def _build_resolver_for_latest_model_blessing(
self,
model_blessing_channel_key: str) -> pipeline_pb2.PipelineTaskSpec:
"""Builds the resolver spec for latest valid ModelBlessing artifact."""
# 1. Build the task info.
result = pipeline_pb2.PipelineTaskSpec()
name = '{}{}'.format(self._name, _MODEL_BLESSING_RESOLVER_SUFFIX)
result.task_info.CopyFrom(pipeline_pb2.PipelineTaskInfo(name=name))
executor_label = _EXECUTOR_LABEL_PATTERN.format(name)
result.executor_label = executor_label
# 2. Specify the outputs of the task.
result.outputs.artifacts[model_blessing_channel_key].CopyFrom(
compiler_utils.build_output_artifact_spec(
self._outputs[model_blessing_channel_key]))
# 3. Build the resolver executor spec for latest valid ModelBlessing.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
artifact_queries = {}
query_filter = ("artifact_type='{type}' and state={state}"
" and custom_properties['{key}']='{value}'").format(
type=compiler_utils.get_artifact_title(
standard_artifacts.ModelBlessing),
state=metadata_store_pb2.Artifact.State.Name(
metadata_store_pb2.Artifact.LIVE),
key=constants.ARTIFACT_PROPERTY_BLESSED_KEY,
value=constants.BLESSED_VALUE)
artifact_queries[
model_blessing_channel_key] = ResolverSpec.ArtifactQuerySpec(
filter=query_filter)
executor.resolver.CopyFrom(
ResolverSpec(output_artifact_queries=artifact_queries))
self._deployment_config.executors[executor_label].CopyFrom(executor)
return result
def testBuildOutputArtifactSpec(self):
examples = standard_artifacts.Examples()
examples.span = 1
examples.set_int_custom_property(key='int_param', value=42)
examples.set_string_custom_property(key='str_param', value='42')
example_channel = channel.Channel(
type=standard_artifacts.Examples).set_artifacts([examples])
spec = compiler_utils.build_output_artifact_spec(example_channel)
expected_spec = text_format.Parse(
"""
artifact_type {
instance_schema: "title: tfx.Examples\\ntype: object\\nproperties:\\n span:\\n type: integer\\n description: Span for an artifact.\\n version:\\n type: integer\\n description: Version for an artifact.\\n split_names:\\n type: string\\n description: JSON-encoded list of splits for an artifact. Empty string means artifact has no split.\\n"
}
metadata {
fields {
key: "int_param"
value {
number_value: 42.0
}
}
fields {
key: "span"
value {
number_value: 1.0
}
}
fields {
key: "str_param"
value {
string_value: "42"
}
}
}
""", pipeline_pb2.ComponentOutputsSpec.ArtifactSpec())
self.assertProtoEquals(spec, expected_spec)
# Empty output channel with only type info.
model_channel = channel.Channel(type=standard_artifacts.Model)
spec = compiler_utils.build_output_artifact_spec(model_channel)
expected_spec = text_format.Parse(
"""
artifact_type {
instance_schema: "title: tfx.Model\\ntype: object\\n"
}
""", pipeline_pb2.ComponentOutputsSpec.ArtifactSpec())
self.assertProtoEquals(spec, expected_spec)
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 testCustomArtifactMappingFails(self):
my_bad_artifact = _MyBadArtifact()
my_bad_artifact_schema = compiler_utils.get_artifact_schema(
my_bad_artifact)
self.assertDictEqual(yaml.safe_load(my_bad_artifact_schema),
yaml.safe_load(_EXPECTED_MY_BAD_ARTIFACT_SCHEMA))
my_bad_artifact.int1 = 42
with self.assertRaisesRegexp(KeyError, 'Actual property:'):
_ = compiler_utils.build_output_artifact_spec(
channel_utils.as_channel([my_bad_artifact]))
def _build_resolver_for_latest_blessed_model(
self, model_channel_key: str, model_blessing_resolver_name: str,
model_blessing_channel_key: str) -> pipeline_pb2.PipelineTaskSpec:
"""Builds the resolver spec for latest blessed Model artifact."""
name = '{}{}'.format(self._name, _MODEL_RESOLVER_SUFFIX)
# Component def.
component_def = pipeline_pb2.ComponentSpec()
executor_label = _EXECUTOR_LABEL_PATTERN.format(name)
component_def.executor_label = executor_label
input_artifact_spec = compiler_utils.build_input_artifact_spec(
self._outputs[model_blessing_channel_key])
component_def.input_definitions.artifacts[
_MODEL_RESOLVER_INPUT_KEY].CopyFrom(input_artifact_spec)
output_artifact_spec = compiler_utils.build_output_artifact_spec(
self._outputs[model_channel_key])
component_def.output_definitions.artifacts[model_channel_key].CopyFrom(
output_artifact_spec)
self._component_defs[name] = component_def
# Task spec.
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.name = name
task_spec.component_ref.name = name
input_artifact_spec = pipeline_pb2.TaskInputsSpec.InputArtifactSpec()
input_artifact_spec.task_output_artifact.producer_task = model_blessing_resolver_name
input_artifact_spec.task_output_artifact.output_artifact_key = model_blessing_channel_key
task_spec.inputs.artifacts[_MODEL_RESOLVER_INPUT_KEY].CopyFrom(
input_artifact_spec)
# Resolver executor spec.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
artifact_queries = {}
query_filter = (
'schema_title="{type}" AND '
'state={state} AND '
'name="{{{{$.inputs.artifacts[\'{input_key}\']'
'.metadata[\'{property_key}\']}}}}"').format(
type=compiler_utils.get_artifact_title(standard_artifacts.Model),
state=metadata_store_pb2.Artifact.State.Name(
metadata_store_pb2.Artifact.LIVE),
input_key=_MODEL_RESOLVER_INPUT_KEY,
property_key=constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY)
artifact_queries[model_channel_key] = ResolverSpec.ArtifactQuerySpec(
filter=query_filter)
executor.resolver.CopyFrom(
ResolverSpec(output_artifact_queries=artifact_queries))
self._deployment_config.executors[executor_label].CopyFrom(executor)
return task_spec
def _build_resolver_for_latest_blessed_model(
self, model_channel_key: str, model_blessing_resolver_name: str,
model_blessing_channel_key: str) -> pipeline_pb2.PipelineTaskSpec:
"""Builds the resolver spec for latest blessed Model artifact."""
# 1. Build the task info.
result = pipeline_pb2.PipelineTaskSpec()
name = '{}{}'.format(self._name, _MODEL_RESOLVER_SUFFIX)
result.task_info.CopyFrom(pipeline_pb2.PipelineTaskInfo(name=name))
executor_label = _EXECUTOR_LABEL_PATTERN.format(name)
result.executor_label = executor_label
# 2. Specify the input of the task. The output from model_blessing_resolver
# will be used as the input.
input_artifact_spec = pipeline_pb2.TaskInputsSpec.InputArtifactSpec(
producer_task=model_blessing_resolver_name,
output_artifact_key=model_blessing_channel_key)
result.inputs.artifacts[_MODEL_RESOLVER_INPUT_KEY].CopyFrom(
input_artifact_spec)
# 3. Specify the outputs of the task. model_resolver has one output for
# the latest blessed model.
result.outputs.artifacts[model_channel_key].CopyFrom(
compiler_utils.build_output_artifact_spec(
self._outputs[model_channel_key]))
# 4. Build the resolver executor spec for latest blessed Model.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
artifact_queries = {}
query_filter = (
"artifact_type='{type}' and "
"state={state} and name={{$.inputs.artifacts['{input_key}']"
".custom_properties['{property_key}']}}").format(
type=compiler_utils.get_artifact_title(
standard_artifacts.Model),
state=metadata_store_pb2.Artifact.State.Name(
metadata_store_pb2.Artifact.LIVE),
input_key=_MODEL_RESOLVER_INPUT_KEY,
property_key=constants.ARTIFACT_PROPERTY_CURRENT_MODEL_ID_KEY)
artifact_queries[model_channel_key] = ResolverSpec.ArtifactQuerySpec(
filter=query_filter)
executor.resolver.CopyFrom(
ResolverSpec(output_artifact_queries=artifact_queries))
self._deployment_config.executors[executor_label].CopyFrom(executor)
return result
def _build_resolver_for_latest_model_blessing(
self,
model_blessing_channel_key: str) -> pipeline_pb2.PipelineTaskSpec:
"""Builds the resolver spec for latest valid ModelBlessing artifact."""
name = '{}{}'.format(self._name, _MODEL_BLESSING_RESOLVER_SUFFIX)
# Component def.
component_def = pipeline_pb2.ComponentSpec()
executor_label = _EXECUTOR_LABEL_PATTERN.format(name)
component_def.executor_label = executor_label
output_artifact_spec = compiler_utils.build_output_artifact_spec(
self._outputs[model_blessing_channel_key])
component_def.output_definitions.artifacts[
model_blessing_channel_key].CopyFrom(output_artifact_spec)
self._component_defs[name] = component_def
# Task spec.
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.name = name
task_spec.component_ref.name = name
# Builds the resolver executor spec for latest valid ModelBlessing.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
artifact_queries = {}
query_filter = ('artifact_type="{type}" and state={state}'
' and metadata.{key}.number_value={value}').format(
type=compiler_utils.get_artifact_title(
standard_artifacts.ModelBlessing),
state=metadata_store_pb2.Artifact.State.Name(
metadata_store_pb2.Artifact.LIVE),
key=constants.ARTIFACT_PROPERTY_BLESSED_KEY,
value=constants.BLESSED_VALUE)
artifact_queries[
model_blessing_channel_key] = ResolverSpec.ArtifactQuerySpec(
filter=query_filter)
executor.resolver.CopyFrom(
ResolverSpec(output_artifact_queries=artifact_queries))
self._deployment_config.executors[executor_label].CopyFrom(executor)
return task_spec
def _build_latest_artifact_resolver(
self) -> Dict[str, 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.
"""
# 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')))
component_def = pipeline_pb2.ComponentSpec()
executor_label = _EXECUTOR_LABEL_PATTERN.format(self._name)
component_def.executor_label = executor_label
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.name = self._name
for name, output_channel in self._outputs.items():
output_artifact_spec = compiler_utils.build_output_artifact_spec(
output_channel)
component_def.output_definitions.artifacts[name].CopyFrom(
output_artifact_spec)
for name, value in self._exec_properties.items():
if value is None:
continue
parameter_type_spec = compiler_utils.build_parameter_type_spec(value)
component_def.input_definitions.parameters[name].CopyFrom(
parameter_type_spec)
if isinstance(value, data_types.RuntimeParameter):
parameter_utils.attach_parameter(value)
task_spec.inputs.parameters[name].component_input_parameter = value.name
else:
task_spec.inputs.parameters[name].CopyFrom(
pipeline_pb2.TaskInputsSpec.InputParameterSpec(
runtime_value=compiler_utils.value_converter(value)))
self._component_defs[self._name] = component_def
task_spec.component_ref.name = self._name
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))
# Resolver's output dict has the same set of keys as its input dict.
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 {self._name: task_spec}
def build(self) -> Dict[str, pipeline_pb2.PipelineTaskSpec]:
"""Builds a pipeline PipelineTaskSpec given the node information.
Each TFX node maps one task spec and usually one component definition and
one executor spec. (with resolver node as an exception. See explaination
in the Returns section).
- Component definition includes interfaces of a node. For example, name
and type information of inputs/outputs/execution_properties.
- Task spec contains the topologies around the node. For example, the
dependency nodes, where to read the inputs and exec_properties (from another
task, from parent component or from a constant value). The task spec has the
name of the component definition it references. It is possible that a task
spec references an existing component definition that's built previously.
- Executor spec encodes how the node is actually executed. For example,
args to start a container, or query strings for resolvers. All executor spec
will be packed into deployment config proto.
During the build, all three parts mentioned above will be updated.
Returns:
A Dict mapping from node id to PipelineTaskSpec messages corresponding to
the node. For most of the cases, the dict 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.
"""
# 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 component spec.
component_def = pipeline_pb2.ComponentSpec()
executor_label = _EXECUTOR_LABEL_PATTERN.format(self._name)
component_def.executor_label = executor_label
# Inputs
for name, input_channel in self._inputs.items():
input_artifact_spec = compiler_utils.build_input_artifact_spec(
input_channel)
component_def.input_definitions.artifacts[name].CopyFrom(
input_artifact_spec)
# Outputs
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)
component_def.output_definitions.artifacts[name].CopyFrom(
output_artifact_spec)
# Exec properties
for name, value in self._exec_properties.items():
# value can be None for unprovided optional exec properties.
if value is None:
continue
parameter_type_spec = compiler_utils.build_parameter_type_spec(value)
component_def.input_definitions.parameters[name].CopyFrom(
parameter_type_spec)
if self._name not in self._component_defs:
self._component_defs[self._name] = component_def
else:
raise ValueError(f'Found duplicate component ids {self._name} while '
'building component definitions.')
# 3. Build task spec.
task_spec = pipeline_pb2.PipelineTaskSpec()
task_spec.task_info.name = self._name
dependency_ids = [node.id for node in self._node.upstream_nodes]
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()
input_artifact_spec.task_output_artifact.producer_task = producer_id
input_artifact_spec.task_output_artifact.output_artifact_key = output_key
task_spec.inputs.artifacts[name].CopyFrom(input_artifact_spec)
for name, value in self._exec_properties.items():
if value is None:
continue
if isinstance(value, data_types.RuntimeParameter):
parameter_utils.attach_parameter(value)
task_spec.inputs.parameters[name].component_input_parameter = value.name
else:
task_spec.inputs.parameters[name].CopyFrom(
pipeline_pb2.TaskInputsSpec.InputParameterSpec(
runtime_value=compiler_utils.value_converter(value)))
task_spec.component_ref.name = self._name
dependency_ids = sorted(dependency_ids)
for dependency in dependency_ids:
task_spec.dependent_tasks.append(dependency)
if self._enable_cache:
task_spec.caching_options.CopyFrom(
pipeline_pb2.PipelineTaskSpec.CachingOptions(
enable_cache=self._enable_cache))
# 4. Build the executor body for other common tasks.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
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())
self._deployment_config.executors[executor_label].CopyFrom(executor)
return {self._name: 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]
def build(self) -> Dict[str, pipeline_pb2.PipelineTaskSpec]:
"""Builds a pipeline PipelineTaskSpec given the node information.
Each TFX node maps one task spec and usually one component definition and
one executor spec. (with resolver node as an exception. See explaination
in the Returns section).
- Component definition includes interfaces of a node. For example, name
and type information of inputs/outputs/execution_properties.
- Task spec contains the topologies around the node. For example, the
dependency nodes, where to read the inputs and exec_properties (from another
task, from parent component or from a constant value). The task spec has the
name of the component definition it references. It is possible that a task
spec references an existing component definition that's built previously.
- Executor spec encodes how the node is actually executed. For example,
args to start a container, or query strings for resolvers. All executor spec
will be packed into deployment config proto.
During the build, all three parts mentioned above will be updated.
Returns:
A Dict mapping from node id to PipelineTaskSpec messages corresponding to
the node. For most of the cases, the dict 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.
"""
# 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 component spec.
component_def = pipeline_pb2.ComponentSpec()
task_spec = pipeline_pb2.PipelineTaskSpec()
executor_label = _EXECUTOR_LABEL_PATTERN.format(self._name)
component_def.executor_label = executor_label
# Conditionals
implicit_input_channels = {}
implicit_upstream_node_ids = set()
predicates = conditional.get_predicates(self._node)
if predicates:
implicit_keys_map = {
tfx_compiler_utils.implicit_channel_key(channel): key
for key, channel in self._inputs.items()
}
cel_predicates = []
for predicate in predicates:
for channel in predicate.dependent_channels():
implicit_key = tfx_compiler_utils.implicit_channel_key(channel)
if implicit_key not in implicit_keys_map:
# Store this channel and add it to the node inputs later.
implicit_input_channels[implicit_key] = channel
# Store the producer node and add it to the upstream nodes later.
implicit_upstream_node_ids.add(channel.producer_component_id)
placeholder_pb = predicate.encode_with_keys(
tfx_compiler_utils.build_channel_to_key_fn(implicit_keys_map))
cel_predicates.append(compiler_utils.placeholder_to_cel(placeholder_pb))
task_spec.trigger_policy.condition = ' && '.join(cel_predicates)
# Inputs
for name, input_channel in itertools.chain(self._inputs.items(),
implicit_input_channels.items()):
input_artifact_spec = compiler_utils.build_input_artifact_spec(
input_channel)
component_def.input_definitions.artifacts[name].CopyFrom(
input_artifact_spec)
# Outputs
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)
component_def.output_definitions.artifacts[name].CopyFrom(
output_artifact_spec)
# Exec properties
for name, value in self._exec_properties.items():
# value can be None for unprovided optional exec properties.
if value is None:
continue
parameter_type_spec = compiler_utils.build_parameter_type_spec(value)
component_def.input_definitions.parameters[name].CopyFrom(
parameter_type_spec)
if self._name not in self._component_defs:
self._component_defs[self._name] = component_def
else:
raise ValueError(f'Found duplicate component ids {self._name} while '
'building component definitions.')
# 3. Build task spec.
task_spec.task_info.name = self._name
dependency_ids = sorted({node.id for node in self._node.upstream_nodes}
| implicit_upstream_node_ids)
for name, input_channel in itertools.chain(self._inputs.items(),
implicit_input_channels.items()):
# TODO(b/169573945): Add support for vertex if requested.
if not isinstance(input_channel, Channel):
raise TypeError('Only single Channel is supported.')
if self._is_exit_handler:
logging.error('exit handler component doesn\'t take input artifact, '
'the input will be ignored.')
continue
# 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()
input_artifact_spec.task_output_artifact.producer_task = producer_id
input_artifact_spec.task_output_artifact.output_artifact_key = output_key
task_spec.inputs.artifacts[name].CopyFrom(input_artifact_spec)
for name, value in self._exec_properties.items():
if value is None:
continue
if isinstance(value, data_types.RuntimeParameter):
parameter_utils.attach_parameter(value)
task_spec.inputs.parameters[name].component_input_parameter = value.name
elif isinstance(value, decorators.FinalStatusStr):
if not self._is_exit_handler:
logging.error('FinalStatusStr type is only allowed to use in exit'
' handler. The parameter is ignored.')
else:
task_spec.inputs.parameters[name].task_final_status.producer_task = (
compiler_utils.TFX_DAG_NAME)
else:
task_spec.inputs.parameters[name].CopyFrom(
pipeline_pb2.TaskInputsSpec.InputParameterSpec(
runtime_value=compiler_utils.value_converter(value)))
task_spec.component_ref.name = self._name
dependency_ids = sorted(dependency_ids)
for dependency in dependency_ids:
task_spec.dependent_tasks.append(dependency)
if self._enable_cache:
task_spec.caching_options.CopyFrom(
pipeline_pb2.PipelineTaskSpec.CachingOptions(
enable_cache=self._enable_cache))
if self._is_exit_handler:
task_spec.trigger_policy.strategy = (
pipeline_pb2.PipelineTaskSpec.TriggerPolicy
.ALL_UPSTREAM_TASKS_COMPLETED)
task_spec.dependent_tasks.append(compiler_utils.TFX_DAG_NAME)
# 4. Build the executor body for other common tasks.
executor = pipeline_pb2.PipelineDeploymentConfig.ExecutorSpec()
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())
self._deployment_config.executors[executor_label].CopyFrom(executor)
return {self._name: task_spec}
