def testGetArtifactsDict(self):
with metadata.Metadata(connection_config=self._connection_config) as m:
# Create and shuffle a few artifacts. The shuffled order should be
# retained in the output of `execution_lib.get_artifacts_dict`.
input_examples = []
for i in range(10):
input_example = standard_artifacts.Examples()
input_example.uri = 'example{}'.format(i)
input_example.type_id = common_utils.register_type_if_not_exist(
m, input_example.artifact_type).id
input_examples.append(input_example)
random.shuffle(input_examples)
output_models = []
for i in range(8):
output_model = standard_artifacts.Model()
output_model.uri = 'model{}'.format(i)
output_model.type_id = common_utils.register_type_if_not_exist(
m, output_model.artifact_type).id
output_models.append(output_model)
random.shuffle(output_models)
m.store.put_artifacts([
a.mlmd_artifact
for a in itertools.chain(input_examples, output_models)
])
execution = execution_lib.prepare_execution(
m,
metadata_store_pb2.ExecutionType(name='my_execution_type'),
state=metadata_store_pb2.Execution.RUNNING)
contexts = self._generate_contexts(m)
input_artifacts_dict = {'examples': input_examples}
output_artifacts_dict = {'model': output_models}
execution = execution_lib.put_execution(
m,
execution,
contexts,
input_artifacts=input_artifacts_dict,
output_artifacts=output_artifacts_dict)
# Verify that the same artifacts are returned in the correct order.
artifacts_dict = execution_lib.get_artifacts_dict(
m, execution.id, metadata_store_pb2.Event.INPUT)
self.assertCountEqual(['examples'], list(artifacts_dict.keys()))
self.assertEqual([ex.uri for ex in input_examples],
[a.uri for a in artifacts_dict['examples']])
artifacts_dict = execution_lib.get_artifacts_dict(
m, execution.id, metadata_store_pb2.Event.OUTPUT)
self.assertCountEqual(['model'], list(artifacts_dict.keys()))
self.assertEqual([model.uri for model in output_models],
[a.uri for a in artifacts_dict['model']])
def get_all_node_artifacts(
pipeline: pipeline_pb2.Pipeline, mlmd_handle: metadata.Metadata
) -> Dict[str, Dict[int, Dict[str, List[metadata_store_pb2.Artifact]]]]:
"""Returns all output artifacts of all pipeline nodes if present.
Args:
pipeline: Pipeline proto associated with a `PipelineState` object.
mlmd_handle: Handle to MLMD db.
Returns:
Dict of node id to Dict of execution id to Dict of key to output artifact
list.
"""
executions_dict = get_all_node_executions(pipeline, mlmd_handle)
result = {}
for node_id, executions in executions_dict.items():
node_artifacts = {}
for execution in executions:
execution_artifacts = {}
for key, artifacts in execution_lib.get_artifacts_dict(
mlmd_handle, execution.id, [
metadata_store_pb2.Event.OUTPUT,
metadata_store_pb2.Event.DECLARED_OUTPUT
]).items():
execution_artifacts[key] = [
artifact.mlmd_artifact for artifact in artifacts
]
node_artifacts[execution.id] = execution_artifacts
result[node_id] = node_artifacts
return result
def _generate_task_from_execution(
metadata_handler: metadata.Metadata, pipeline: pipeline_pb2.Pipeline,
node: pipeline_pb2.PipelineNode,
execution: metadata_store_pb2.Execution) -> task_lib.Task:
"""Generates `ExecNodeTask` given execution."""
contexts = metadata_handler.store.get_contexts_by_execution(execution.id)
exec_properties = _extract_properties(execution)
input_artifacts = execution_lib.get_artifacts_dict(
metadata_handler, execution.id, metadata_store_pb2.Event.INPUT)
outputs_resolver = outputs_utils.OutputsResolver(node,
pipeline.pipeline_info,
pipeline.runtime_spec,
pipeline.execution_mode)
return task_lib.ExecNodeTask(
node_uid=task_lib.NodeUid.from_pipeline_node(pipeline, node),
execution=execution,
contexts=contexts,
exec_properties=exec_properties,
input_artifacts=input_artifacts,
output_artifacts=outputs_resolver.generate_output_artifacts(
execution.id),
executor_output_uri=outputs_resolver.get_executor_output_uri(
execution.id),
stateful_working_dir=outputs_resolver.get_stateful_working_directory(
execution.id))
def _cache_and_publish(self,
existing_execution: metadata_store_pb2.Execution):
"""Updates MLMD."""
cached_execution_contexts = self._get_cached_execution_contexts(
existing_execution)
# Check if there are any previous attempts to cache and publish.
prev_cache_executions = (
execution_lib.get_executions_associated_with_all_contexts(
self._mlmd, contexts=cached_execution_contexts))
if not prev_cache_executions:
new_execution = execution_publish_utils.register_execution(
self._mlmd,
execution_type=metadata_store_pb2.ExecutionType(
id=existing_execution.type_id),
contexts=cached_execution_contexts)
else:
if len(prev_cache_executions) > 1:
logging.warning(
'More than one previous cache executions seen when attempting '
'reuse_node_outputs: %s', prev_cache_executions)
if (prev_cache_executions[-1].last_known_state ==
metadata_store_pb2.Execution.CACHED):
return
else:
new_execution = prev_cache_executions[-1]
output_artifacts = execution_lib.get_artifacts_dict(
self._mlmd,
existing_execution.id,
event_types=list(event_lib.VALID_OUTPUT_EVENT_TYPES))
execution_publish_utils.publish_cached_execution(
self._mlmd,
contexts=cached_execution_contexts,
execution_id=new_execution.id,
output_artifacts=output_artifacts)
def testGetArtifactsDict(self):
with metadata.Metadata(connection_config=self._connection_config) as m:
# Create and shuffle a few artifacts. The shuffled order should be
# retained in the output of `execution_lib.get_artifacts_dict`.
input_artifact_keys = ('input1', 'input2', 'input3')
input_artifacts_dict = collections.OrderedDict()
for input_key in input_artifact_keys:
input_examples = []
for i in range(10):
input_example = standard_artifacts.Examples()
input_example.uri = f'{input_key}/example{i}'
input_example.type_id = common_utils.register_type_if_not_exist(
m, input_example.artifact_type).id
input_examples.append(input_example)
random.shuffle(input_examples)
input_artifacts_dict[input_key] = input_examples
output_models = []
for i in range(8):
output_model = standard_artifacts.Model()
output_model.uri = f'model{i}'
output_model.type_id = common_utils.register_type_if_not_exist(
m, output_model.artifact_type).id
output_models.append(output_model)
random.shuffle(output_models)
output_artifacts_dict = {'model': output_models}
# Store input artifacts only. Outputs will be saved in put_execution().
input_mlmd_artifacts = [
a.mlmd_artifact
for a in itertools.chain(*input_artifacts_dict.values())
]
artifact_ids = m.store.put_artifacts(input_mlmd_artifacts)
for artifact_id, mlmd_artifact in zip(artifact_ids,
input_mlmd_artifacts):
mlmd_artifact.id = artifact_id
execution = execution_lib.prepare_execution(
m,
metadata_store_pb2.ExecutionType(name='my_execution_type'),
state=metadata_store_pb2.Execution.RUNNING)
contexts = self._generate_contexts(m)
# Change the order of the OrderedDict to shuffle the order of input keys.
input_artifacts_dict.move_to_end('input1')
execution = execution_lib.put_execution(
m,
execution,
contexts,
input_artifacts=input_artifacts_dict,
output_artifacts=output_artifacts_dict)
# Verify that the same artifacts are returned in the correct order.
artifacts_dict = execution_lib.get_artifacts_dict(
m, execution.id, [metadata_store_pb2.Event.INPUT])
self.assertEqual(set(input_artifact_keys),
set(artifacts_dict.keys()))
for key in artifacts_dict:
self.assertEqual([ex.uri for ex in input_artifacts_dict[key]],
[a.uri for a in artifacts_dict[key]],
f'for key={key}')
artifacts_dict = execution_lib.get_artifacts_dict(
m, execution.id, [metadata_store_pb2.Event.OUTPUT])
self.assertEqual({'model'}, set(artifacts_dict.keys()))
self.assertEqual([model.uri for model in output_models],
[a.uri for a in artifacts_dict['model']])
