def setUp(self):
super(PlaceholderUtilsTest, self).setUp()
examples = [standard_artifacts.Examples()]
examples[0].uri = "/tmp"
examples[0].split_names = artifact_utils.encode_split_names(
["train", "eval"])
self._serving_spec = infra_validator_pb2.ServingSpec()
self._serving_spec.tensorflow_serving.tags.extend(
["latest", "1.15.0-gpu"])
self._resolution_context = placeholder_utils.ResolutionContext(
exec_info=data_types.ExecutionInfo(
input_dict={
"model": [standard_artifacts.Model()],
"examples": examples,
},
output_dict={"blessing": [standard_artifacts.ModelBlessing()]},
exec_properties={
"proto_property":
json_format.MessageToJson(message=self._serving_spec,
sort_keys=True,
preserving_proto_field_name=True,
indent=0)
},
execution_output_uri="test_executor_output_uri",
stateful_working_dir="test_stateful_working_dir",
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
type=metadata_store_pb2.ExecutionType(
name="infra_validator"))),
pipeline_info=pipeline_pb2.PipelineInfo(
id="test_pipeline_id")),
executor_spec=executable_spec_pb2.PythonClassExecutableSpec(
class_path="test_class_path"),
)
# Resolution context to simulate missing optional values.
self._none_resolution_context = placeholder_utils.ResolutionContext(
exec_info=data_types.ExecutionInfo(
input_dict={
"model": [],
"examples": [],
},
output_dict={"blessing": []},
exec_properties={},
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
type=metadata_store_pb2.ExecutionType(
name="infra_validator"))),
pipeline_info=pipeline_pb2.PipelineInfo(
id="test_pipeline_id")),
executor_spec=None,
platform_config=None)
def _set_up_test_execution_info(self,
input_dict=None,
output_dict=None,
exec_properties=None):
return data_types.ExecutionInfo(
input_dict=input_dict or {},
output_dict=output_dict or {},
exec_properties=exec_properties or {},
execution_output_uri='/testing/executor/output/',
stateful_working_dir='/testing/stateful/dir',
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
type=metadata_store_pb2.ExecutionType(name='Docker_executor'))),
pipeline_info=pipeline_pb2.PipelineInfo(id='test_pipeline_id'))
def _set_up_test_execution_info(self,
input_dict=None,
output_dict=None,
exec_properties=None):
return data_types.ExecutionInfo(
execution_id=123,
input_dict=input_dict or {},
output_dict=output_dict or {},
exec_properties=exec_properties or {},
execution_output_uri='/testing/executor/output/',
stateful_working_dir='/testing/stateful/dir',
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
id='fakecomponent-fakecomponent')),
pipeline_info=pipeline_pb2.PipelineInfo(id='Test'),
pipeline_run_id='123')