def test_get_model_spec(self):
(
model,
dataset_fn,
loss,
optimizer,
eval_metrics_fn,
prediction_outputs_processor,
) = get_model_spec(
model_zoo=_model_zoo_path,
model_def="test_module.custom_model",
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
)
self.assertTrue(model is not None)
self.assertTrue(dataset_fn is not None)
self.assertTrue(loss is not None)
self.assertTrue(optimizer is not None)
self.assertTrue(eval_metrics_fn is not None)
self.assertTrue(prediction_outputs_processor is not None)
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
get_model_steps=1,
):
"""
Arguments:
model_file: A module to define the model
channel: grpc channel
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
self._model,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._init_embedding_layer()
self._var_created = self._model.built
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._embedding_service_endpoint = embedding_service_endpoint
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION
)
self._get_model_steps = get_model_steps
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
get_model_steps=1,
):
"""
Arguments:
worker_id: The worker ID.
job_type: The job type.
minibatch_size: The size of the minibatch used for each iteration.
model_zoo: The directory that contains user-defined model files
or a specific model file.
dataset_fn: The name of the dataset function defined in the
model file.
loss: The name of the loss function defined in the model file.
optimizer: The name of the optimizer defined in the model file.
eval_metrics_fn: The name of the evaluation metrics function
defined in the model file.
channel: The channel for the gRPC master service.
embedding_service_endpoint: The endpoint to the embedding service.
model_def: The import path to the model definition
function/class in the model zoo, e.g.
"cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that
will be used to instantiate the model,
e.g. "param1=1,param2=2".
prediction_outputs_processor: The name of the prediction output
processor class defined in the model file.
get_model_steps: Worker will perform `get_model` from the
parameter server every this many steps.
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
self._model,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._init_embedding_layer()
self._var_created = self._model.built
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._embedding_service_endpoint = embedding_service_endpoint
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION)
self._get_model_steps = get_model_steps
