def fakeUpstreamOutputs(mlmd_connection: metadata.Metadata,
example_gen: pipeline_pb2.PipelineNode,
transform: pipeline_pb2.PipelineNode):
with mlmd_connection as m:
if example_gen:
# Publishes ExampleGen output.
output_example = types.Artifact(
example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example.uri = 'my_examples_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example],
})
if transform:
# Publishes Transform output.
output_transform_graph = types.Artifact(
transform.outputs.outputs['transform_graph'].artifact_spec.
type)
output_example.uri = 'my_transform_graph_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, transform.contexts)
execution = execution_publish_utils.register_execution(
m, transform.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'transform_graph': [output_transform_graph],
})
def generate_resolved_info(metadata_handler: metadata.Metadata,
node: pipeline_pb2.PipelineNode) -> ResolvedInfo:
"""Returns a `ResolvedInfo` object for executing the node.
Args:
metadata_handler: A handler to access MLMD db.
node: The pipeline node for which to generate.
Returns:
A `ResolvedInfo` with input resolutions.
"""
# Register node contexts.
contexts = context_lib.register_contexts_if_not_exists(
metadata_handler=metadata_handler, node_contexts=node.contexts)
# Resolve execution properties.
exec_properties = inputs_utils.resolve_parameters(
node_parameters=node.parameters)
# Resolve inputs.
input_artifacts = inputs_utils.resolve_input_artifacts(
metadata_handler=metadata_handler, node_inputs=node.inputs)
return ResolvedInfo(contexts=contexts,
exec_properties=exec_properties,
input_artifacts=input_artifacts)
def testRegisterContexts(self):
node_contexts = pipeline_pb2.NodeContexts()
self.load_proto_from_text('node_context_spec.pbtxt', node_contexts)
with metadata.Metadata(connection_config=self._connection_config) as m:
context_lib.register_contexts_if_not_exists(
metadata_handler=m, node_contexts=node_contexts)
# Duplicated call should succeed.
contexts = context_lib.register_contexts_if_not_exists(
metadata_handler=m, node_contexts=node_contexts)
self.assertProtoEquals(
"""
id: 1
name: 'my_context_type_one'
""", m.store.get_context_type('my_context_type_one'))
self.assertProtoEquals(
"""
id: 2
name: 'my_context_type_two'
""", m.store.get_context_type('my_context_type_two'))
self.assertEqual(
contexts[0],
m.store.get_context_by_type_and_name('my_context_type_one',
'my_context_one'))
self.assertEqual(
contexts[1],
m.store.get_context_by_type_and_name('my_context_type_one',
'my_context_two'))
self.assertEqual(
contexts[2],
m.store.get_context_by_type_and_name('my_context_type_two',
'my_context_three'))
self.assertEqual(
contexts[0].custom_properties['property_a'].int_value, 1)
self.assertEqual(
contexts[1].custom_properties['property_a'].int_value, 2)
self.assertEqual(
contexts[2].custom_properties['property_a'].int_value, 3)
self.assertEqual(
contexts[2].custom_properties['property_b'].string_value, '4')
def fake_trainer_output(mlmd_connection, trainer, execution=None):
"""Writes fake trainer output and execution to MLMD."""
with mlmd_connection as m:
output_trainer_model = types.Artifact(
trainer.outputs.outputs['model'].artifact_spec.type)
output_trainer_model.uri = 'my_trainer_model_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, trainer.contexts)
if not execution:
execution = execution_publish_utils.register_execution(
m, trainer.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'model': [output_trainer_model],
})
def fake_transform_output(mlmd_connection, transform, execution=None):
"""Writes fake transform output and execution to MLMD."""
with mlmd_connection as m:
output_transform_graph = types.Artifact(
transform.outputs.outputs['transform_graph'].artifact_spec.type)
output_transform_graph.uri = 'my_transform_graph_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, transform.contexts)
if not execution:
execution = execution_publish_utils.register_execution(
m, transform.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'transform_graph': [output_transform_graph],
})
def fake_example_gen_run(mlmd_connection, example_gen, span, version):
"""Writes fake example_gen output and successful execution to MLMD."""
with mlmd_connection as m:
output_example = types.Artifact(
example_gen.outputs.outputs['output_examples'].artifact_spec.type)
output_example.set_int_custom_property('span', span)
output_example.set_int_custom_property('version', version)
output_example.uri = 'my_examples_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example],
})
def testResolverWithResolverPolicy(self):
pipeline = pipeline_pb2.Pipeline()
self.load_proto_from_text(
os.path.join(self._testdata_dir,
'pipeline_for_input_resolver_test.pbtxt'), pipeline)
my_example_gen = pipeline.nodes[0].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
with metadata.Metadata(connection_config=connection_config) as m:
# Publishes first ExampleGen with two output channels. `output_examples`
# will be consumed by downstream Transform.
output_example_1 = types.Artifact(
my_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example_1.uri = 'my_examples_uri_1'
output_example_2 = types.Artifact(
my_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example_2.uri = 'my_examples_uri_2'
contexts = context_lib.register_contexts_if_not_exists(
m, my_example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, my_example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example_1, output_example_2],
})
my_transform.inputs.resolver_config.resolver_policy = (
pipeline_pb2.ResolverConfig.LATEST_ARTIFACT)
# Gets inputs for transform. Should get back what the first ExampleGen
# published in the `output_examples` channel.
transform_inputs = inputs_utils.resolve_input_artifacts(
m, my_transform.inputs)
self.assertEqual(len(transform_inputs), 1)
self.assertEqual(len(transform_inputs['examples']), 1)
self.assertProtoPartiallyEquals(
transform_inputs['examples'][0].mlmd_artifact,
output_example_2.mlmd_artifact,
ignored_fields=[
'create_time_since_epoch', 'last_update_time_since_epoch'
])
def fakeExampleGenOutput(mlmd_connection: metadata.Metadata,
example_gen: pipeline_pb2.PipelineNode, span: int,
version: int):
with mlmd_connection as m:
output_example = types.Artifact(
example_gen.outputs.outputs['output_examples'].artifact_spec.type)
output_example.set_int_custom_property('span', span)
output_example.set_int_custom_property('version', version)
output_example.uri = 'my_examples_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example],
})
def _generate_contexts(self, metadata_handler):
context_spec = pipeline_pb2.NodeContexts()
text_format.Parse(
"""
contexts {
type {name: 'pipeline_context'}
name {
field_value {string_value: 'my_pipeline'}
}
}
contexts {
type {name: 'component_context'}
name {
field_value {string_value: 'my_component'}
}
}""", context_spec)
return context_lib.register_contexts_if_not_exists(metadata_handler,
context_spec)
def testResolveInputArtifactsOutputKeyUnset(self):
pipeline = pipeline_pb2.Pipeline()
self.load_proto_from_text(
os.path.join(
self._testdata_dir,
'pipeline_for_input_resolver_test_output_key_unset.pbtxt'),
pipeline)
my_trainer = pipeline.nodes[0].pipeline_node
my_pusher = pipeline.nodes[1].pipeline_node
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
with metadata.Metadata(connection_config=connection_config) as m:
# Publishes Trainer with one output channels. `output_model`
# will be consumed by the Pusher in the different run.
output_model = types.Artifact(
my_trainer.outputs.outputs['model'].artifact_spec.type)
output_model.uri = 'my_output_model_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, my_trainer.contexts)
execution = execution_publish_utils.register_execution(
m, my_trainer.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'model': [output_model],
})
# Gets inputs for pusher. Should get back what the first Model
# published in the `output_model` channel.
pusher_inputs = inputs_utils.resolve_input_artifacts(
m, my_pusher.inputs)
self.assertEqual(len(pusher_inputs), 1)
self.assertEqual(len(pusher_inputs['model']), 1)
self.assertProtoPartiallyEquals(
output_model.mlmd_artifact,
pusher_inputs['model'][0].mlmd_artifact,
ignored_fields=[
'create_time_since_epoch', 'last_update_time_since_epoch'
])
def _prepare_execution(self) -> _PrepareExecutionResult:
"""Prepares inputs, outputs and execution properties for actual execution."""
# TODO(b/150979622): handle the edge case that the component get evicted
# between successful pushlish and stateful working dir being clean up.
# Otherwise following retries will keep failing because of duplicate
# publishes.
with self._mlmd_connection as m:
# 1.Prepares all contexts.
contexts = context_lib.register_contexts_if_not_exists(
metadata_handler=m, node_contexts=self._pipeline_node.contexts)
# 2. Resolves inputs an execution properties.
exec_properties = inputs_utils.resolve_parameters(
node_parameters=self._pipeline_node.parameters)
input_artifacts = inputs_utils.resolve_input_artifacts(
metadata_handler=m, node_inputs=self._pipeline_node.inputs)
# 3. If not all required inputs are met. Return ExecutionInfo with
# is_execution_needed being false. No publish will happen so down stream
# nodes won't be triggered.
if input_artifacts is None:
return _PrepareExecutionResult(
execution_info=data_types.ExecutionInfo(),
contexts=contexts,
is_execution_needed=False)
# 4. Registers execution in metadata.
execution = execution_publish_utils.register_execution(
metadata_handler=m,
execution_type=self._pipeline_node.node_info.type,
contexts=contexts,
input_artifacts=input_artifacts,
exec_properties=exec_properties)
# 5. Resolve output
output_artifacts = self._output_resolver.generate_output_artifacts(
execution.id)
# If there is a custom driver, runs it.
if self._driver_operator:
driver_output = self._driver_operator.run_driver(
data_types.ExecutionInfo(
input_dict=input_artifacts,
output_dict=output_artifacts,
exec_properties=exec_properties,
execution_output_uri=self._output_resolver.
get_driver_output_uri()))
self._update_with_driver_output(driver_output, exec_properties,
output_artifacts)
# We reconnect to MLMD here because the custom driver closes MLMD connection
# on returning.
with self._mlmd_connection as m:
# 6. Check cached result
cache_context = cache_utils.get_cache_context(
metadata_handler=m,
pipeline_node=self._pipeline_node,
pipeline_info=self._pipeline_info,
input_artifacts=input_artifacts,
output_artifacts=output_artifacts,
parameters=exec_properties)
contexts.append(cache_context)
cached_outputs = cache_utils.get_cached_outputs(
metadata_handler=m, cache_context=cache_context)
# 7. Should cache be used?
if (self._pipeline_node.execution_options.caching_options.
enable_cache and cached_outputs):
# Publishes cache result
execution_publish_utils.publish_cached_execution(
metadata_handler=m,
contexts=contexts,
execution_id=execution.id,
output_artifacts=cached_outputs)
return _PrepareExecutionResult(
execution_info=data_types.ExecutionInfo(
execution_id=execution.id),
execution_metadata=execution,
contexts=contexts,
is_execution_needed=False)
pipeline_run_id = (self._pipeline_runtime_spec.pipeline_run_id.
field_value.string_value)
# 8. Going to trigger executor.
return _PrepareExecutionResult(
execution_info=data_types.ExecutionInfo(
execution_id=execution.id,
input_dict=input_artifacts,
output_dict=output_artifacts,
exec_properties=exec_properties,
execution_output_uri=self._output_resolver.
get_executor_output_uri(execution.id),
stateful_working_dir=(self._output_resolver.
get_stateful_working_directory()),
tmp_dir=self._output_resolver.make_tmp_dir(execution.id),
pipeline_node=self._pipeline_node,
pipeline_info=self._pipeline_info,
pipeline_run_id=pipeline_run_id),
execution_metadata=execution,
contexts=contexts,
is_execution_needed=True)
def testResolverInputsArtifacts(self):
pipeline = pipeline_pb2.Pipeline()
self.load_proto_from_text(
os.path.join(self._testdata_dir,
'pipeline_for_input_resolver_test.pbtxt'), pipeline)
my_example_gen = pipeline.nodes[0].pipeline_node
another_example_gen = pipeline.nodes[1].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
my_trainer = pipeline.nodes[3].pipeline_node
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
with metadata.Metadata(connection_config=connection_config) as m:
# Publishes first ExampleGen with two output channels. `output_examples`
# will be consumed by downstream Transform.
output_example = types.Artifact(
my_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example.uri = 'my_examples_uri'
side_examples = types.Artifact(
my_example_gen.outputs.outputs['side_examples'].artifact_spec.
type)
side_examples.uri = 'side_examples_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, my_example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, my_example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example],
'another_examples': [side_examples]
})
# Publishes second ExampleGen with one output channel with the same output
# key as the first ExampleGen. However this is not consumed by downstream
# nodes.
another_output_example = types.Artifact(
another_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
another_output_example.uri = 'another_examples_uri'
contexts = context_lib.register_contexts_if_not_exists(
m, another_example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, another_example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [another_output_example],
})
# Gets inputs for transform. Should get back what the first ExampleGen
# published in the `output_examples` channel.
transform_inputs = inputs_utils.resolve_input_artifacts(
m, my_transform.inputs)
self.assertEqual(len(transform_inputs), 1)
self.assertEqual(len(transform_inputs['examples']), 1)
self.assertProtoPartiallyEquals(
transform_inputs['examples'][0].mlmd_artifact,
output_example.mlmd_artifact,
ignored_fields=[
'create_time_since_epoch', 'last_update_time_since_epoch'
])
# Tries to resolve inputs for trainer. As trainer also requires min_count
# for both input channels (from example_gen and from transform) but we did
# not publish anything from transform, it should return nothing.
self.assertIsNone(
inputs_utils.resolve_input_artifacts(m, my_trainer.inputs))
def run(
self, mlmd_connection: metadata.Metadata,
pipeline_node: pipeline_pb2.PipelineNode,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec
) -> metadata_store_pb2.Execution:
"""Runs Importer specific logic.
Args:
mlmd_connection: ML metadata connection.
pipeline_node: The specification of the node that this launcher lauches.
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.
Returns:
The execution of the run.
"""
logging.info('Running as an importer node.')
with mlmd_connection as m:
# 1.Prepares all contexts.
contexts = context_lib.register_contexts_if_not_exists(
metadata_handler=m, node_contexts=pipeline_node.contexts)
# 2. Resolves execution properties, please note that importers has no
# input.
exec_properties = inputs_utils.resolve_parameters(
node_parameters=pipeline_node.parameters)
# 3. Registers execution in metadata.
execution = execution_publish_utils.register_execution(
metadata_handler=m,
execution_type=pipeline_node.node_info.type,
contexts=contexts,
exec_properties=exec_properties)
# 4. Generate output artifacts to represent the imported artifacts.
output_spec = pipeline_node.outputs.outputs[
importer_node.IMPORT_RESULT_KEY]
properties = self._extract_proto_map(
output_spec.artifact_spec.additional_properties)
custom_properties = self._extract_proto_map(
output_spec.artifact_spec.additional_custom_properties)
output_artifact_class = types.Artifact(
output_spec.artifact_spec.type).type
output_artifacts = importer_node.generate_output_dict(
metadata_handler=m,
uri=str(exec_properties[importer_node.SOURCE_URI_KEY]),
properties=properties,
custom_properties=custom_properties,
reimport=bool(
exec_properties[importer_node.REIMPORT_OPTION_KEY]),
output_artifact_class=output_artifact_class,
mlmd_artifact_type=output_spec.artifact_spec.type)
# 5. Publish the output artifacts.
execution_publish_utils.publish_succeeded_execution(
metadata_handler=m,
execution_id=execution.id,
contexts=contexts,
output_artifacts=output_artifacts)
return execution
def testSuccess(self):
with self._mlmd_connection as m:
# Publishes two models which will be consumed by downstream resolver.
output_model_1 = types.Artifact(
self._my_trainer.outputs.outputs['model'].artifact_spec.type)
output_model_1.uri = 'my_model_uri_1'
output_model_2 = types.Artifact(
self._my_trainer.outputs.outputs['model'].artifact_spec.type)
output_model_2.uri = 'my_model_uri_2'
contexts = context_lib.register_contexts_if_not_exists(
m, self._my_trainer.contexts)
execution = execution_publish_utils.register_execution(
m, self._my_trainer.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'model': [output_model_1, output_model_2],
})
handler = resolver_node_handler.ResolverNodeHandler()
execution_metadata = handler.run(
mlmd_connection=self._mlmd_connection,
pipeline_node=self._resolver_node,
pipeline_info=self._pipeline_info,
pipeline_runtime_spec=self._pipeline_runtime_spec)
with self._mlmd_connection as m:
# There is no way to directly verify the output artifact of the resolver
# So here a fake downstream component is created which listens to the
# resolver's output and we verify its input.
down_stream_node = text_format.Parse(
"""
inputs {
inputs {
key: "input_models"
value {
channels {
producer_node_query {
id: "my_resolver"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "my_pipeline"
}
}
}
context_queries {
type {
name: "component"
}
name {
field_value {
string_value: "my_resolver"
}
}
}
artifact_query {
type {
name: "Model"
}
}
output_key: "models"
}
min_count: 1
}
}
}
upstream_nodes: "my_resolver"
""", pipeline_pb2.PipelineNode())
downstream_input_artifacts = inputs_utils.resolve_input_artifacts(
metadata_handler=m, node_inputs=down_stream_node.inputs)
downstream_input_model = downstream_input_artifacts['input_models']
self.assertLen(downstream_input_model, 1)
self.assertProtoPartiallyEquals(
"""
id: 2
type_id: 5
uri: "my_model_uri_2"
state: LIVE""",
downstream_input_model[0].mlmd_artifact,
ignored_fields=[
'create_time_since_epoch', 'last_update_time_since_epoch'
])
[execution] = m.store.get_executions_by_id([execution_metadata.id])
self.assertProtoPartiallyEquals("""
id: 2
type_id: 6
last_known_state: COMPLETE
""",
execution,
ignored_fields=[
'create_time_since_epoch',
'last_update_time_since_epoch'
])
