def _init_model_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._feed,
loss,
opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
feed=args.feed,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
self._model_inst = self._model_handler.get_model_to_train(model_inst)
self._model_inst.optimizer = opt_fn()
self._model_inst.loss = loss
self._model_version = -1
self._get_model_steps = args.get_model_steps
def test_on_train_end(self):
worker = MockWorker()
task_data_service = TaskDataService(worker,
JobType.TRAINING_WITH_EVALUATION)
dataset = tf.data.Dataset.from_tensor_slices(
np.array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]]))
task_data_service._pending_train_end_callback_task = (
"",
0,
1,
elasticdl_pb2.TRAIN_END_CALLBACK,
)
task_data_service.get_dataset_by_task = mock.Mock(return_value=dataset)
with tempfile.TemporaryDirectory() as temp_dir_name:
checkpoint_dir = os.path.join(temp_dir_name, "checkpoint")
model = custom_model_with_embedding_layer()
save_checkpoint_without_embedding(model, checkpoint_dir)
model_handler = ModelHandler.get_model_handler(
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
checkpoint_dir=checkpoint_dir,
)
saved_model_exporter = SavedModelExporter(task_data_service,
dataset_fn,
model_handler)
saved_model_path = os.path.join(temp_dir_name, "test_exporter")
params = {"batch_size": 10, "saved_model_path": saved_model_path}
saved_model_exporter.set_params(params)
saved_model_exporter.set_model(model)
saved_model_exporter.on_train_end()
self.assertTrue(os.path.exists(saved_model_path))
self.assertTrue(
os.path.exists(os.path.join(saved_model_path,
"saved_model.pb")))
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
)
self._collective_communicator = (
CollectiveCommunicator() if self._distribution_strategy
== DistributionStrategy.ALLREDUCE else None)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._model_versions_from_ps = [-1 for _ in range(self._ps_num)]
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
if self._get_model_steps > 1:
self._opt = self._opt_fn()
self._non_embed_grads = {}
self._evaluation_result = {}
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
)
self._embedding_service_endpoint = eval(
args.embedding_service_endpoint)
self._distribution_strategy = args.distribution_strategy
self._collective_communicator = (
CollectiveCommunicator() if self._distribution_strategy
== DistributionStrategy.ALLREDUCE else None)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
)
self._get_model_steps = args.get_model_steps
if self._get_model_steps > 1:
self._opt = self._opt_fn()
self._non_embed_grads = None
self._evaluation_result = {}
def setUp(self):
tf.keras.backend.clear_session()
self.master = MasterServicer(
2,
3,
None,
None,
init_var=[],
checkpoint_filename_for_init="",
checkpoint_service=CheckpointService("", 0, 0, False),
evaluation_service=None,
)
self.master._version = 1
self.model_handler = ModelHandler.get_model_handler(
distribution_strategy="ParameterServerStrategy", stub=self.master)
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self._mc,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
custom_data_reader=self._custom_data_reader,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
data_origin=args.training_data,
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
self._opt = self._opt_fn()
self._model.optimizer = self._opt
self._non_embed_grads = {}
self._evaluation_result = {}
saved_model_exporter = SavedModelExporter(self._task_data_service,
self._dataset_fn,
self._model_handler)
# Place default callbacks at the head to execute them firstly
self._callbacks_list.callbacks.insert(0, saved_model_exporter)
self._callbacks_list.set_model(model_inst)
set_callback_parameters(
self._callbacks_list,
batch_size=args.minibatch_size,
saved_model_path=args.output,
checkpoint_path=args.checkpoint_dir,
)
self._allreduce_trainer = None
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
master_addr = args.master_addr.split(":")[0]
self._allreduce_trainer = AllReduceTrainer(self._mc, master_addr,
self._model, self._loss,
self._opt)
def setUp(self):
tf.keras.backend.clear_session()
self.model_handler = ModelHandler.get_model_handler(
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
checkpoint_dir="",
)
def setUp(self):
self.model_handler = ModelHandler.get_model_handler()
def __init__(self, args):
self.logger = get_logger("master", level=args.log_level.upper())
self.num_ps_pods = args.num_ps_pods
self.checkpoint_output_path = args.checkpoint_dir
# Master addr
master_ip = os.getenv("MY_POD_IP", "localhost")
self.master_addr = "%s:%d" % (master_ip, args.port)
self.job_type = Master._get_job_type(args)
# Initialize TensorBoard service if requested
self.tb_service = self._create_tensorboard_service(
args.tensorboard_log_dir, master_ip)
if self.tb_service:
self.tb_client = TensorBoardClient(
job_name=args.job_name,
image_name=args.worker_image,
namespace=args.namespace,
)
# Initialize the components from the model definition
self.model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)).__dict__
self.model_inst = load_model_from_module(args.model_def,
self.model_module,
args.model_params)
model_handler = ModelHandler.get_model_handler(
args.distribution_strategy, checkpoint_dir=args.checkpoint_dir)
self.model_inst = model_handler.get_model_to_train(self.model_inst)
self.optimizer = self.model_module[args.optimizer]()
self._create_data_reader_fn = create_data_reader
if args.custom_data_reader in self.model_module:
self._create_data_reader_fn = self.model_module[
args.custom_data_reader]
# Start task queue
records_per_task = args.minibatch_size * args.num_minibatches_per_task
self.task_d = _make_task_dispatcher(
args.training_data,
args.validation_data,
args.prediction_data,
records_per_task,
args.num_epochs,
args.data_reader_params,
self._create_data_reader_fn,
)
saved_model_path = args.output
if saved_model_path is not None and self.job_type in [
JobType.TRAINING_ONLY,
JobType.TRAINING_WITH_EVALUATION,
]:
self.task_d.add_deferred_callback_create_save_model_task(
saved_model_path)
self.evaluation_service = self._create_evaluation_service(args)
# Initialize master service
self.master_servicer, self.server = self._create_master_service(args)
# Initialize instance manager
self.instance_manager = self._create_instance_manager(args)
self._should_stop = False
self._exit_code = 0
def setUp(self):
tf.keras.backend.clear_session()
self.model_handler = ModelHandler.get_model_handler(
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
checkpoint_dir="elasticdl/python/tests/testdata/functional_ckpt/",
)