def query_and_train(
input_table_spec: TableSpec,
model: ModelManager__Union,
num_epochs: int,
use_gpu: bool,
*,
setup_data: Optional[Dict[str, bytes]] = None,
saved_setup_data: Optional[Dict[str, bytes]] = None,
normalization_data_map: Optional[Dict[str, NormalizationData]] = None,
reward_options: Optional[RewardOptions] = None,
reader_options: Optional[ReaderOptions] = None,
resource_options: Optional[ResourceOptions] = None,
warmstart_path: Optional[str] = None,
validator: Optional[ModelValidator__Union] = None,
publisher: Optional[ModelPublisher__Union] = None,
named_model_ids: Optional[ModuleNameToEntityId] = None,
recurring_period: Optional[RecurringPeriod] = None,
) -> RLTrainingOutput:
child_workflow_id = get_workflow_id()
if named_model_ids is None:
named_model_ids = get_new_named_entity_ids(model.value.serving_module_names())
logger.info("Starting query")
reward_options = reward_options or RewardOptions()
reader_options = reader_options or ReaderOptions()
resource_options = resource_options or ResourceOptions()
manager = model.value
if saved_setup_data is not None:
def _maybe_get_bytes(v) -> bytes:
if isinstance(v, bytes):
return v
# HACK: FBLearner sometimes pack bytes into Blob
return v.data
saved_setup_data = {k: _maybe_get_bytes(v) for k, v in saved_setup_data.items()}
if setup_data is None:
data_module = manager.get_data_module(
input_table_spec=input_table_spec,
reward_options=reward_options,
reader_options=reader_options,
saved_setup_data=saved_setup_data,
)
if data_module is not None:
setup_data = data_module.prepare_data()
# Throw away existing normalization data map
normalization_data_map = None
if sum([int(setup_data is not None), int(normalization_data_map is not None)]) != 1:
raise ValueError("setup_data and normalization_data_map are mutually exclusive")
train_dataset = None
eval_dataset = None
if normalization_data_map is not None:
calc_cpe_in_training = manager.should_generate_eval_dataset
sample_range_output = get_sample_range(input_table_spec, calc_cpe_in_training)
train_dataset = manager.query_data(
input_table_spec=input_table_spec,
sample_range=sample_range_output.train_sample_range,
reward_options=reward_options,
)
eval_dataset = None
if calc_cpe_in_training:
eval_dataset = manager.query_data(
input_table_spec=input_table_spec,
sample_range=sample_range_output.eval_sample_range,
reward_options=reward_options,
)
logger.info("Starting training")
results = manager.train_workflow(
train_dataset,
eval_dataset,
num_epochs=num_epochs,
use_gpu=use_gpu,
setup_data=setup_data,
normalization_data_map=normalization_data_map,
named_model_ids=named_model_ids,
child_workflow_id=child_workflow_id,
reward_options=reward_options,
reader_options=reader_options,
resource_options=resource_options,
warmstart_path=warmstart_path,
)
if validator is not None:
results = run_validator(validator, results)
if publisher is not None:
results = run_publisher(
publisher,
model,
results,
named_model_ids,
child_workflow_id,
recurring_period,
)
return results
def query_and_train(
input_table_spec: TableSpec,
model: ModelManager__Union,
normalization_data_map: Dict[str, NormalizationData],
num_epochs: int,
use_gpu: bool,
reward_options: Optional[RewardOptions] = None,
reader_options: Optional[ReaderOptions] = None,
resource_options: Optional[ResourceOptions] = None,
warmstart_path: Optional[str] = None,
validator: Optional[ModelValidator__Union] = None,
publisher: Optional[ModelPublisher__Union] = None,
parent_workflow_id: Optional[int] = None,
recurring_period: Optional[RecurringPeriod] = None,
) -> RLTrainingOutput:
child_workflow_id = get_workflow_id()
if parent_workflow_id is None:
parent_workflow_id = child_workflow_id
logger.info("Starting query")
reward_options = reward_options or RewardOptions()
reader_options = reader_options or ReaderOptions()
resource_options = resource_options or ResourceOptions()
manager = model.value
calc_cpe_in_training = manager.should_generate_eval_dataset
sample_range_output = get_sample_range(input_table_spec,
calc_cpe_in_training)
train_dataset = manager.query_data(
input_table_spec=input_table_spec,
sample_range=sample_range_output.train_sample_range,
reward_options=reward_options,
)
eval_dataset = None
if calc_cpe_in_training:
eval_dataset = manager.query_data(
input_table_spec=input_table_spec,
sample_range=sample_range_output.eval_sample_range,
reward_options=reward_options,
)
logger.info("Starting training")
results = manager.train_workflow(
train_dataset,
eval_dataset,
normalization_data_map,
num_epochs,
use_gpu,
parent_workflow_id=parent_workflow_id,
child_workflow_id=child_workflow_id,
reward_options=reward_options,
reader_options=reader_options,
resource_options=resource_options,
warmstart_path=warmstart_path,
)
if validator is not None:
results = run_validator(validator, results)
if publisher is not None:
results = run_publisher(
publisher,
model,
results,
parent_workflow_id,
child_workflow_id,
recurring_period,
)
return results
