def testResolveInputsArtifacts(self):
pipeline = self.load_pipeline_proto(
'pipeline_for_input_resolver_test.pbtxt')
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
with self.get_metadata() as m:
# Publishes first ExampleGen with two output channels. `output_examples`
# will be consumed by downstream Transform.
output_example = self.make_examples(uri='my_examples_uri')
side_examples = self.make_examples(uri='side_examples_uri')
output_artifacts = self.fake_execute(m,
my_example_gen,
input_map=None,
output_map={
'output_examples':
[output_example],
'another_examples':
[side_examples]
})
output_example = output_artifacts['output_examples'][0]
side_examples = output_artifacts['another_examples'][0]
# 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 = self.make_examples(
uri='another_examples_uri')
self.fake_execute(
m,
another_example_gen,
input_map=None,
output_map={'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.assertArtifactMapEqual({'examples': [output_example]},
transform_inputs)
# 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))
# Tries to resolve inputs for transform after adding a new context query
# to the input spec that refers to a non-existent context. Inputs cannot
# be resolved in this case.
context_query = my_transform.inputs.inputs['examples'].channels[
0].context_queries.add()
context_query.type.name = 'non_existent_context'
context_query.name.field_value.string_value = 'non_existent_context'
transform_inputs = inputs_utils.resolve_input_artifacts(
m, my_transform.inputs)
self.assertIsNone(transform_inputs)
def testResolveInputArtifacts_NonDictArg(self):
self._setup_pipeline_for_input_resolver_test()
self._append_resolver_step(self._my_transform, DuplicateOp)
self._append_resolver_step(self._my_transform, IdentityStrategy)
with self.assertRaisesRegex(TypeError, 'Invalid argument type'):
inputs_utils.resolve_input_artifacts(self._metadata_handler,
self._my_transform.inputs)
def testResolveInputArtifacts_NonDictOutput(self):
self._setup_pipeline_for_input_resolver_test()
self._append_resolver_step(self._my_transform, BadOutputStrategy)
with self.assertRaisesRegex(TypeError,
'Invalid input resolution result'):
inputs_utils.resolve_input_artifacts(self._metadata_handler,
self._my_transform.inputs)
def testResolverWithLatestArtifactStrategy(self):
pipeline = self.load_pipeline_proto(
'pipeline_for_input_resolver_test.pbtxt')
my_example_gen = pipeline.nodes[0].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
with self.get_metadata() as m:
# Publishes first ExampleGen with two output channels. `output_examples`
# will be consumed by downstream Transform.
output_example_1 = self.make_examples(uri='my_examples_uri_1')
output_example_2 = self.make_examples(uri='my_examples_uri_2')
output_artifacts = self.fake_execute(
m,
my_example_gen,
input_map=None,
output_map={
'output_examples': [output_example_1, output_example_2]
})
output_example_1 = output_artifacts['output_examples'][0]
output_example_2 = output_artifacts['output_examples'][1]
transform_resolver = (
my_transform.inputs.resolver_config.resolver_steps.add())
transform_resolver.class_path = (
'tfx.dsl.input_resolution.strategies.latest_artifact_strategy'
'.LatestArtifactStrategy')
transform_resolver.config_json = '{}'
# 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.assertArtifactMapEqual({'examples': [output_example_2]},
transform_inputs)
def testResolveInputArtifacts_SkippingStrategy(self):
self._setup_pipeline_for_input_resolver_test()
self._append_resolver_step(self._my_transform, SkippingStrategy)
result = inputs_utils.resolve_input_artifacts(
self._metadata_handler, self._my_transform.inputs)
self.assertIsNone(result)
def run(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text,
Any]) -> driver_output_pb2.DriverOutput:
# Fake a constant span number, which, on prod, is usually calculated based
# on date.
span = 2
with self._mlmd_connection as m:
previous_output = inputs_utils.resolve_input_artifacts(
m, self._self_output)
# Version should be the max of existing version + 1 if span exists,
# otherwise 0.
version = 0
if previous_output:
version = max([
artifact.get_int_custom_property('version')
for artifact in previous_output['examples']
if artifact.get_int_custom_property('span') == span
] or [-1]) + 1
output_example = copy.deepcopy(
output_dict['output_examples'][0].mlmd_artifact)
output_example.custom_properties['span'].int_value = span
output_example.custom_properties['version'].int_value = version
result = driver_output_pb2.DriverOutput()
result.output_artifacts['output_examples'].artifacts.append(
output_example)
result.exec_properties['span'].int_value = span
result.exec_properties['version'].int_value = version
return result
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 testResolveInputArtifacts_MixedStrategyAndOp(self):
self._setup_pipeline_for_input_resolver_test()
self._append_resolver_step(self._my_transform, IdentityStrategy)
self._append_resolver_step(self._my_transform, IdentityOp)
result = inputs_utils.resolve_input_artifacts(
self._metadata_handler, self._my_transform.inputs)
self.assertArtifactMapEqual({'examples': self._examples}, result)
def testLatestArtifacts_withInputKeys(self):
pipeline = self.load_pipeline_proto(
'pipeline_for_input_resolver_test.pbtxt')
my_example_gen = pipeline.nodes[0].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
my_trainer = pipeline.nodes[3].pipeline_node
# Use LatestArtifactStrategy for TransformGraph only.
resolver = my_trainer.inputs.resolver_config.resolver_steps.add()
resolver.class_path = (
'tfx.dsl.input_resolution.strategies.latest_artifact_strategy'
'.LatestArtifactStrategy')
resolver.config_json = '{}'
resolver.input_keys.append('transform_graph')
with self.get_metadata() as m:
ex1 = self.make_examples(uri='examples/1')
ex2 = self.make_examples(uri='examples/2')
tf1 = self.make_transform_graph(uri='transform_graph/1')
tf2 = self.make_transform_graph(uri='transform_graph/2')
output_artifacts = self.fake_execute(
m,
my_example_gen,
input_map=None,
output_map={'output_examples': [ex1]})
ex1 = output_artifacts['output_examples'][0]
output_artifacts = self.fake_execute(
m,
my_example_gen,
input_map=None,
output_map={'output_examples': [ex2]})
ex2 = output_artifacts['output_examples'][0]
output_artifacts = self.fake_execute(
m,
my_transform,
input_map={'examples': [ex1]},
output_map={'transform_graph': [tf1]})
tf1 = output_artifacts['transform_graph'][0]
output_artifacts = self.fake_execute(
m,
my_transform,
input_map={'examples': [ex2]},
output_map={'transform_graph': [tf2]})
tf2 = output_artifacts['transform_graph'][0]
result = inputs_utils.resolve_input_artifacts(
metadata_handler=m, node_inputs=my_trainer.inputs)
# "examples" input channel doesn't go through the resolver and its order is
# undeterministic. Sort artifacts by ID for testing convenience.
result['examples'].sort(key=lambda a: a.id)
self.assertArtifactMapEqual(
{
'examples': [ex1, ex2],
'transform_graph': [tf2]
}, result)
def run(
self, mlmd_connection: metadata.Metadata,
pipeline_node: pipeline_pb2.PipelineNode,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec
) -> data_types.ExecutionInfo:
"""Runs Resolver 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 resolver node.')
with mlmd_connection as m:
# 1.Prepares all contexts.
contexts = context_lib.prepare_contexts(
metadata_handler=m, node_contexts=pipeline_node.contexts)
# 2. Resolves inputs an execution properties.
exec_properties = inputs_utils.resolve_parameters(
node_parameters=pipeline_node.parameters)
input_artifacts = inputs_utils.resolve_input_artifacts(
metadata_handler=m, node_inputs=pipeline_node.inputs)
# 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. Publish the execution as a cached execution with
# resolved input artifact as the output artifacts.
execution_publish_utils.publish_internal_execution(
metadata_handler=m,
contexts=contexts,
execution_id=execution.id,
output_artifacts=input_artifacts)
return data_types.ExecutionInfo(execution_id=execution.id,
input_dict=input_artifacts,
output_dict=input_artifacts,
exec_properties=exec_properties,
pipeline_node=pipeline_node,
pipeline_info=pipeline_info)
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 testLatestUnprocessedArtifacts_NoneIfEverythingProcessed(self):
pipeline = self.load_pipeline_proto(
'pipeline_for_input_resolver_test.pbtxt')
my_example_gen = pipeline.nodes[0].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
resolver1 = my_transform.inputs.resolver_config.resolver_steps.add()
resolver1.class_path = (
'tfx.dsl.resolvers.unprocessed_artifacts_resolver'
'.UnprocessedArtifactsResolver')
resolver1.config_json = '{"execution_type_name": "Transform"}'
resolver2 = my_transform.inputs.resolver_config.resolver_steps.add()
resolver2.class_path = (
'tfx.dsl.input_resolution.strategies.latest_artifact_strategy'
'.LatestArtifactStrategy')
resolver2.config_json = '{}'
with self.get_metadata() as m:
ex1 = self.make_examples(uri='a')
ex2 = self.make_examples(uri='b')
output_artifacts = self.fake_execute(
m,
my_example_gen,
input_map=None,
output_map={'output_examples': [ex1]})
ex1 = output_artifacts['output_examples'][0]
output_artifacts = self.fake_execute(
m,
my_example_gen,
input_map=None,
output_map={'output_examples': [ex2]})
ex2 = output_artifacts['output_examples'][0]
self.fake_execute(m,
my_transform,
input_map={'examples': [ex1]},
output_map=None)
self.fake_execute(m,
my_transform,
input_map={'examples': [ex2]},
output_map=None)
result = inputs_utils.resolve_input_artifacts(
metadata_handler=m, node_inputs=my_transform.inputs)
self.assertIsNone(result)
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 testResolveInputArtifactsOutputKeyUnset(self):
pipeline = self.load_pipeline_proto(
'pipeline_for_input_resolver_test_output_key_unset.pbtxt')
my_trainer = pipeline.nodes[0].pipeline_node
my_pusher = pipeline.nodes[1].pipeline_node
with self.get_metadata() as m:
# Publishes Trainer with one output channels. `output_model`
# will be consumed by the Pusher in the different run.
output_model = self.make_model(uri='my_output_model_uri')
output_artifacts = self.fake_execute(
m,
my_trainer,
input_map=None,
output_map={'model': [output_model]})
output_model = output_artifacts['model'][0]
# 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.assertArtifactMapEqual({'model': [output_model]},
pusher_inputs)
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 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.prepare_contexts(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'
])
def testLauncher_ReEntry(self):
# Some executors or runtime environment may reschedule the launcher job
# before the launcher job can publish any results of the execution to MLMD.
# The launcher should reuse the previous execution and proceed to a
# successful execution.
self.reloadPipelineWithNewRunId()
LauncherTest.fakeUpstreamOutputs(self._mlmd_connection,
self._example_gen, self._transform)
def create_test_launcher(executor_operators):
return 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)
test_launcher = create_test_launcher(
{_PYTHON_CLASS_EXECUTABLE_SPEC: _FakeCrashingExecutorOperator})
# The first launch simulates the launcher being restarted by preventing the
# publishing of any results to MLMD.
with contextlib.ExitStack() as stack:
stack.enter_context(
mock.patch.object(test_launcher, '_publish_failed_execution'))
stack.enter_context(
mock.patch.object(test_launcher,
'_clean_up_stateless_execution_info'))
stack.enter_context(self.assertRaises(FakeError))
test_launcher.launch()
# Retrieve execution from the first launch, which should be in RUNNING
# state.
with self._mlmd_connection as m:
contexts = context_lib.prepare_contexts(
metadata_handler=m,
node_contexts=test_launcher._pipeline_node.contexts)
exec_properties = data_types_utils.build_parsed_value_dict(
inputs_utils.resolve_parameters_with_schema(
node_parameters=test_launcher._pipeline_node.parameters))
input_artifacts = inputs_utils.resolve_input_artifacts(
metadata_handler=m,
node_inputs=test_launcher._pipeline_node.inputs)
first_execution = test_launcher._register_or_reuse_execution(
metadata_handler=m,
contexts=contexts,
input_artifacts=input_artifacts,
exec_properties=exec_properties)
self.assertEqual(first_execution.last_known_state,
metadata_store_pb2.Execution.RUNNING)
# Create a second test launcher. It should reuse the previous execution.
second_test_launcher = create_test_launcher(
{_PYTHON_CLASS_EXECUTABLE_SPEC: _FakeExecutorOperator})
execution_info = second_test_launcher.launch()
with self._mlmd_connection as m:
self.assertEqual(first_execution.id, execution_info.execution_id)
executions = m.store.get_executions_by_id(
[execution_info.execution_id])
self.assertLen(executions, 1)
self.assertEqual(executions.pop().last_known_state,
metadata_store_pb2.Execution.COMPLETE)
# Create a third test launcher. It should not require an execution.
third_test_launcher = create_test_launcher(
{_PYTHON_CLASS_EXECUTABLE_SPEC: _FakeExecutorOperator})
execution_preparation_result = third_test_launcher._prepare_execution()
self.assertFalse(execution_preparation_result.is_execution_needed)
