custom_property('inferred', 0)
logging.info('Model on %s was not blessed', model_blessing.uri)
return
else:
logging.info('Model blessing is not provided, exported model will be '
'used.')
if _CUSTOM_CONFIG_KEY not in exec_properties:
raise ValueError('Input exec properties are not valid, {} '
'need to be specified.'.format(_CUSTOM_CONFIG_KEY))
custom_config = json_utils.loads(
exec_properties.get(_CUSTOM_CONFIG_KEY, 'null'))
if custom_config is not None and not isinstance(custom_config, Dict):
raise ValueError('custom_config in execution properties needs to be a '
'dict.')
ai_platform_serving_args = custom_config.get(SERVING_ARGS_KEY)
if not ai_platform_serving_args:
raise ValueError(
'\'ai_platform_serving_args\' is missing in \'custom_config\'')
service_name, api_version = runner.get_service_name_and_api_version(
ai_platform_serving_args)
executor_class_path = '%s.%s' % (self.__class__.__module__,
self.__class__.__name__)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.get_labels_dict()
model = artifact_utils.get_single_instance(input_dict['model'])
model_path = path_utils.serving_model_path(model.uri)
logging.info('Use exported model from %s.', model_path)
# Use model artifact uri to generate model version to guarantee the
# 1:1 mapping from model version to model.
model_version = 'version_' + hashlib.sha256(model.uri.encode()).hexdigest()
inference_spec = self._get_inference_spec(model_path, model_version,
ai_platform_serving_args)
data_spec = bulk_inferrer_pb2.DataSpec()
json_format.Parse(exec_properties['data_spec'], data_spec)
api = discovery.build(service_name, api_version)
new_model_created = False
try:
new_model_created = runner.create_model_for_aip_prediction_if_not_exist(
api, job_labels, ai_platform_serving_args)
runner.deploy_model_for_aip_prediction(
api,
model_path,
model_version,
ai_platform_serving_args,
job_labels,
skip_model_creation=True,
set_default_version=False,
)
self._run_model_inference(data_spec, input_dict['examples'], output.uri,
inference_spec)
except Exception as e:
logging.error('Error in executing CloudAIBulkInferrerComponent: %s',
str(e))
output.set_int_custom_property('inferred', 0)
raise
finally:
# Guarantee newly created resources are cleaned up even if theinference
# job failed.
# Clean up the newly deployed model.
runner.delete_model_version_from_aip_if_exists(api, model_version,
ai_platform_serving_args)
if new_model_created:
runner.delete_model_from_aip_if_exists(api, ai_platform_serving_args)
# Mark the inferenence as successful after resources are cleaned up.
output.set_int_custom_property('inferred', 1)
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 = _make_beam_pipeline_args(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.TFX_RUNNER: 'kfp',
}):
execution_info = launcher.launch()
# Dump the UI metadata.
_dump_ui_metadata(component, execution_info)
def run(self, pipeline: Union[pipeline_pb2.Pipeline,
pipeline_py.Pipeline]) -> None:
"""Deploys given logical pipeline on Beam.
Args:
pipeline: Logical pipeline in IR format.
"""
# For CLI, while creating or updating pipeline, pipeline_args are extracted
# and hence we avoid deploying the pipeline.
if 'TFX_JSON_EXPORT_PIPELINE_ARGS_PATH' in os.environ:
return
if isinstance(pipeline, pipeline_py.Pipeline):
c = compiler.Compiler()
pipeline = c.compile(pipeline)
run_id = datetime.datetime.now().strftime('%Y%m%d-%H%M%S.%f')
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline, {
constants.PIPELINE_RUN_ID_PARAMETER_NAME: run_id,
})
deployment_config = self._extract_deployment_config(pipeline)
connection_config = self._connection_config_from_deployment_config(
deployment_config)
logging.info('Running pipeline:\n %s', pipeline)
logging.info('Using deployment config:\n %s', deployment_config)
logging.info('Using connection config:\n %s', connection_config)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_RUNNER: 'beam'}):
with beam.Pipeline() as p:
# Uses for triggering the node DoFns.
root = p | 'CreateRoot' >> beam.Create([None])
# Stores mapping of node to its signal.
signal_map = {}
# pipeline.nodes are in topological order.
for node in pipeline.nodes:
# TODO(b/160882349): Support subpipeline
pipeline_node = node.pipeline_node
node_id = pipeline_node.node_info.id
executor_spec = self._extract_executor_spec(
deployment_config, node_id)
custom_driver_spec = self._extract_custom_driver_spec(
deployment_config, node_id)
# Signals from upstream nodes.
signals_to_wait = []
for upstream_node in pipeline_node.upstream_nodes:
assert upstream_node in signal_map, ('Nodes are not in '
'topological order')
signals_to_wait.append(signal_map[upstream_node])
logging.info('Node %s depends on %s.', node_id,
[s.producer.full_label for s in signals_to_wait])
# Each signal is an empty PCollection. AsIter ensures a node will
# be triggered after upstream nodes are finished.
signal_map[node_id] = (
root
| 'Run[%s]' % node_id >> beam.ParDo(
self._PIPELINE_NODE_DO_FN_CLS(
pipeline_node=pipeline_node,
mlmd_connection_config=connection_config,
pipeline_info=pipeline.pipeline_info,
pipeline_runtime_spec=pipeline.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
deployment_config=deployment_config),
*[beam.pvalue.AsIter(s) for s in signals_to_wait]))
logging.info('Node %s is scheduled.', node_id)
def start_aip_training(input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text,
Any], executor_class_path: Text,
training_inputs: Dict[Text,
Any], job_id: Optional[Text]):
"""Start a trainer job on AI Platform (AIP).
This is done by forwarding the inputs/outputs/exec_properties to the
tfx.scripts.run_executor module on a AI Platform training job interpreter.
Args:
input_dict: Passthrough input dict for tfx.components.Trainer.executor.
output_dict: Passthrough input dict for tfx.components.Trainer.executor.
exec_properties: Passthrough input dict for tfx.components.Trainer.executor.
executor_class_path: class path for TFX core default trainer.
training_inputs: Training input argument for AI Platform training job.
'pythonModule', 'pythonVersion' and 'runtimeVersion' will be inferred. For
the full set of parameters, refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput
job_id: Job ID for AI Platform Training job. If not supplied,
system-determined unique ID is given. Refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#resource-job
Returns:
None
Raises:
RuntimeError: if the Google Cloud AI Platform training job failed/cancelled.
"""
training_inputs = training_inputs.copy()
json_inputs = artifact_utils.jsonify_artifact_dict(input_dict)
logging.info('json_inputs=\'%s\'.', json_inputs)
json_outputs = artifact_utils.jsonify_artifact_dict(output_dict)
logging.info('json_outputs=\'%s\'.', json_outputs)
json_exec_properties = json.dumps(exec_properties, sort_keys=True)
logging.info('json_exec_properties=\'%s\'.', json_exec_properties)
# Configure AI Platform training job
api_client = discovery.build('ml', 'v1')
# We use custom containers to launch training on AI Platform, which invokes
# the specified image using the container's entrypoint. The default
# entrypoint for TFX containers is to call scripts/run_executor.py. The
# arguments below are passed to this run_executor entry to run the executor
# specified in `executor_class_path`.
job_args = [
'--executor_class_path', executor_class_path, '--inputs', json_inputs,
'--outputs', json_outputs, '--exec-properties', json_exec_properties
]
if not training_inputs.get('masterConfig'):
training_inputs['masterConfig'] = {
'imageUri': _TFX_IMAGE,
}
training_inputs['args'] = job_args
# Pop project_id so AIP doesn't complain about an unexpected parameter.
# It's been a stowaway in aip_args and has finally reached its destination.
project = training_inputs.pop('project')
project_id = 'projects/{}'.format(project)
with telemetry_utils.scoped_labels(
{telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}):
job_labels = telemetry_utils.get_labels_dict()
# 'tfx_YYYYmmddHHMMSS' is the default job ID if not explicitly specified.
job_id = job_id or 'tfx_{}'.format(
datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
job_spec = {
'jobId': job_id,
'trainingInput': training_inputs,
'labels': job_labels,
}
# Submit job to AIP Training
logging.info('Submitting job=\'%s\', project=\'%s\' to AI Platform.', job_id,
project)
request = api_client.projects().jobs().create(
body=job_spec, parent=project_id)
request.execute()
# Wait for AIP Training job to finish
job_name = '{}/jobs/{}'.format(project_id, job_id)
request = api_client.projects().jobs().get(name=job_name)
response = request.execute()
retry_count = 0
# Monitors the long-running operation by polling the job state periodically,
# and retries the polling when a transient connectivity issue is encountered.
#
# Long-running operation monitoring:
# The possible states of "get job" response can be found at
# https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#State
# where SUCCEEDED/FAILED/CANCELLED are considered to be final states.
# The following logic will keep polling the state of the job until the job
# enters a final state.
#
# During the polling, if a connection error was encountered, the GET request
# will be retried by recreating the Python API client to refresh the lifecycle
# of the connection being used. See
# https://github.com/googleapis/google-api-python-client/issues/218
# for a detailed description of the problem. If the error persists for
# _CONNECTION_ERROR_RETRY_LIMIT consecutive attempts, the function will exit
# with code 1.
while response['state'] not in ('SUCCEEDED', 'FAILED', 'CANCELLED'):
time.sleep(_POLLING_INTERVAL_IN_SECONDS)
try:
response = request.execute()
retry_count = 0
# Handle transient connection error.
except ConnectionError as err:
if retry_count < _CONNECTION_ERROR_RETRY_LIMIT:
retry_count += 1
logging.warning(
'ConnectionError (%s) encountered when polling job: %s. Trying to '
'recreate the API client.', err, job_id)
# Recreate the Python API client.
api_client = discovery.build('ml', 'v1')
request = api_client.projects().jobs().get(name=job_name)
else:
# TODO(b/158433873): Consider raising the error instead of exit with
# code 1 after CMLE supports configurable retry policy.
# Currently CMLE will automatically retry the job unless return code
# 1-128 is returned.
logging.error('Request failed after %s retries.',
_CONNECTION_ERROR_RETRY_LIMIT)
sys.exit(1)
if response['state'] in ('FAILED', 'CANCELLED'):
err_msg = 'Job \'{}\' did not succeed. Detailed response {}.'.format(
job_name, response)
logging.error(err_msg)
raise RuntimeError(err_msg)
# AIP training complete
logging.info('Job \'%s\' successful.', job_name)
