def testLauncher_with_CustomDriver_ExistingSpan(self):
LauncherTest.fakeExampleGenOutput(self._mlmd_connection, self._example_gen,
2, 1)
test_launcher = launcher.Launcher(
pipeline_node=self._example_gen,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_driver_spec=self._custom_driver_spec,
custom_executor_operators=self._test_executor_operators)
_ = test_launcher.launch()
with self._mlmd_connection as m:
artifact = m.store.get_artifacts_by_type('Examples')[1]
self.assertProtoPartiallyEquals(
"""
id: 2
type_id: 4
custom_properties {
key: "name"
value {
string_value: ":test_run_0:my_example_gen:output_examples:0"
}
}
custom_properties {
key: "span"
value {
int_value: 2
}
}
custom_properties {
key: "version"
value {
int_value: 2
}
}
state: LIVE""",
artifact,
ignored_fields=[
'uri', 'create_time_since_epoch', 'last_update_time_since_epoch'
])
def testLauncher_EmptyOptionalInputTriggersExecution(self):
# In this test case, both inputs of trainer are mark as optional. So even
# when there is no input from them, the trainer can stil be triggered.
self._trainer.inputs.inputs['examples'].min_count = 0
self._trainer.inputs.inputs['transform_graph'].min_count = 0
test_launcher = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators)
execution_metadata = test_launcher.launch()
with self._mlmd_connection as m:
[artifact] = m.store.get_artifacts_by_type('Model')
self.assertProtoPartiallyEquals("""
id: 1
type_id: 6
custom_properties {
key: "name"
value {
string_value: ":test_run_0:my_trainer:model:0"
}
}
state: LIVE""",
artifact,
ignored_fields=[
'uri',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
[execution] = m.store.get_executions_by_id([execution_metadata.id])
self.assertProtoPartiallyEquals("""
id: 1
type_id: 4
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'create_time_since_epoch',
'last_update_time_since_epoch'
])
def testLauncher_CacheIsSupportedForNodeWithNoOutput(self):
# Even though a node has no output at all, the launcher should treat the
# second execution as CACHED as long as the cache context is the same.
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection,
self._example_gen, self._transform)
self._trainer.ClearField('outputs')
test_launcher = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators)
execution_info = test_launcher.launch()
with self._mlmd_connection as m:
[execution
] = m.store.get_executions_by_id([execution_info.execution_id])
self.assertProtoPartiallyEquals("""
id: 3
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'type_id',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
execution_info = test_launcher.launch()
with self._mlmd_connection as m:
[execution
] = m.store.get_executions_by_id([execution_info.execution_id])
self.assertProtoPartiallyEquals("""
id: 4
last_known_state: CACHED
""",
execution,
ignored_fields=[
'type_id',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
def testLauncher_ExecutionFailedViaReturnCode(self):
# In the case that the executor failed and raises an execption.
# An Execution will be published.
self.reloadPipelineWithNewRunId()
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection,
self._example_gen, self._transform)
executor_operators = {
_PYTHON_CLASS_EXECUTABLE_SPEC: _FakeErrorExecutorOperator
}
test_launcher = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=executor_operators)
with self.assertRaisesRegex(
Exception,
'Execution .* failed with error code .* and error message .*'):
_ = test_launcher.launch()
with self._mlmd_connection as m:
artifacts = m.store.get_artifacts_by_type('Model')
self.assertEmpty(artifacts)
executions = m.store.get_executions()
self.assertProtoPartiallyEquals("""
id: 3
last_known_state: FAILED
custom_properties {
key: '__execution_result__'
value {
string_value: '{\\n "resultMessage": "execution canceled.",\\n "code": 1\\n}'
}
}
""",
executions[-1],
ignored_fields=[
'type_id',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
def testLauncher_InputPartiallyReady(self):
# No new execution is triggered and registered if all inputs are not ready.
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection, self._example_gen,
None)
test_launcher = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators)
with self._mlmd_connection as m:
existing_exeuctions = m.store.get_executions()
execution_metadata = test_launcher.launch()
self.assertIsNone(execution_metadata)
with self._mlmd_connection as m:
# No new execution is registered in MLMD.
self.assertCountEqual(existing_exeuctions, m.store.get_executions())
def __init__(self,
pipeline_node: pipeline_pb2.PipelineNode,
mlmd_connection: metadata.Metadata,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec):
"""Initializes the _PipelineNodeAsDoFn.
Args:
pipeline_node: The specification of the node that this launcher lauches.
mlmd_connection: ML metadata connection. The connection is expected to
not be opened before launcher is initiated.
pipeline_info: The information of the pipeline that this node runs in.
pipeline_runtime_spec: The runtime information of the pipeline that this
node runs in.
"""
self._launcher = launcher.Launcher(
pipeline_node=pipeline_node,
mlmd_connection=mlmd_connection,
pipeline_info=pipeline_info,
pipeline_runtime_spec=pipeline_runtime_spec)
self._component_id = pipeline_node.node_info.id
def testLauncher_resolver_node(self):
mock_resolver_node_handler_class = mock.create_autospec(
system_node_handler.SystemNodeHandler)
mock_resolver_node_handler = mock.create_autospec(
system_node_handler.SystemNodeHandler, instance=True)
mock_resolver_node_handler_class.return_value = mock_resolver_node_handler
expected_execution_info = data_types.ExecutionInfo()
expected_execution_info.execution_id = 123
mock_resolver_node_handler.run.return_value = expected_execution_info
launcher._SYSTEM_NODE_HANDLERS[
'tfx.dsl.components.common.resolver.Resolver'] = (
mock_resolver_node_handler_class)
test_launcher = launcher.Launcher(
pipeline_node=self._resolver,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec)
execution_info = test_launcher.launch()
mock_resolver_node_handler.run.assert_called_once_with(
self._mlmd_connection, self._resolver, self._pipeline_info,
self._pipeline_runtime_spec)
self.assertEqual(execution_info, expected_execution_info)
def testLauncher_importer_node(self):
mock_import_node_handler_class = mock.create_autospec(
system_node_handler.SystemNodeHandler)
mock_import_node_handler = mock.create_autospec(
system_node_handler.SystemNodeHandler, instance=True)
mock_import_node_handler_class.return_value = mock_import_node_handler
expected_execution = metadata_store_pb2.Execution()
expected_execution.id = 123
mock_import_node_handler.run.return_value = expected_execution
launcher._SYSTEM_NODE_HANDLERS[
'tfx.components.common_nodes.importer_node.ImporterNode'] = (
mock_import_node_handler_class)
test_launcher = launcher.Launcher(
pipeline_node=self._importer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec)
execution_metadata = test_launcher.launch()
mock_import_node_handler.run.assert_called_once_with(
self._mlmd_connection, self._importer, self._pipeline_info,
self._pipeline_runtime_spec)
self.assertEqual(execution_metadata, expected_execution)
def testLauncher_CacheDisabled(self):
# In this test case, there are two executions:
# In the first one,trainer reads the fake upstream outputs and publish
# a new output.
# In the second one, because the enable_cache is false and inputs don't
# change. The launcher will published a new COMPLETE execution.
self._trainer.execution_options.caching_options.enable_cache = False
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection,
self._example_gen, self._transform)
test_launcher = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators)
execution_metadata = test_launcher.launch()
with self._mlmd_connection as m:
[artifact] = m.store.get_artifacts_by_type('Model')
self.assertProtoPartiallyEquals("""
id: 3
type_id: 10
custom_properties {
key: "name"
value {
string_value: ":test_run_0:my_trainer:model:0"
}
}
state: LIVE""",
artifact,
ignored_fields=[
'uri',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
[execution] = m.store.get_executions_by_id([execution_metadata.id])
self.assertProtoPartiallyEquals("""
id: 3
type_id: 8
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'create_time_since_epoch',
'last_update_time_since_epoch'
])
execution_metadata = test_launcher.launch()
with self._mlmd_connection as m:
artifacts = m.store.get_artifacts_by_type('Model')
self.assertLen(artifacts, 2)
self.assertProtoPartiallyEquals("""
id: 4
type_id: 10
custom_properties {
key: "name"
value {
string_value: ":test_run_0:my_trainer:model:0"
}
}
state: LIVE""",
artifacts[1],
ignored_fields=[
'uri',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
[execution] = m.store.get_executions_by_id([execution_metadata.id])
self.assertProtoPartiallyEquals("""
id: 4
type_id: 8
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'create_time_since_epoch',
'last_update_time_since_epoch'
])
def testLauncher_PublishingNewArtifactsAndUseCache(self):
# In this test case, there are two executions:
# In the first one,trainer reads the fake upstream outputs and publish
# a new output.
# In the second one, because the enable_cache is true and inputs don't
# change. The launcher will published a CACHED execution.
self.reloadPipelineWithNewRunId()
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection,
self._example_gen, self._transform)
execution_info = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators).launch()
with self._mlmd_connection as m:
[artifact] = m.store.get_artifacts_by_type('Model')
self.assertProtoPartiallyEquals("""
id: 3
custom_properties {
key: "name"
value {
string_value: ":test_run_%d:my_trainer:model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "0.123.4.dev"
}
}
state: LIVE""" % self._pipeline_run_id_counter,
artifact,
ignored_fields=[
'type_id', 'uri',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
[execution
] = m.store.get_executions_by_id([execution_info.execution_id])
self.assertProtoPartiallyEquals("""
id: 3
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'type_id',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
self.reloadPipelineWithNewRunId()
execution_info = launcher.Launcher(
pipeline_node=self._trainer,
mlmd_connection=self._mlmd_connection,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec,
executor_spec=self._trainer_executor_spec,
custom_executor_operators=self._test_executor_operators).launch()
with self._mlmd_connection as m:
[execution
] = m.store.get_executions_by_id([execution_info.execution_id])
self.assertProtoPartiallyEquals("""
id: 4
last_known_state: CACHED
""",
execution,
ignored_fields=[
'type_id',
'create_time_since_epoch',
'last_update_time_since_epoch'
])
def main(argv):
# 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_root', type=str, required=True)
parser.add_argument('--metadata_ui_path',
type=str,
required=False,
default='/mlpipeline-ui-metadata.json')
parser.add_argument('--kubeflow_metadata_config', type=str, required=True)
parser.add_argument('--tfx_ir', type=str, required=True)
parser.add_argument('--node_id', type=str, required=True)
# There might be multiple runtime parameters.
# `args.runtime_parameter` should become List[str] by using "append".
parser.add_argument('--runtime_parameter', type=str, action='append')
# TODO(b/196892362): Replace hooking with a more straightforward mechanism.
launcher._register_execution = _register_execution # pylint: disable=protected-access
args = parser.parse_args(argv)
tfx_ir = pipeline_pb2.Pipeline()
json_format.Parse(args.tfx_ir, tfx_ir)
_resolve_runtime_parameters(tfx_ir, args.runtime_parameter)
deployment_config = runner_utils.extract_local_deployment_config(tfx_ir)
kubeflow_metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
json_format.Parse(args.kubeflow_metadata_config, kubeflow_metadata_config)
metadata_connection = metadata.Metadata(
_get_metadata_connection_config(kubeflow_metadata_config))
node_id = args.node_id
# 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',
}):
custom_executor_operators = {
executable_spec_pb2.ContainerExecutableSpec:
kubernetes_executor_operator.KubernetesExecutorOperator
}
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id)
pipeline_node = _get_pipeline_node(tfx_ir, node_id)
component_launcher = launcher.Launcher(
pipeline_node=pipeline_node,
mlmd_connection=metadata_connection,
pipeline_info=tfx_ir.pipeline_info,
pipeline_runtime_spec=tfx_ir.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
custom_executor_operators=custom_executor_operators)
logging.info('Component %s is running.', node_id)
execution_info = component_launcher.launch()
logging.info('Component %s is finished.', node_id)
# Dump the UI metadata.
_dump_ui_metadata(pipeline_node, execution_info, args.metadata_ui_path)
def run(
self, pipeline: tfx_pipeline.Pipeline, run_name: Optional[str] = None
) -> None:
"""Runs given logical pipeline locally.
Args:
pipeline: Logical pipeline containing pipeline args and components.
run_name: Optional name for the run.
"""
for component in pipeline.components:
if isinstance(component, base_component.BaseComponent):
component._resolve_pip_dependencies(
pipeline.pipeline_info.pipeline_root
)
c = compiler.Compiler()
pipeline = c.compile(pipeline)
run_name = run_name or datetime.now().strftime("%d_%h_%y-%H_%M_%S_%f")
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
pipeline,
{
PIPELINE_RUN_ID_PARAMETER_NAME: run_name,
},
)
deployment_config = runner_utils.extract_local_deployment_config(
pipeline
)
connection_config = deployment_config.metadata_connection_config # type: ignore[attr-defined] # noqa
logger.debug(f"Using deployment config:\n {deployment_config}")
logger.debug(f"Using connection config:\n {connection_config}")
# Run each component. Note that the pipeline.components list is in
# topological order.
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,
)
start = time.time()
logger.info(f"Step `{node_id}` has started.")
component_launcher.launch()
end = time.time()
logger.info(
f"Step `{node_id}` has finished"
f" in {format_timedelta_pretty(end - start)}."
)
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_root', type=str, required=True)
parser.add_argument('--kubeflow_metadata_config', type=str, required=True)
parser.add_argument('--serialized_component', type=str, required=True)
parser.add_argument('--tfx_ir', type=str, required=True)
parser.add_argument('--node_id', type=str, required=True)
launcher._register_execution = _register_execution # pylint: disable=protected-access
args = parser.parse_args()
tfx_ir = pipeline_pb2.Pipeline()
json_format.Parse(args.tfx_ir, tfx_ir)
# Substitute the runtime parameter to be a concrete run_id
runtime_parameter_utils.substitute_runtime_parameter(
tfx_ir, {
constants.PIPELINE_RUN_ID_PARAMETER_NAME:
os.environ['WORKFLOW_ID'],
})
deployment_config = runner_utils.extract_local_deployment_config(tfx_ir)
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))
node_id = args.node_id
# 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',
}):
custom_executor_operators = {
executable_spec_pb2.ContainerExecutableSpec:
kubernetes_executor_operator.KubernetesExecutorOperator
}
executor_spec = runner_utils.extract_executor_spec(
deployment_config, node_id)
custom_driver_spec = runner_utils.extract_custom_driver_spec(
deployment_config, node_id)
pipeline_node = _get_pipeline_node(tfx_ir, node_id)
component_launcher = launcher.Launcher(
pipeline_node=pipeline_node,
mlmd_connection=metadata_connection,
pipeline_info=tfx_ir.pipeline_info,
pipeline_runtime_spec=tfx_ir.runtime_spec,
executor_spec=executor_spec,
custom_driver_spec=custom_driver_spec,
custom_executor_operators=custom_executor_operators)
logging.info('Component %s is running.', node_id)
execution_info = component_launcher.launch()
logging.info('Component %s is finished.', node_id)
# Dump the UI metadata.
_dump_ui_metadata(pipeline_node, execution_info)
def run_with_ir(
self,
pipeline: pipeline_pb2.Pipeline,
run_options: Optional[pipeline_pb2.RunOptions] = None,
) -> None:
"""Runs given pipeline locally.
Args:
pipeline: Pipeline IR containing pipeline args and components.
run_options: Optional args for the run.
Raises:
ValueError: If run_options is provided, and partial_run_options.from_nodes
and partial_run_options.to_nodes are both empty.
"""
# 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 = getattr(
deployment_config.metadata_connection_config,
deployment_config.metadata_connection_config.WhichOneof(
'connection_config'))
logging.info('Using deployment config:\n %s', deployment_config)
logging.info('Using connection config:\n %s', connection_config)
if run_options:
logging.info('Using run_options:\n %s', run_options)
pr_opts = run_options.partial_run
partial_run_utils.mark_pipeline(
pipeline,
from_nodes=pr_opts.from_nodes or None,
to_nodes=pr_opts.to_nodes or None,
snapshot_settings=pr_opts.snapshot_settings)
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
if pipeline_node.execution_options.HasField('skip'):
logging.info('Skipping component %s.', node_id)
continue
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)
if pipeline_node.execution_options.run.perform_snapshot:
with metadata.Metadata(connection_config) as mlmd_handle:
partial_run_utils.snapshot(mlmd_handle, pipeline)
component_launcher.launch()
logging.info('Component %s is finished.', node_id)