def train(
self,
train_dataset: Dataset,
eval_dataset: Optional[Dataset],
num_epochs: int,
reader_options: ReaderOptions,
) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainingOutput.
The field that should not be filled are:
- output_path
"""
batch_preprocessor = self.build_batch_preprocessor()
reporter = self.get_reporter()
# pyre-fixme[16]: `RLTrainer` has no attribute `add_observer`.
self.trainer.add_observer(reporter)
train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
trainer_module=self.trainer,
num_epochs=num_epochs,
use_gpu=self.use_gpu,
batch_preprocessor=batch_preprocessor,
reader_options=self.reader_options,
checkpoint_path=self._lightning_checkpoint_path,
)
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
return RLTrainingOutput(training_report=training_report)
def train(
self,
train_dataset: Optional[Dataset],
eval_dataset: Optional[Dataset],
data_module: Optional[ReAgentDataModule],
num_epochs: int,
reader_options: ReaderOptions,
) -> RLTrainingOutput:
batch_preprocessor = self.build_batch_preprocessor()
reporter = self.get_reporter()
# pyre-fixme[16]: `Trainer` has no attribute `set_reporter`.
# pyre-fixme[16]: `Trainer` has no attribute `set_reporter`.
self.trainer.set_reporter(reporter)
# assert eval_dataset is None
self._lightning_trainer = train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
trainer_module=self.trainer,
data_module=data_module,
num_epochs=num_epochs,
use_gpu=self.use_gpu,
batch_preprocessor=batch_preprocessor,
reader_options=self.reader_options,
checkpoint_path=self._lightning_checkpoint_path,
)
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
return RLTrainingOutput(training_report=training_report)
def train(
self,
train_dataset: Optional[Dataset],
eval_dataset: Optional[Dataset],
data_module: Optional[ReAgentDataModule],
num_epochs: int,
reader_options: ReaderOptions,
resource_options: Optional[ResourceOptions] = None,
) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainingOutput.
The field that should not be filled are:
- output_path
"""
batch_preprocessor = self.build_batch_preprocessor()
reporter = self.get_reporter()
# pyre-fixme[16]: `RLTrainer` has no attribute `set_reporter`.
self.trainer.set_reporter(reporter)
train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
trainer_module=self.trainer,
data_module=None,
num_epochs=num_epochs,
use_gpu=self.use_gpu,
batch_preprocessor=batch_preprocessor,
reader_options=self.reader_options,
checkpoint_path=self._lightning_checkpoint_path,
resource_options=resource_options,
)
rank = get_rank()
if rank == 0:
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
return RLTrainingOutput(training_report=training_report)
# Output from processes with non-0 rank is not used
return RLTrainingOutput()
def train(
self,
train_dataset: Optional[Dataset],
eval_dataset: Optional[Dataset],
test_dataset: Optional[Dataset],
data_module: Optional[ReAgentDataModule],
num_epochs: int,
reader_options: ReaderOptions,
resource_options: Optional[ResourceOptions] = None,
) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainingOutput.
The field that should not be filled are:
- output_path
"""
reporter = self.get_reporter()
# pyre-fixme[16]: `RLTrainer` has no attribute `set_reporter`.
self.trainer.set_reporter(reporter)
assert data_module
# pyre-fixme[16]: `DiscreteDQNBase` has no attribute `_lightning_trainer`.
self._lightning_trainer = train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
test_dataset=test_dataset,
trainer_module=self.trainer,
data_module=data_module,
num_epochs=num_epochs,
use_gpu=self.use_gpu,
logger_name="DiscreteDqn",
reader_options=reader_options,
checkpoint_path=self._lightning_checkpoint_path,
resource_options=resource_options,
)
rank = get_rank()
if rank == 0:
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
logger_data = self._lightning_trainer.logger.line_plot_aggregated
self._lightning_trainer.logger.clear_local_data()
return RLTrainingOutput(training_report=training_report,
logger_data=logger_data)
# Output from processes with non-0 rank is not used
return RLTrainingOutput()
def train(
self,
train_dataset: Optional[Dataset],
eval_dataset: Optional[Dataset],
test_dataset: Optional[Dataset],
data_module: Optional[ReAgentDataModule],
num_epochs: int,
reader_options: ReaderOptions,
resource_options: ResourceOptions,
) -> RLTrainingOutput:
batch_preprocessor = self.build_batch_preprocessor(
resource_options.use_gpu)
reporter = self.get_reporter()
# pyre-fixme[16]: `Trainer` has no attribute `set_reporter`.
# pyre-fixme[16]: `Trainer` has no attribute `set_reporter`.
self.trainer.set_reporter(reporter)
# assert eval_dataset is None
# pyre-fixme[16]: `ActorCriticBase` has no attribute `_lightning_trainer`.
self._lightning_trainer = train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
test_dataset=test_dataset,
trainer_module=self.trainer,
data_module=data_module,
num_epochs=num_epochs,
logger_name="ActorCritic",
batch_preprocessor=batch_preprocessor,
reader_options=self.reader_options,
checkpoint_path=self._lightning_checkpoint_path,
resource_options=resource_options or ResourceOptions(),
)
if reporter is None:
training_report = None
else:
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
logger_data = self._lightning_trainer.logger.line_plot_aggregated
self._lightning_trainer.logger.clear_local_data()
return RLTrainingOutput(training_report=training_report,
logger_data=logger_data)
def train(
self,
trainer_module: ReAgentLightningModule,
train_dataset: Optional[Dataset],
eval_dataset: Optional[Dataset],
test_dataset: Optional[Dataset],
data_module: Optional[ReAgentDataModule],
num_epochs: int,
reader_options: ReaderOptions,
resource_options: ResourceOptions,
checkpoint_path: Optional[str] = None,
) -> Tuple[RLTrainingOutput, pl.Trainer]:
"""
Train the model
Returns partially filled RLTrainingOutput.
The field that should not be filled are:
- output_path
Arguments:
train/eval/test_dataset: what you'd expect
data_module: [pytorch lightning only] a lightning data module that replaces the use of train/eval datasets
num_epochs: number of training epochs
reader_options: options for the data reader
resource_options: options for training resources (currently only used for setting num_nodes in pytorch lightning trainer)
"""
if isinstance(trainer_module, MultiStageTrainer):
assert trainer_module.multi_stage_total_epochs == num_epochs, (
f"The sum of each stage's epoch ({trainer_module.trainer_epoch_mapping})"
f" should be equal to num_epochs ({num_epochs})."
)
reporter = self.get_reporter()
trainer_module.set_reporter(reporter)
assert data_module
lightning_trainer = train_eval_lightning(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
test_dataset=test_dataset,
trainer_module=trainer_module,
data_module=data_module,
num_epochs=num_epochs,
logger_name=str(type(self)),
reader_options=reader_options,
checkpoint_path=checkpoint_path,
resource_options=resource_options,
)
rank = get_rank()
if rank == 0:
# pyre-ignore
trainer_logger = lightning_trainer.logger
logger_data = trainer_logger.line_plot_aggregated
trainer_logger.clear_local_data()
if reporter is None:
training_report = None
else:
# pyre-ignore
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report()
)
return (
RLTrainingOutput(
training_report=training_report, logger_data=logger_data
),
lightning_trainer,
)
# Output from processes with non-0 rank is not used
return RLTrainingOutput(), lightning_trainer
