def test_action_repeated(self):
mock_env = self._get_mock_env_episode()
env = wrappers.ActionRepeat(mock_env, 3)
env.reset()
env.step([2])
env.step([3])
mock_env.step.assert_has_calls([mock.call([2])] + [mock.call([3])] * 3)
def testBlockingSave(self):
saver = mock.create_autospec(policy_saver.PolicySaver, instance=True)
async_saver = async_policy_saver.AsyncPolicySaver(saver)
path1 = os.path.join(self.get_temp_dir(), 'save_model')
path2 = os.path.join(self.get_temp_dir(), 'save_model2')
self.evaluate(tf.compat.v1.global_variables_initializer())
async_saver.save(path1)
async_saver.save(path2, blocking=True)
saver.save.assert_has_calls([mock.call(path1), mock.call(path2)])
def test_action_stops_on_last(self):
mock_env = self._get_mock_env_episode()
env = wrappers.ActionRepeat(mock_env, 3)
env.reset()
env.step([2])
time_step = env.step([3])
mock_env.step.assert_has_calls([mock.call([2])] * 3 + [mock.call([3])])
self.assertEqual(7, time_step.reward)
self.assertEqual([3], time_step.observation)
def test_task_handling(self, mock_publish):
collector = _Collector()
# Register a fake task scheduler.
ts.TaskSchedulerRegistry.register(
self._type_url,
functools.partial(
_FakeTaskScheduler,
block_nodes={'Trainer', 'Transform'},
collector=collector))
task_queue = tq.TaskQueue()
# Enqueue some tasks.
trainer_exec_task = _test_exec_node_task('Trainer', 'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(trainer_exec_task)
task_queue.enqueue(_test_cancel_node_task('Trainer', 'test-pipeline'))
with self._task_manager(task_queue) as task_manager:
# Enqueue more tasks after task manager starts.
transform_exec_task = _test_exec_node_task('Transform', 'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(transform_exec_task)
evaluator_exec_task = _test_exec_node_task('Evaluator', 'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(evaluator_exec_task)
task_queue.enqueue(_test_cancel_node_task('Transform', 'test-pipeline'))
self.assertTrue(task_manager.done())
self.assertIsNone(task_manager.exception())
# 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(
mlmd_handle=mock.ANY,
task=trainer_exec_task,
result=mock.ANY),
mock.call(
mlmd_handle=mock.ANY,
task=transform_exec_task,
result=mock.ANY),
mock.call(
mlmd_handle=mock.ANY,
task=evaluator_exec_task,
result=mock.ANY),
],
any_order=True)
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 testBlockingCheckpointSave(self):
saver = mock.create_autospec(policy_saver.PolicySaver, instance=True)
async_saver = async_policy_saver.AsyncPolicySaver(saver)
path1 = os.path.join(self.get_temp_dir(), 'save_model')
path2 = os.path.join(self.get_temp_dir(), 'save_model2')
self.evaluate(tf.compat.v1.global_variables_initializer())
async_saver.save_checkpoint(path1)
async_saver.save_checkpoint(path2, blocking=True)
saver.save_checkpoint.assert_has_calls([mock.call(path1), mock.call(path2)])
# Have to close the saver to avoid hanging threads that will prevent OSS
# tests from finishing.
async_saver.close()
def test_after_train_step_fn_with_fresh_data_only(self,
create_strategy_fn):
strategy = create_strategy_fn()
with strategy.scope():
# Prepare the test context context.
train_step = train_utils.create_train_step()
train_step.assign(225)
train_steps_per_policy_update = 100
# Create the after train function to test, and the test input.
after_train_step_fn = (
train_utils.create_staleness_metrics_after_train_step_fn(
train_step,
train_steps_per_policy_update=train_steps_per_policy_update
))
observation_train_steps = np.array([[200], [200], [200]],
dtype=np.int64)
# Define the expectations (expected scalar summary calls).
expected_scalar_summary_calls = [
mock.call(name='staleness/max_train_step_delta_in_batch',
data=0,
step=225),
mock.call(name='staleness/max_policy_update_delta_in_batch',
data=0,
step=225),
mock.call(name='staleness/num_stale_obserations_in_batch',
data=0,
step=225)
]
# Call the after train function and check the expectations.
with mock.patch.object(tf.summary, 'scalar',
autospec=True) as mock_scalar_summary:
# Call the `after_train_function` on the test input. Assumed the
# observation train steps are stored in the field `priority` of the
# the sample info of Reverb.
strategy.run(after_train_step_fn,
args=((None,
reverb.replay_sample.SampleInfo(
key=None,
probability=None,
table_size=None,
priority=observation_train_steps)),
None))
# Check if the expected calls happened on the scalar summary.
mock_scalar_summary.assert_has_calls(
expected_scalar_summary_calls, any_order=False)
def test_exceptions_are_surfaced(self, mock_publish):
def _publish(**kwargs):
task = kwargs['task']
assert task_lib.is_exec_node_task(task)
if task.node_uid.node_id == 'Transform':
raise ValueError('test error')
return mock.DEFAULT
mock_publish.side_effect = _publish
collector = _Collector()
# Register a fake task scheduler.
ts.TaskSchedulerRegistry.register(
self._type_url,
functools.partial(
_FakeTaskScheduler, block_nodes={}, collector=collector))
task_queue = tq.TaskQueue()
with self._task_manager(task_queue) as task_manager:
transform_task = _test_exec_node_task('Transform', 'test-pipeline',
pipeline=self._pipeline)
trainer_task = _test_exec_node_task('Trainer', 'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(transform_task)
task_queue.enqueue(trainer_task)
self.assertTrue(task_manager.done())
exception = task_manager.exception()
self.assertIsNotNone(exception)
self.assertIsInstance(exception, tm.TasksProcessingError)
self.assertLen(exception.errors, 1)
self.assertEqual('test error', str(exception.errors[0]))
self.assertCountEqual([transform_task, trainer_task],
collector.scheduled_tasks)
mock_publish.assert_has_calls([
mock.call(
mlmd_handle=mock.ANY,
task=transform_task,
result=mock.ANY),
mock.call(
mlmd_handle=mock.ANY,
task=trainer_task,
result=mock.ANY),
],
any_order=True)
def testLifeLoss(self):
self._setup_mocks()
with self.cached_session() as sess:
self.trainer._initialize_graph(sess)
time_step = ts_restart(0)
# Run a regular step.
self.trainer._env.step.return_value = ts_transition(1)
time_step = self.trainer._collect_step(time_step,
self.metric_observers,
train=True)
self.trainer._replay_buffer.add_batch.reset_mock()
self.observer.reset_mock()
# Lose a life, but not the end of a game.
self.trainer._env.step.side_effect = [
ts_termination(2),
ts_transition(3),
]
time_step = self.trainer._collect_step(time_step,
self.metric_observers,
train=True)
self.assertTrue(time_step.is_mid())
expected_rb_calls = [
mock.call(trajectory_last(1, discount=1.0)),
mock.call(trajectory_first(2))
]
self.assertEqual(
expected_rb_calls,
self.trainer._replay_buffer.add_batch.call_args_list)
expected_observer_calls = [
mock.call(trajectory_mid(1)),
mock.call(trajectory_mid(2)),
]
self.assertEqual(expected_observer_calls,
self.observer.call_args_list)
def test_schedule_successful_task_should_send_pubsub_message(self):
job = self._define_job_with_two_dependent_tasks()
scheduler = job.make_scheduler()
job.start()
message = json.dumps({'id': job.id}).encode('utf-8')
call = [mock.call(self.topic_path, data=message)]
# When start() is called, there should be #{max_parallel_tasks} pubsub
# messages sent to pubsub.
self.mock_pubsub.publish.assert_has_calls(call *
self.max_parallel_tasks)
self.assertEqual(self.mock_pubsub.publish.call_count,
self.max_parallel_tasks)
self._call_job_scheduler(job, scheduler)
# When task1 finishes, there should be another #{max_parallel_tasks} pubsub
# messages sent to pubsub to trigger subsequent tasks.
self.mock_pubsub.publish.assert_has_calls(
call * (self.max_parallel_tasks * 2))
self.assertEqual(self.mock_pubsub.publish.call_count,
self.max_parallel_tasks * 2)
def test_upload_directory(self, mock_walk, mock_upload_file):
destination_blob_path = 'dir1'
file_structure = [['/tmp/dir1', ['dir2', 'dir3'], ['file1', 'file2']],
['/tmp/dir1/dir2', [], ['file3']],
['/tmp/dir1/dir3', ['dir4'], ['file4']],
['/tmp/dir1/dir3/dir4', [], []]]
mock_walk.return_value = file_structure
calls = []
for (root, _, files) in file_structure:
for file in files:
source_file = root + '/' + file
call = mock.call(
self.cloud_storage_obj, root + '/' + file,
self.mock_bucket,
source_file.replace(self.source_directory_path,
destination_blob_path))
calls.append(call)
self.cloud_storage_obj.upload_directory(self.source_directory_path,
self.mock_bucket,
destination_blob_path)
mock_upload_file.assert_has_calls(calls)
def test_stop_initiated_pipelines(self, pipeline, mock_gen_task_from_active,
mock_async_task_gen, mock_sync_task_gen):
with self._mlmd_connection as m:
pipeline.nodes.add().pipeline_node.node_info.id = 'ExampleGen'
pipeline.nodes.add().pipeline_node.node_info.id = 'Transform'
pipeline.nodes.add().pipeline_node.node_info.id = 'Trainer'
pipeline.nodes.add().pipeline_node.node_info.id = 'Evaluator'
mock_service_job_manager = mock.create_autospec(
service_jobs.ServiceJobManager, instance=True)
mock_service_job_manager.is_pure_service_node.side_effect = (
lambda _, node_id: node_id == 'ExampleGen')
mock_service_job_manager.is_mixed_service_node.side_effect = (
lambda _, node_id: node_id == 'Transform')
pipeline_ops.initiate_pipeline_start(m, pipeline)
with pstate.PipelineState.load(
m, task_lib.PipelineUid.from_pipeline(pipeline)) as pipeline_state:
pipeline_state.initiate_stop(
status_lib.Status(code=status_lib.Code.CANCELLED))
pipeline_execution = pipeline_state.execution
task_queue = tq.TaskQueue()
# For the stop-initiated pipeline, "Transform" execution task is in queue,
# "Trainer" has an active execution in MLMD but no task in queue,
# "Evaluator" has no active execution.
task_queue.enqueue(
test_utils.create_exec_node_task(
task_lib.NodeUid(
pipeline_uid=task_lib.PipelineUid.from_pipeline(pipeline),
node_id='Transform')))
transform_task = task_queue.dequeue() # simulates task being processed
mock_gen_task_from_active.side_effect = [
test_utils.create_exec_node_task(
node_uid=task_lib.NodeUid(
pipeline_uid=task_lib.PipelineUid.from_pipeline(pipeline),
node_id='Trainer'),
is_cancelled=True), None, None, None, None
]
pipeline_ops.orchestrate(m, task_queue, mock_service_job_manager)
# There are no active pipelines so these shouldn't be called.
mock_async_task_gen.assert_not_called()
mock_sync_task_gen.assert_not_called()
# stop_node_services should be called for ExampleGen which is a pure
# service node.
mock_service_job_manager.stop_node_services.assert_called_once_with(
mock.ANY, 'ExampleGen')
mock_service_job_manager.reset_mock()
task_queue.task_done(transform_task) # Pop out transform task.
# CancelNodeTask for the "Transform" ExecNodeTask should be next.
task = task_queue.dequeue()
task_queue.task_done(task)
self.assertTrue(task_lib.is_cancel_node_task(task))
self.assertEqual('Transform', task.node_uid.node_id)
# ExecNodeTask (with is_cancelled=True) for "Trainer" is next.
task = task_queue.dequeue()
task_queue.task_done(task)
self.assertTrue(task_lib.is_exec_node_task(task))
self.assertEqual('Trainer', task.node_uid.node_id)
self.assertTrue(task.is_cancelled)
self.assertTrue(task_queue.is_empty())
mock_gen_task_from_active.assert_has_calls([
mock.call(
m,
pipeline_state.pipeline,
pipeline.nodes[2].pipeline_node,
mock.ANY,
is_cancelled=True),
mock.call(
m,
pipeline_state.pipeline,
pipeline.nodes[3].pipeline_node,
mock.ANY,
is_cancelled=True)
])
self.assertEqual(2, mock_gen_task_from_active.call_count)
# Pipeline execution should continue to be active since active node
# executions were found in the last call to `orchestrate`.
[execution] = m.store.get_executions_by_id([pipeline_execution.id])
self.assertTrue(execution_lib.is_execution_active(execution))
# Call `orchestrate` again; this time there are no more active node
# executions so the pipeline should be marked as cancelled.
pipeline_ops.orchestrate(m, task_queue, mock_service_job_manager)
self.assertTrue(task_queue.is_empty())
[execution] = m.store.get_executions_by_id([pipeline_execution.id])
self.assertEqual(metadata_store_pb2.Execution.CANCELED,
execution.last_known_state)
# stop_node_services should be called on both ExampleGen and Transform
# which are service nodes.
mock_service_job_manager.stop_node_services.assert_has_calls(
[mock.call(mock.ANY, 'ExampleGen'),
mock.call(mock.ANY, 'Transform')],
any_order=True)
def test_stop_initiated_async_pipelines(self, mock_gen_task_from_active,
mock_async_task_gen,
mock_sync_task_gen):
with self._mlmd_connection as m:
pipeline1 = _test_pipeline('pipeline1')
pipeline1.nodes.add().pipeline_node.node_info.id = 'Transform'
pipeline1.nodes.add().pipeline_node.node_info.id = 'Trainer'
pipeline1.nodes.add().pipeline_node.node_info.id = 'Evaluator'
pipeline_ops.initiate_pipeline_start(m, pipeline1)
pipeline1_execution = pipeline_ops._initiate_pipeline_stop(
m, task_lib.PipelineUid.from_pipeline(pipeline1))
task_queue = tq.TaskQueue()
# For the stop-initiated pipeline, "Transform" execution task is in queue,
# "Trainer" has an active execution in MLMD but no task in queue,
# "Evaluator" has no active execution.
task_queue.enqueue(
test_utils.create_exec_node_task(node_uid=task_lib.NodeUid(
pipeline_uid=task_lib.PipelineUid(pipeline_id='pipeline1',
pipeline_run_id=None),
node_id='Transform')))
transform_task = task_queue.dequeue(
) # simulates task being processed
mock_gen_task_from_active.side_effect = [
test_utils.create_exec_node_task(node_uid=task_lib.NodeUid(
pipeline_uid=task_lib.PipelineUid(pipeline_id='pipeline1',
pipeline_run_id=None),
node_id='Trainer'),
is_cancelled=True), None,
None, None, None
]
pipeline_ops.generate_tasks(m, task_queue)
# There are no active pipelines so these shouldn't be called.
mock_async_task_gen.assert_not_called()
mock_sync_task_gen.assert_not_called()
# Simulate finishing the "Transform" ExecNodeTask.
task_queue.task_done(transform_task)
# CancelNodeTask for the "Transform" ExecNodeTask should be next.
task = task_queue.dequeue()
task_queue.task_done(task)
self.assertTrue(task_lib.is_cancel_node_task(task))
self.assertEqual('Transform', task.node_uid.node_id)
# ExecNodeTask for "Trainer" is next.
task = task_queue.dequeue()
task_queue.task_done(task)
self.assertTrue(task_lib.is_exec_node_task(task))
self.assertEqual('Trainer', task.node_uid.node_id)
self.assertTrue(task_queue.is_empty())
mock_gen_task_from_active.assert_has_calls([
mock.call(m,
pipeline1,
pipeline1.nodes[1].pipeline_node,
mock.ANY,
is_cancelled=True),
mock.call(m,
pipeline1,
pipeline1.nodes[2].pipeline_node,
mock.ANY,
is_cancelled=True)
])
self.assertEqual(2, mock_gen_task_from_active.call_count)
# Pipeline execution should continue to be active since active node
# executions were found in the last call to `generate_tasks`.
[execution
] = m.store.get_executions_by_id([pipeline1_execution.id])
self.assertTrue(execution_lib.is_execution_active(execution))
# Call `generate_tasks` again; this time there are no more active node
# executions so the pipeline should be marked as cancelled.
pipeline_ops.generate_tasks(m, task_queue)
self.assertTrue(task_queue.is_empty())
[execution
] = m.store.get_executions_by_id([pipeline1_execution.id])
self.assertEqual(metadata_store_pb2.Execution.CANCELED,
execution.last_known_state)
def test_task_generation(self, use_task_queue):
"""Tests async pipeline task generation.
Args:
use_task_queue: If task queue is enabled, new tasks are only generated if
a task with the same task_id does not already exist in the queue.
`use_task_queue=False` is useful to test the case of task generation
when task queue is empty (for eg: due to orchestrator restart).
"""
# Simulate that ExampleGen has already completed successfully.
test_utils.fake_example_gen_run(self._mlmd_connection, self._example_gen, 1,
1)
# Generate once.
[update_example_gen_task, update_transform_task,
exec_transform_task] = self._generate_and_test(
use_task_queue,
num_initial_executions=1,
num_tasks_generated=3,
num_new_executions=1,
num_active_executions=1,
expected_exec_nodes=[self._transform])
self.assertTrue(task_lib.is_update_node_state_task(update_example_gen_task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_transform_task))
self.assertEqual(pstate.NodeState.RUNNING, update_transform_task.state)
self.assertTrue(task_lib.is_exec_node_task(exec_transform_task))
self._mock_service_job_manager.ensure_node_services.assert_has_calls([
mock.call(mock.ANY, self._example_gen.node_info.id),
mock.call(mock.ANY, self._transform.node_info.id)
])
# No new effects if generate called again.
tasks = self._generate_and_test(
use_task_queue,
num_initial_executions=2,
num_tasks_generated=1 if use_task_queue else 3,
num_new_executions=0,
num_active_executions=1,
expected_exec_nodes=[] if use_task_queue else [self._transform])
if not use_task_queue:
exec_transform_task = tasks[2]
# Mark transform execution complete.
self._finish_node_execution(use_task_queue, exec_transform_task)
# Trainer execution task should be generated next.
[
update_example_gen_task, update_transform_task, update_trainer_task,
exec_trainer_task
] = self._generate_and_test(
use_task_queue,
num_initial_executions=2,
num_tasks_generated=4,
num_new_executions=1,
num_active_executions=1,
expected_exec_nodes=[self._trainer])
self.assertTrue(task_lib.is_update_node_state_task(update_example_gen_task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_transform_task))
self.assertEqual(pstate.NodeState.STARTED, update_transform_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_trainer_task))
self.assertEqual(pstate.NodeState.RUNNING, update_trainer_task.state)
self.assertTrue(task_lib.is_exec_node_task(exec_trainer_task))
# Mark the trainer execution complete.
self._finish_node_execution(use_task_queue, exec_trainer_task)
# Only UpdateNodeStateTask are generated as there are no new inputs.
tasks = self._generate_and_test(
use_task_queue,
num_initial_executions=3,
num_tasks_generated=3,
num_new_executions=0,
num_active_executions=0)
for task in tasks:
self.assertTrue(task_lib.is_update_node_state_task(task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
# Fake another ExampleGen run.
test_utils.fake_example_gen_run(self._mlmd_connection, self._example_gen, 1,
1)
# Both transform and trainer tasks should be generated as they both find
# new inputs.
[
update_example_gen_task, update_transform_task, exec_transform_task,
update_trainer_task, exec_trainer_task
] = self._generate_and_test(
use_task_queue,
num_initial_executions=4,
num_tasks_generated=5,
num_new_executions=2,
num_active_executions=2,
expected_exec_nodes=[self._transform, self._trainer])
self.assertTrue(task_lib.is_update_node_state_task(update_example_gen_task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_transform_task))
self.assertEqual(pstate.NodeState.RUNNING, update_transform_task.state)
self.assertTrue(task_lib.is_exec_node_task(exec_transform_task))
self.assertTrue(task_lib.is_update_node_state_task(update_trainer_task))
self.assertEqual(pstate.NodeState.RUNNING, update_trainer_task.state)
self.assertTrue(task_lib.is_exec_node_task(exec_trainer_task))
# Re-generation will produce the same tasks when task queue disabled.
tasks = self._generate_and_test(
use_task_queue,
num_initial_executions=6,
num_tasks_generated=1 if use_task_queue else 5,
num_new_executions=0,
num_active_executions=2,
expected_exec_nodes=[]
if use_task_queue else [self._transform, self._trainer])
if not use_task_queue:
self.assertTrue(task_lib.is_update_node_state_task(tasks[0]))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(tasks[1]))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_exec_node_task(tasks[2]))
self.assertTrue(task_lib.is_update_node_state_task(tasks[3]))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_exec_node_task(tasks[4]))
exec_transform_task = tasks[2]
exec_trainer_task = tasks[4]
else:
self.assertTrue(task_lib.is_update_node_state_task(tasks[0]))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
# Mark transform execution complete.
self._finish_node_execution(use_task_queue, exec_transform_task)
# Mark the trainer execution complete.
self._finish_node_execution(use_task_queue, exec_trainer_task)
# Trainer should be triggered again due to transform producing new output.
[
update_example_gen_task, update_transform_task, update_trainer_task,
exec_trainer_task
] = self._generate_and_test(
use_task_queue,
num_initial_executions=6,
num_tasks_generated=4,
num_new_executions=1,
num_active_executions=1,
expected_exec_nodes=[self._trainer])
self.assertTrue(task_lib.is_update_node_state_task(update_example_gen_task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_transform_task))
self.assertEqual(pstate.NodeState.STARTED, update_transform_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_trainer_task))
self.assertEqual(pstate.NodeState.RUNNING, update_trainer_task.state)
self.assertTrue(task_lib.is_exec_node_task(exec_trainer_task))
# Finally, no new tasks once trainer completes.
self._finish_node_execution(use_task_queue, exec_trainer_task)
[update_example_gen_task, update_transform_task,
update_trainer_task] = self._generate_and_test(
use_task_queue,
num_initial_executions=7,
num_tasks_generated=3,
num_new_executions=0,
num_active_executions=0)
self.assertTrue(task_lib.is_update_node_state_task(update_example_gen_task))
self.assertEqual(pstate.NodeState.RUNNING, update_example_gen_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_transform_task))
self.assertEqual(pstate.NodeState.STARTED, update_transform_task.state)
self.assertTrue(task_lib.is_update_node_state_task(update_trainer_task))
self.assertEqual(pstate.NodeState.STARTED, update_trainer_task.state)
if use_task_queue:
self.assertTrue(self._task_queue.is_empty())
def test_task_handling(self, mock_publish):
collector = _Collector()
# Register a fake task scheduler.
ts.TaskSchedulerRegistry.register(
self._type_url,
functools.partial(_FakeTaskScheduler,
block_nodes={'Trainer', 'Transform', 'Pusher'},
collector=collector))
task_queue = tq.TaskQueue()
# Enqueue some tasks.
trainer_exec_task = _test_exec_node_task('Trainer',
'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(trainer_exec_task)
task_queue.enqueue(_test_cancel_node_task('Trainer', 'test-pipeline'))
with self._task_manager(task_queue) as task_manager:
# Enqueue more tasks after task manager starts.
transform_exec_task = _test_exec_node_task('Transform',
'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(transform_exec_task)
evaluator_exec_task = _test_exec_node_task('Evaluator',
'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(evaluator_exec_task)
task_queue.enqueue(
_test_cancel_node_task('Transform', 'test-pipeline'))
pusher_exec_task = _test_exec_node_task('Pusher',
'test-pipeline',
pipeline=self._pipeline)
task_queue.enqueue(pusher_exec_task)
task_queue.enqueue(
_test_cancel_node_task('Pusher', 'test-pipeline', pause=True))
self.assertTrue(task_manager.done())
self.assertIsNone(task_manager.exception())
# Ensure that all exec and cancellation tasks were processed correctly.
self.assertCountEqual([
trainer_exec_task,
transform_exec_task,
evaluator_exec_task,
pusher_exec_task,
], collector.scheduled_tasks)
self.assertCountEqual([
trainer_exec_task,
transform_exec_task,
pusher_exec_task,
], collector.cancelled_tasks)
result_ok = ts.TaskSchedulerResult(status=status_lib.Status(
code=status_lib.Code.OK, message='_FakeTaskScheduler result'))
result_cancelled = ts.TaskSchedulerResult(
status=status_lib.Status(code=status_lib.Code.CANCELLED,
message='_FakeTaskScheduler result'))
mock_publish.assert_has_calls([
mock.call(mlmd_handle=mock.ANY,
task=trainer_exec_task,
result=result_cancelled),
mock.call(mlmd_handle=mock.ANY,
task=transform_exec_task,
result=result_cancelled),
mock.call(mlmd_handle=mock.ANY,
task=evaluator_exec_task,
result=result_ok),
],
any_order=True)
# It is expected that publish is not called for Pusher because it was
# cancelled with pause=True so there must be only 3 calls.
self.assertLen(mock_publish.mock_calls, 3)