def _get_executor_spec(pipeline: pipeline_pb2.Pipeline,
node_id: str) -> Optional[any_pb2.Any]:
"""Returns executor spec for given node_id if it exists in pipeline IR, None otherwise."""
if not pipeline.deployment_config.Is(
pipeline_pb2.IntermediateDeploymentConfig.DESCRIPTOR):
return None
depl_config = pipeline_pb2.IntermediateDeploymentConfig()
pipeline.deployment_config.Unpack(depl_config)
return depl_config.executor_specs.get(node_id)
def main(argv):
del argv
with mlmd_connection_func(FLAGS.path) as m:
depl_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='fake.ClassPath')
depl_config.executor_specs['arg1'].Pack(executor_spec)
depl_config.executor_specs['arg2'].Pack(executor_spec)
create_sample_pipeline(m, FLAGS.pipeline_id, FLAGS.pipeline_run_num,
FLAGS.export_ir_dir, FLAGS.ir_file, depl_config,
execute_nodes_func)
def test_queue_multiplexing(self, mock_publish):
# Create a pipeline IR containing deployment config for testing.
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='trainer.TrainerExecutor')
deployment_config.executor_specs['Trainer'].Pack(executor_spec)
deployment_config.executor_specs['Transform'].Pack(executor_spec)
deployment_config.executor_specs['Evaluator'].Pack(executor_spec)
pipeline = pipeline_pb2.Pipeline()
pipeline.deployment_config.Pack(deployment_config)
collector = _Collector()
# Register a bunch of fake task schedulers.
# Register fake task scheduler.
ts.TaskSchedulerRegistry.register(
deployment_config.executor_specs['Trainer'].type_url,
functools.partial(_FakeTaskScheduler,
block_nodes={'Trainer', 'Transform'},
collector=collector))
task_queue = tq.TaskQueue()
# Enqueue some tasks.
trainer_exec_task = _test_exec_task('Trainer', 'test-pipeline')
task_queue.enqueue(trainer_exec_task)
task_queue.enqueue(_test_cancel_task('Trainer', 'test-pipeline'))
with tm.TaskManager(mock.Mock(),
pipeline,
task_queue,
max_active_task_schedulers=1000,
max_dequeue_wait_secs=0.1,
process_all_queued_tasks_before_exit=True):
# Enqueue more tasks after task manager starts.
transform_exec_task = _test_exec_task('Transform', 'test-pipeline')
task_queue.enqueue(transform_exec_task)
evaluator_exec_task = _test_exec_task('Evaluator', 'test-pipeline')
task_queue.enqueue(evaluator_exec_task)
task_queue.enqueue(_test_cancel_task('Transform', 'test-pipeline'))
# Ensure that all exec and cancellation tasks were processed correctly.
self.assertCountEqual(
[trainer_exec_task, transform_exec_task, evaluator_exec_task],
collector.scheduled_tasks)
self.assertCountEqual([trainer_exec_task, transform_exec_task],
collector.cancelled_tasks)
mock_publish.assert_has_calls([
mock.call(mock.ANY, pipeline, trainer_exec_task, mock.ANY),
mock.call(mock.ANY, pipeline, transform_exec_task, mock.ANY),
mock.call(mock.ANY, pipeline, evaluator_exec_task, mock.ANY)
],
any_order=True)
def replace_executor_with_stub(pipeline: pipeline_pb2.Pipeline,
test_data_dir: str,
test_component_ids: List[str]):
"""Replace executors in pipeline IR with the stub executor.
This funciton will replace the IR inplace.
For example,
pipeline_mock.replace_executor_with_stub(
pipeline_ir,
test_data_dir,
test_component_ids = ['Trainer', 'Transform'])
Then you can pass the modified `pipeline_ir` into a dag runner to execute
the stubbed pipeline.
Args:
pipeline: The pipeline to alter.
test_data_dir: The directory where pipeline outputs are recorded
(pipeline_recorder.py).
test_component_ids: List of ids of components that are to be tested. In
other words, executors of components other than those specified by this
list will be replaced with a BaseStubExecutor.
Returns:
None
"""
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
if not pipeline.deployment_config.Unpack(deployment_config):
raise NotImplementedError(
'Unexpected pipeline.deployment_config type "{}". Currently only '
'IntermediateDeploymentConfig is supported.'.format(
pipeline.deployment_config.type_url))
for component_id in deployment_config.executor_specs:
if component_id not in test_component_ids:
executable_spec = deployment_config.executor_specs[component_id]
if not executable_spec.Is(
executable_spec_pb2.PythonClassExecutableSpec.DESCRIPTOR):
raise NotImplementedError(
'Unexpected executable_spec type "{}". Currently only '
'PythonClassExecutableSpec is supported.'.format(
executable_spec.type_url))
stub_executor_class_spec = executor_spec.ExecutorClassSpec(
base_stub_executor.BaseStubExecutor)
stub_executor_class_spec.add_extra_flags(
(base_stub_executor.TEST_DATA_DIR_FLAG + '=' + test_data_dir,
base_stub_executor.COMPONENT_ID_FLAG + '=' + component_id))
stub_executor_spec = stub_executor_class_spec.encode()
executable_spec.Pack(stub_executor_spec)
pipeline.deployment_config.Pack(deployment_config)
def compile(self, tfx_pipeline: pipeline.Pipeline) -> pipeline_pb2.Pipeline:
"""Compiles a tfx pipeline into uDSL proto.
Args:
tfx_pipeline: A TFX pipeline.
Returns:
A Pipeline proto that encodes all necessary information of the pipeline.
"""
_validate_pipeline(tfx_pipeline)
context = _CompilerContext(tfx_pipeline)
pipeline_pb = pipeline_pb2.Pipeline()
pipeline_pb.pipeline_info.id = context.pipeline_info.pipeline_name
pipeline_pb.execution_mode = context.execution_mode
if isinstance(context.pipeline_info.pipeline_root, placeholder.Placeholder):
pipeline_pb.runtime_spec.pipeline_root.placeholder.CopyFrom(
context.pipeline_info.pipeline_root.encode())
else:
compiler_utils.set_runtime_parameter_pb(
pipeline_pb.runtime_spec.pipeline_root.runtime_parameter,
constants.PIPELINE_ROOT_PARAMETER_NAME, str,
context.pipeline_info.pipeline_root)
if pipeline_pb.execution_mode == pipeline_pb2.Pipeline.ExecutionMode.SYNC:
compiler_utils.set_runtime_parameter_pb(
pipeline_pb.runtime_spec.pipeline_run_id.runtime_parameter,
constants.PIPELINE_RUN_ID_PARAMETER_NAME, str)
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
if tfx_pipeline.metadata_connection_config:
deployment_config.metadata_connection_config.Pack(
tfx_pipeline.metadata_connection_config)
for node in tfx_pipeline.components:
# In ASYNC mode Resolver nodes are merged into the downstream node as a
# ResolverConfig
if compiler_utils.is_resolver(node) and context.is_async_mode:
continue
node_pb = self._compile_node(node, context, deployment_config,
tfx_pipeline.enable_cache)
pipeline_or_node = pipeline_pb.PipelineOrNode()
pipeline_or_node.pipeline_node.CopyFrom(node_pb)
# TODO(b/158713812): Support sub-pipeline.
pipeline_pb.nodes.append(pipeline_or_node)
context.node_pbs[node.id] = node_pb
if tfx_pipeline.platform_config:
deployment_config.pipeline_level_platform_config.Pack(
tfx_pipeline.platform_config)
pipeline_pb.deployment_config.Pack(deployment_config)
return pipeline_pb
def setUp(self):
super().setUp()
pipeline = pipeline_pb2.Pipeline()
pipeline.pipeline_info.id = 'pipeline'
pipeline.nodes.add().pipeline_node.node_info.id = 'Trainer'
pipeline.nodes.add().pipeline_node.node_info.id = 'Transform'
importer_node = pipeline.nodes.add().pipeline_node
importer_node.node_info.id = 'Importer'
importer_node.node_info.type.name = constants.IMPORTER_NODE_TYPE
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='trainer.TrainerExecutor')
deployment_config.executor_specs['Trainer'].Pack(executor_spec)
pipeline.deployment_config.Pack(deployment_config)
self._spec_type_url = deployment_config.executor_specs['Trainer'].type_url
self._pipeline = pipeline
ts.TaskSchedulerRegistry.clear()
def compile(self,
tfx_pipeline: pipeline.Pipeline) -> pipeline_pb2.Pipeline:
"""Compiles a tfx pipeline into uDSL proto.
Args:
tfx_pipeline: A TFX pipeline.
Returns:
A Pipeline proto that encodes all necessary information of the pipeline.
"""
context = _CompilerContext(
tfx_pipeline.pipeline_info,
compiler_utils.resolve_execution_mode(tfx_pipeline))
pipeline_pb = pipeline_pb2.Pipeline()
pipeline_pb.pipeline_info.id = context.pipeline_info.pipeline_name
pipeline_pb.execution_mode = context.execution_mode
compiler_utils.set_runtime_parameter_pb(
pipeline_pb.runtime_spec.pipeline_root.runtime_parameter,
constants.PIPELINE_ROOT_PARAMETER_NAME, str,
context.pipeline_info.pipeline_root)
if pipeline_pb.execution_mode == pipeline_pb2.Pipeline.ExecutionMode.SYNC:
compiler_utils.set_runtime_parameter_pb(
pipeline_pb.runtime_spec.pipeline_run_id.runtime_parameter,
constants.PIPELINE_RUN_ID_PARAMETER_NAME, str)
assert compiler_utils.ensure_topological_order(
tfx_pipeline.components), (
"Pipeline components are not topologically sorted.")
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
if tfx_pipeline.metadata_connection_config:
deployment_config.metadata_connection_config.Pack(
tfx_pipeline.metadata_connection_config)
for node in tfx_pipeline.components:
node_pb = self._compile_node(node, context, deployment_config,
tfx_pipeline.enable_cache)
pipeline_or_node = pipeline_pb.PipelineOrNode()
pipeline_or_node.pipeline_node.CopyFrom(node_pb)
# TODO(b/158713812): Support sub-pipeline.
pipeline_pb.nodes.append(pipeline_or_node)
context.node_pbs[node.id] = node_pb
if tfx_pipeline.platform_config:
deployment_config.pipeline_level_platform_config.Pack(
tfx_pipeline.platform_config)
pipeline_pb.deployment_config.Pack(deployment_config)
return pipeline_pb
def setUp(self):
super(TaskManagerTest, self).setUp()
# Create a pipeline IR containing deployment config for testing.
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='trainer.TrainerExecutor')
deployment_config.executor_specs['Trainer'].Pack(executor_spec)
deployment_config.executor_specs['Transform'].Pack(executor_spec)
deployment_config.executor_specs['Evaluator'].Pack(executor_spec)
pipeline = pipeline_pb2.Pipeline()
pipeline.deployment_config.Pack(deployment_config)
ts.TaskSchedulerRegistry.clear()
self._deployment_config = deployment_config
self._pipeline = pipeline
self._type_url = deployment_config.executor_specs['Trainer'].type_url
def _dehydrate_tfx_ir(self, original_pipeline: pipeline_pb2.Pipeline,
node_id: str) -> pipeline_pb2.Pipeline:
pipeline = copy.deepcopy(original_pipeline)
for node in pipeline.nodes:
if (node.WhichOneof('node') == 'pipeline_node'
and node.pipeline_node.node_info.id == node_id):
del pipeline.nodes[:]
pipeline.nodes.extend([node])
break
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
pipeline.deployment_config.Unpack(deployment_config)
self._del_unused_field(node_id, deployment_config.executor_specs)
self._del_unused_field(node_id, deployment_config.custom_driver_specs)
self._del_unused_field(node_id,
deployment_config.node_level_platform_configs)
pipeline.deployment_config.Pack(deployment_config)
return pipeline
def extract_local_deployment_config(
pipeline: pipeline_pb2.Pipeline
) -> local_deployment_config_pb2.LocalDeploymentConfig:
"""Extracts the proto.Any pipeline.deployment_config to LocalDeploymentConfig."""
if not pipeline.deployment_config:
raise ValueError('deployment_config is not available in the pipeline.')
result = local_deployment_config_pb2.LocalDeploymentConfig()
if pipeline.deployment_config.Unpack(result):
return result
result = pipeline_pb2.IntermediateDeploymentConfig()
if pipeline.deployment_config.Unpack(result):
return _to_local_deployment(result)
raise ValueError('deployment_config {} of type {} is not supported'.format(
pipeline.deployment_config, type(pipeline.deployment_config)))
def test_registration_and_creation(self):
# Create a pipeline IR containing deployment config for testing.
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='trainer.TrainerExecutor')
deployment_config.executor_specs['Trainer'].Pack(executor_spec)
pipeline = pipeline_pb2.Pipeline()
pipeline.deployment_config.Pack(deployment_config)
# Register a fake task scheduler.
spec_type_url = deployment_config.executor_specs['Trainer'].type_url
ts.TaskSchedulerRegistry.register(spec_type_url, _FakeTaskScheduler)
# Create a task and verify that the correct scheduler is instantiated.
task = test_utils.create_exec_node_task(node_uid=task_lib.NodeUid(
pipeline_id='pipeline', pipeline_run_id=None, node_id='Trainer'))
task_scheduler = ts.TaskSchedulerRegistry.create_task_scheduler(
mock.Mock(), pipeline, task)
self.assertIsInstance(task_scheduler, _FakeTaskScheduler)
def _fix_deployment_config(
input_pipeline: p_pb2.Pipeline,
node_ids_to_keep: Collection[str]) -> Union[any_pb2.Any, None]:
"""Filter per-node deployment configs.
Cast deployment configs from Any proto to IntermediateDeploymentConfig.
Take all three per-node fields and filter out the nodes using
node_ids_to_keep. This works because those fields don't contain references to
other nodes.
Args:
input_pipeline: The input Pipeline IR proto.
node_ids_to_keep: Set of node_ids to keep.
Returns:
If the deployment_config field is set in the input_pipeline, this would
output the deployment config with filtered per-node configs, then cast into
an Any proto. If the deployment_config field is unset in the input_pipeline,
then this function would return None.
"""
if not input_pipeline.HasField('deployment_config'):
return None
deployment_config = p_pb2.IntermediateDeploymentConfig()
input_pipeline.deployment_config.Unpack(deployment_config)
def _fix_per_node_config(config_map: MutableMapping[str, Any]):
# We have to make two passes because we cannot modify the dictionary while
# iterating over it.
node_ids_to_delete = [
node_id for node_id in config_map if node_id not in node_ids_to_keep
]
for node_id_to_delete in node_ids_to_delete:
del config_map[node_id_to_delete]
_fix_per_node_config(deployment_config.executor_specs)
_fix_per_node_config(deployment_config.custom_driver_specs)
_fix_per_node_config(deployment_config.node_level_platform_configs)
result = any_pb2.Any()
result.Pack(deployment_config)
return result
def _scheduler_class_for_executor_spec(
cls: Type[T], pipeline: pipeline_pb2.Pipeline,
task: task_lib.ExecNodeTask) -> Type[TaskScheduler]:
"""Returns scheduler class for executor spec url if feasible, raises error otherwise."""
if not pipeline.deployment_config.Is(
pipeline_pb2.IntermediateDeploymentConfig.DESCRIPTOR):
raise ValueError('No deployment config found in pipeline IR')
depl_config = pipeline_pb2.IntermediateDeploymentConfig()
pipeline.deployment_config.Unpack(depl_config)
node_id = task.node_uid.node_id
if node_id not in depl_config.executor_specs:
raise ValueError(f'Executor spec not found for node id: {node_id}')
executor_spec_type_url = depl_config.executor_specs[node_id].type_url
scheduler_class = cls._task_scheduler_registry.get(
executor_spec_type_url)
if scheduler_class is None:
raise ValueError(
f'No task scheduler registered for executor spec type url: '
f'{executor_spec_type_url}')
return scheduler_class
def create_task_scheduler(cls: Type[T], mlmd_handle: metadata.Metadata,
pipeline: pipeline_pb2.Pipeline,
task: task_lib.Task) -> TaskScheduler:
"""Creates a task scheduler for the given task.
Note that this assumes deployment_config packed in the pipeline IR is of
type `IntermediateDeploymentConfig`. This detail may change in the future.
Args:
mlmd_handle: A handle to the MLMD db.
pipeline: The pipeline IR.
task: The task that needs to be scheduled.
Returns:
An instance of `TaskScheduler` for the given task.
Raises:
NotImplementedError: Raised if not an `ExecNodeTask`.
ValueError: Deployment config not present in the IR proto or if executor
spec for the node corresponding to `task` not configured in the IR.
"""
if not task_lib.is_exec_node_task(task):
raise NotImplementedError(
'Can create a task scheduler only for an `ExecNodeTask`.')
task = typing.cast(task_lib.ExecNodeTask, task)
# TODO(b/170383494): Decide which DeploymentConfig to use.
if not pipeline.deployment_config.Is(
pipeline_pb2.IntermediateDeploymentConfig.DESCRIPTOR):
raise ValueError('No deployment config found in pipeline IR.')
depl_config = pipeline_pb2.IntermediateDeploymentConfig()
pipeline.deployment_config.Unpack(depl_config)
node_id = task.node_uid.node_id
if node_id not in depl_config.executor_specs:
raise ValueError(
'Executor spec for node id `{}` not found in pipeline IR.'.
format(node_id))
executor_spec_type_url = depl_config.executor_specs[node_id].type_url
return cls._task_scheduler_registry[executor_spec_type_url](
mlmd_handle=mlmd_handle, pipeline=pipeline, task=task)
}
}
custom_driver_specs {
key: "my_example_gen"
value {
[type.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec] {
class_path: "tfx.components.example_gen_driver"
}
}
}
metadata_connection_config {
[type.googleapis.com/ml_metadata.ConnectionConfig] {
fake_database {}
}
}
""", pipeline_pb2.IntermediateDeploymentConfig())
_executed_components = []
_component_executors = {}
_component_drivers = {}
_conponent_to_pipeline_run = {}
# TODO(b/162980675): When PythonExecutorOperator is implemented. We don't
# Need to Fake the whole FakeComponentAsDoFn. Instead, just fake or mock
# executors.
class _FakeComponentAsDoFn(beam_dag_runner._PipelineNodeAsDoFn):
def __init__(self, pipeline_node: pipeline_pb2.PipelineNode,
mlmd_connection: metadata.Metadata,
pipeline_info: pipeline_pb2.PipelineInfo,
pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec,
def setUp(self):
super(TaskManagerE2ETest, self).setUp()
pipeline_root = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self.id())
# Makes sure multiple connections within a test always connect to the same
# MLMD instance.
metadata_path = os.path.join(pipeline_root, 'metadata', 'metadata.db')
self._metadata_path = metadata_path
connection_config = metadata.sqlite_metadata_connection_config(
metadata_path)
connection_config.sqlite.SetInParent()
self._mlmd_connection = metadata.Metadata(
connection_config=connection_config)
# Sets up the pipeline.
pipeline = pipeline_pb2.Pipeline()
self.load_proto_from_text(
os.path.join(os.path.dirname(__file__), 'testdata',
'async_pipeline.pbtxt'), pipeline)
# Extracts components.
self._example_gen = pipeline.nodes[0].pipeline_node
self._transform = pipeline.nodes[1].pipeline_node
self._trainer = pipeline.nodes[2].pipeline_node
# Pack deployment config for testing.
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='fake.ClassPath')
deployment_config.executor_specs[self._trainer.node_info.id].Pack(
executor_spec)
deployment_config.executor_specs[self._transform.node_info.id].Pack(
executor_spec)
self._type_url = deployment_config.executor_specs[
self._trainer.node_info.id].type_url
pipeline.deployment_config.Pack(deployment_config)
self._pipeline = pipeline
self._pipeline_info = pipeline.pipeline_info
self._pipeline_runtime_spec = pipeline.runtime_spec
self._pipeline_runtime_spec.pipeline_root.field_value.string_value = (
pipeline_root)
ts.TaskSchedulerRegistry.clear()
self._task_queue = tq.TaskQueue()
# Run fake example-gen to prepare downstreams component triggers.
test_utils.fake_example_gen_run(self._mlmd_connection,
self._example_gen, 1, 1)
# Task generator should produce a task to run transform.
with self._mlmd_connection as m:
pipeline_state = pstate.PipelineState(m, self._pipeline, 0)
tasks = asptg.AsyncPipelineTaskGenerator(
m, pipeline_state, self._task_queue.contains_task_id,
service_jobs.DummyServiceJobManager()).generate()
self.assertLen(tasks, 1)
task = tasks[0]
self.assertEqual('my_transform', task.node_uid.node_id)
# Task generator should produce a task to run transform.
with self._mlmd_connection as m:
pipeline_state = pstate.PipelineState(m, self._pipeline, 0)
tasks = asptg.AsyncPipelineTaskGenerator(
m, pipeline_state, self._task_queue.contains_task_id,
service_jobs.DummyServiceJobManager()).generate()
self.assertLen(tasks, 1)
self._task = tasks[0]
self.assertEqual('my_transform', self._task.node_uid.node_id)
self._task_queue.enqueue(self._task)
# There should be 1 active execution in MLMD.
with self._mlmd_connection as m:
executions = m.store.get_executions()
active_executions = [
e for e in executions
if e.last_known_state == metadata_store_pb2.Execution.RUNNING
]
self.assertLen(active_executions, 1)
# Active execution id.
self._execution_id = active_executions[0].id
def setUp(self):
super().setUp()
pipeline_root = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self.id())
# Makes sure multiple connections within a test always connect to the same
# MLMD instance.
metadata_path = os.path.join(pipeline_root, 'metadata', 'metadata.db')
self._metadata_path = metadata_path
connection_config = metadata.sqlite_metadata_connection_config(
metadata_path)
connection_config.sqlite.SetInParent()
self._mlmd_connection = metadata.Metadata(
connection_config=connection_config)
# Sets up the pipeline.
pipeline = test_async_pipeline.create_pipeline()
# Extracts components.
self._example_gen = pipeline.nodes[0].pipeline_node
self._transform = pipeline.nodes[1].pipeline_node
self._trainer = pipeline.nodes[2].pipeline_node
# Pack deployment config for testing.
deployment_config = pipeline_pb2.IntermediateDeploymentConfig()
executor_spec = pipeline_pb2.ExecutorSpec.PythonClassExecutorSpec(
class_path='fake.ClassPath')
deployment_config.executor_specs[self._trainer.node_info.id].Pack(
executor_spec)
deployment_config.executor_specs[self._transform.node_info.id].Pack(
executor_spec)
self._type_url = deployment_config.executor_specs[
self._trainer.node_info.id].type_url
pipeline.deployment_config.Pack(deployment_config)
self._pipeline = pipeline
self._pipeline_info = pipeline.pipeline_info
self._pipeline_runtime_spec = pipeline.runtime_spec
self._pipeline_runtime_spec.pipeline_root.field_value.string_value = (
pipeline_root)
ts.TaskSchedulerRegistry.clear()
self._task_queue = tq.TaskQueue()
# Run fake example-gen to prepare downstreams component triggers.
test_utils.fake_example_gen_run(self._mlmd_connection,
self._example_gen, 1, 1)
# Task generator should produce two tasks for transform. The first one is
# UpdateNodeStateTask and the second one is ExecNodeTask.
with self._mlmd_connection as m:
pipeline_state = pstate.PipelineState.new(m, self._pipeline)
tasks = asptg.AsyncPipelineTaskGenerator(
m, self._task_queue.contains_task_id,
service_jobs.DummyServiceJobManager()).generate(pipeline_state)
self.assertLen(tasks, 2)
self.assertTrue(task_lib.is_update_node_state_task(tasks[0]))
self.assertEqual(pstate.NodeState.RUNNING, tasks[0].state)
self.assertEqual('my_transform', tasks[0].node_uid.node_id)
self.assertTrue(task_lib.is_exec_node_task(tasks[1]))
self.assertEqual('my_transform', tasks[1].node_uid.node_id)
self.assertTrue(os.path.exists(tasks[1].stateful_working_dir))
self.assertTrue(os.path.exists(tasks[1].tmp_dir))
self._task = tasks[1]
self._output_artifact_uri = self._task.output_artifacts[
'transform_graph'][0].uri
self.assertTrue(os.path.exists(self._output_artifact_uri))
self._task_queue.enqueue(self._task)
# There should be 1 active execution in MLMD.
with self._mlmd_connection as m:
executions = m.store.get_executions()
active_executions = [
e for e in executions
if e.last_known_state == metadata_store_pb2.Execution.RUNNING
]
self.assertLen(active_executions, 1)
# Active execution id.
self._execution_id = active_executions[0].id
