def _build_latest_artifact_resolver(
self) -> List[pipeline_pb2.PipelineTaskSpec]:
"""Builds a resolver spec for a latest artifact resolver.
Returns:
A list of two PipelineTaskSpecs. One represents the query for latest valid
ModelBlessing artifact. Another one represents the query for latest
blessed Model artifact.
Raises:
ValueError: when desired_num_of_artifacts != 1. 1 is the only supported
value currently.
"""
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.CopyFrom(
pipeline_pb2.PipelineTaskInfo(name=self._name))
executor_label = _EXECUTOR_LABEL_PATTERN.format(self._name)
task_spec.executor_label = executor_label
# Fetch the init kwargs for the resolver.
resolver_config = self._exec_properties[resolver.RESOLVER_CONFIG]
if (isinstance(resolver_config, dict)
and resolver_config.get('desired_num_of_artifacts', 0) > 1):
raise ValueError(
'Only desired_num_of_artifacts=1 is supported currently.'
' Got {}'.format(
resolver_config.get('desired_num_of_artifacts')))
# Specify the outputs of the task.
for name, output_channel in self._outputs.items():
# Currently, we're working under the assumption that for tasks
# (those generated by BaseComponent), each channel contains a single
# artifact.
output_artifact_spec = compiler_utils.build_output_artifact_spec(
output_channel)
task_spec.outputs.artifacts[name].CopyFrom(output_artifact_spec)
# Specify the input parameters of the task.
for k, v in compiler_utils.build_input_parameter_spec(
self._exec_properties).items():
task_spec.inputs.parameters[k].CopyFrom(v)
artifact_queries = {}
# Buid the artifact query for each channel in the input dict.
for name, c in self._inputs.items():
query_filter = ("artifact_type='{type}' and state={state}").format(
type=compiler_utils.get_artifact_title(c.type),
state=metadata_store_pb2.Artifact.State.Name(
metadata_store_pb2.Artifact.LIVE))
artifact_queries[name] = ResolverSpec.ArtifactQuerySpec(
filter=query_filter)
resolver_spec = ResolverSpec(output_artifact_queries=artifact_queries)
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
executor.resolver.CopyFrom(resolver_spec)
self._deployment_config.executors[executor_label].CopyFrom(executor)
return [task_spec]
def build(self) -> List[pipeline_pb2.PipelineTaskSpec]:
"""Builds a pipeline StepSpec given the node information.
Returns:
A list of PipelineTaskSpec messages corresponding to the node. For most of
the cases, the list contains a single element. The only exception is when
compiling latest blessed model resolver. One DSL node will be
split to two resolver specs to reflect the two-phased query execution.
Raises:
NotImplementedError: When the node being built is an InfraValidator.
"""
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.CopyFrom(
pipeline_pb2.PipelineTaskInfo(name=self._name))
executor_label = _EXECUTOR_LABEL_PATTERN.format(self._name)
task_spec.executor_label = executor_label
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
# 1. Resolver tasks won't have input artifacts in the API proto. First we
# specialcase two resolver types we support.
if isinstance(self._node, resolver.Resolver):
return self._build_resolver_spec()
# 2. Build the node spec.
# TODO(b/157641727): Tests comparing dictionaries are brittle when comparing
# lists as ordering matters.
dependency_ids = [node.id for node in self._node.upstream_nodes]
# Specify the inputs of the task.
for name, input_channel in self._inputs.items():
# If the redirecting map is provided (usually for latest blessed model
# resolver, we'll need to redirect accordingly. Also, the upstream node
# list will be updated and replaced by the new producer id.
producer_id = input_channel.producer_component_id
output_key = input_channel.output_key
for k, v in self._channel_redirect_map.items():
if k[0] == producer_id and producer_id in dependency_ids:
dependency_ids.remove(producer_id)
dependency_ids.append(v[0])
producer_id = self._channel_redirect_map.get(
(producer_id, output_key), (producer_id, output_key))[0]
output_key = self._channel_redirect_map.get(
(producer_id, output_key), (producer_id, output_key))[1]
input_artifact_spec = pipeline_pb2.TaskInputsSpec.InputArtifactSpec(
producer_task=producer_id, output_artifact_key=output_key)
task_spec.inputs.artifacts[name].CopyFrom(input_artifact_spec)
# Specify the outputs of the task.
for name, output_channel in self._outputs.items():
# Currently, we're working under the assumption that for tasks
# (those generated by BaseComponent), each channel contains a single
# artifact.
output_artifact_spec = compiler_utils.build_output_artifact_spec(
output_channel)
task_spec.outputs.artifacts[name].CopyFrom(output_artifact_spec)
# Specify the input parameters of the task.
for k, v in compiler_utils.build_input_parameter_spec(
self._exec_properties).items():
task_spec.inputs.parameters[k].CopyFrom(v)
# 3. Build the executor body for other common tasks.
if isinstance(self._node, importer.Importer):
executor.importer.CopyFrom(self._build_importer_spec())
elif isinstance(self._node, components.FileBasedExampleGen):
executor.container.CopyFrom(
self._build_file_based_example_gen_spec())
elif isinstance(self._node, (components.InfraValidator)):
raise NotImplementedError(
'The componet type "{}" is not supported'.format(
type(self._node)))
else:
executor.container.CopyFrom(self._build_container_spec())
dependency_ids = sorted(dependency_ids)
for dependency in dependency_ids:
task_spec.dependent_tasks.append(dependency)
task_spec.caching_options.CopyFrom(
pipeline_pb2.PipelineTaskSpec.CachingOptions(
enable_cache=self._enable_cache))
# 4. Attach the built executor spec to the deployment config.
self._deployment_config.executors[executor_label].CopyFrom(executor)
return [task_spec]