def resolve(
self,
pipeline_info: data_types.PipelineInfo,
metadata_handler: metadata.Metadata,
source_channels: Dict[Text, types.Channel],
) -> resolver.ResolveResult:
pipeline_context = metadata_handler.get_pipeline_context(pipeline_info)
if pipeline_context is None:
raise RuntimeError('Pipeline context absent for %s' %
pipeline_context)
candidate_dict = {}
for k, c in source_channels.items():
candidate_artifacts = metadata_handler.get_qualified_artifacts(
contexts=[pipeline_context],
type_name=c.type_name,
producer_component_id=c.producer_component_id,
output_key=c.output_key)
candidate_dict[k] = [
artifact_utils.deserialize_artifact(a.type, a.artifact)
for a in candidate_artifacts
]
resolved_dict = self._resolve(candidate_dict)
resolve_state_dict = {
k: bool(artifact_list)
for k, artifact_list in resolved_dict.items()
}
return resolver.ResolveResult(per_key_resolve_result=resolved_dict,
per_key_resolve_state=resolve_state_dict)
def resolve(
self,
pipeline_info: data_types.PipelineInfo,
metadata_handler: metadata.Metadata,
source_channels: Dict[Text, types.Channel],
) -> resolver.ResolveResult:
# First, checks whether we have exactly Model and ModelBlessing Channels.
model_channel_key = None
model_blessing_channel_key = None
assert len(source_channels) == 2, 'Expecting 2 input Channels'
for k, c in source_channels.items():
if issubclass(c.type, standard_artifacts.Model):
model_channel_key = k
elif issubclass(c.type, standard_artifacts.ModelBlessing):
model_blessing_channel_key = k
else:
raise RuntimeError('Only expecting Model or ModelBlessing, got %s' %
c.type)
assert model_channel_key is not None, 'Expecting Model as input'
assert model_blessing_channel_key is not None, ('Expecting ModelBlessing as'
' input')
model_channel = source_channels[model_channel_key]
model_blessing_channel = source_channels[model_blessing_channel_key]
# Gets the pipeline context as the artifact search space.
pipeline_context = metadata_handler.get_pipeline_context(pipeline_info)
if pipeline_context is None:
raise RuntimeError('Pipeline context absent for %s' % pipeline_context)
candidate_dict = {}
# Gets all models in the search space and sort in reverse order by id.
all_models = metadata_handler.get_qualified_artifacts(
contexts=[pipeline_context],
type_name=model_channel.type_name,
producer_component_id=model_channel.producer_component_id,
output_key=model_channel.output_key)
candidate_dict[model_channel_key] = [
artifact_utils.deserialize_artifact(a.type, a.artifact)
for a in all_models
]
# Gets all ModelBlessing artifacts in the search space.
all_model_blessings = metadata_handler.get_qualified_artifacts(
contexts=[pipeline_context],
type_name=model_blessing_channel.type_name,
producer_component_id=model_blessing_channel.producer_component_id,
output_key=model_blessing_channel.output_key)
candidate_dict[model_blessing_channel_key] = [
artifact_utils.deserialize_artifact(a.type, a.artifact)
for a in all_model_blessings
]
resolved_dict = self._resolve(candidate_dict, model_channel_key,
model_blessing_channel_key)
resolve_state_dict = {
k: bool(artifact_list) for k, artifact_list in resolved_dict.items()
}
return resolver.ResolveResult(
per_key_resolve_result=resolved_dict,
per_key_resolve_state=resolve_state_dict)