def value_converter( tfx_value: Any) -> Optional[pipeline_pb2.ValueOrRuntimeParameter]: """Converts TFX/MLMD values into Kubeflow pipeline ValueOrRuntimeParameter.""" if tfx_value is None: return None result = pipeline_pb2.ValueOrRuntimeParameter() if isinstance(tfx_value, (int, float, str, Text)): result.constant_value.CopyFrom(get_kubeflow_value(tfx_value)) elif isinstance(tfx_value, (Dict, List)): result.constant_value.CopyFrom( pipeline_pb2.Value(string_value=json.dumps(tfx_value))) elif isinstance(tfx_value, data_types.RuntimeParameter): # Attach the runtime parameter to the context. parameter_utils.attach_parameter(tfx_value) result.runtime_parameter = tfx_value.name elif isinstance(tfx_value, metadata_store_pb2.Value): if tfx_value.WhichOneof('value') == 'int_value': result.constant_value.CopyFrom( pipeline_pb2.Value(int_value=tfx_value.int_value)) elif tfx_value.WhichOneof('value') == 'double_value': result.constant_value.CopyFrom( pipeline_pb2.Value(double_value=tfx_value.double_value)) elif tfx_value.WhichOneof('value') == 'string_value': result.constant_value.CopyFrom( pipeline_pb2.Value(string_value=tfx_value.string_value)) elif isinstance(tfx_value, message.Message): result.constant_value.CopyFrom( pipeline_pb2.Value(string_value=json_format.MessageToJson( message=tfx_value, sort_keys=True))) else: # By default will attempt to encode the object using json_utils.dumps. result.constant_value.CopyFrom( pipeline_pb2.Value(string_value=json_utils.dumps(tfx_value))) return result
def _testAttachParametersInSingleThread(self, suffix: Text): with parameter_utils.ParameterContext() as pc: parameter_utils.attach_parameter( data_types.RuntimeParameter(name='param1_in_{}'.format(suffix), ptype=int)) parameter_utils.attach_parameter( data_types.RuntimeParameter(name='param2_in_{}'.format(suffix), ptype=int)) self.assertLen(pc.parameters, 2) self.assertEqual(pc.parameters[0].name, 'param1_in_{}'.format(suffix)) self.assertEqual(pc.parameters[1].name, 'param2_in_{}'.format(suffix))
def testAttachParameters(self): with parameter_utils.ParameterContext() as pc: param1 = data_types.RuntimeParameter(name='test_param_1', ptype=int) parameter_utils.attach_parameter(param1) param2 = data_types.RuntimeParameter(name='test_param_2', ptype=Text) parameter_utils.attach_parameter(param2) param3 = data_types.RuntimeParameter(name='test_param_3', ptype=float) parameter_utils.attach_parameter(param3) self.assertListEqual([param1, param2, param3], pc.parameters)
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 testFailWhenNotRunningUnderContext(self): param = data_types.RuntimeParameter(name='test_param', ptype=int) with self.assertRaisesRegexp( RuntimeError, r'attach_parameter\(\) must run under ParameterContext\.'): parameter_utils.attach_parameter(param)
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}