def training_loop(self) -> None:
scaling_config_dataclass = self._validate_and_get_scaling_config_data_class(
self.scaling_config
)
train_loop_per_worker = construct_train_func(
self.train_loop_per_worker,
self.train_loop_config,
fn_arg_name="train_loop_per_worker",
)
additional_resources_per_worker = (
scaling_config_dataclass.additional_resources_per_worker
)
backend_executor = BackendExecutor(
backend_config=self.backend_config,
num_workers=scaling_config_dataclass.num_workers,
num_cpus_per_worker=scaling_config_dataclass.num_cpus_per_worker,
num_gpus_per_worker=scaling_config_dataclass.num_gpus_per_worker,
additional_resources_per_worker=additional_resources_per_worker,
max_retries=0,
)
checkpoint_manager = self._checkpoint_manager_cls()
checkpoint_manager.on_init(preprocessor=self.preprocessor)
# Start the remote actors.
backend_executor.start(initialization_hook=None)
if self.resume_from_checkpoint:
resume_checkpoint_dict = self.resume_from_checkpoint.to_dict()
else:
resume_checkpoint_dict = None
dataset_spec = _RayDatasetSpec(
dataset_or_dict=self.datasets, dataset_split_fn=_default_dataset_split_fn
)
# TODO(amog): Have TrainingIterator also accept a checkpoint ObjectRef instead
# of just a Dict.
training_iterator = TrainingIterator(
backend_executor=backend_executor,
backend_config=self.backend_config,
train_func=train_loop_per_worker,
dataset_spec=dataset_spec,
checkpoint_manager=checkpoint_manager,
checkpoint=resume_checkpoint_dict,
checkpoint_strategy=None,
)
for results in training_iterator:
# TODO(ml-team): add ability to report results from multiple workers.
first_worker_results = results[0]
tune.report(**first_worker_results)
# Shutdown workers.
backend_executor.shutdown()
class TestConfig(BackendConfig):
@property
def backend_cls(self):
return TestBackend
class TestBackend(Backend):
def on_start(self, worker_group: WorkerGroup, backend_config: TestConfig):
pass
def on_shutdown(self, worker_group: WorkerGroup, backend_config: TestConfig):
pass
EMPTY_RAY_DATASET_SPEC = _RayDatasetSpec(dataset_or_dict=None)
def test_start(ray_start_2_cpus):
config = TestConfig()
e = BackendExecutor(config, num_workers=2)
with pytest.raises(InactiveWorkerGroupError):
e.start_training(lambda: 1, dataset_spec=EMPTY_RAY_DATASET_SPEC)
e.start()
assert len(e.worker_group) == 2
def test_initialization_hook(ray_start_2_cpus):
config = TestConfig()
e = BackendExecutor(config, num_workers=2)
def run_iterator(
self,
train_func: Union[Callable[[], T], Callable[[Dict[str, Any]], T]],
config: Optional[Dict[str, Any]] = None,
dataset: Optional[Union[RayDataset, Dict[str, RayDataset]]] = None,
checkpoint: Optional[Union[Dict, str, Path]] = None,
checkpoint_strategy: Optional[CheckpointStrategy] = None,
) -> "TrainingIterator":
"""Same as ``run`` except returns an iterator over the results.
This is useful if you want to have more customization of what to do
with the intermediate results or how to use the ``Trainer`` with Ray
Tune.
.. code-block:: python
def train_func(config):
...
for _ in config["epochs"]:
metrics = train()
metrics = validate(...)
ray.train.report(**metrics)
return model
iterator = trainer.run_iterator(train_func, config=config)
for result in iterator:
do_stuff(result)
latest_ckpt = trainer.get_latest_checkpoint()
assert iterator.is_finished()
model = iterator.get_fin()[0]
Args:
train_func (Callable): The training function to execute.
This can either take in no arguments or a ``config`` dict.
config (Optional[Dict]): Configurations to pass into
``train_func``. If None then an empty Dict will be created.
checkpoint (Optional[Dict|Path|str]): The checkpoint data that
should be loaded onto each worker and accessed by the
training function via ``train.load_checkpoint()``. If this is a
``str`` or ``Path`` then the value is expected to be a path
to a file that contains a serialized checkpoint dict. If this
is ``None`` then no checkpoint will be loaded.
checkpoint_strategy (Optional[CheckpointStrategy]): The
configurations for saving checkpoints.
Returns:
An Iterator over the intermediate results from ``train.report()``.
"""
# Create new log directory for this run.
self._run_id += 1
self.create_run_dir()
train_func = construct_train_func(train_func, config)
dataset_spec = _RayDatasetSpec(dataset_or_dict=dataset)
return TrainingIterator(
backend_executor=self._backend_executor,
backend_config=self._backend_config,
train_func=train_func,
run_dir=self.latest_run_dir,
dataset_spec=dataset_spec,
checkpoint_manager=self.checkpoint_manager,
checkpoint=checkpoint,
checkpoint_strategy=checkpoint_strategy,
)
def run(
self,
train_func: Union[Callable[[], T], Callable[[Dict[str, Any]], T]],
config: Optional[Dict[str, Any]] = None,
callbacks: Optional[List[TrainingCallback]] = None,
dataset: Optional[Union[RayDataset, Dict[str, RayDataset]]] = None,
checkpoint: Optional[Union[Dict, str, Path]] = None,
checkpoint_strategy: Optional[CheckpointStrategy] = None,
) -> List[T]:
"""Runs a training function in a distributed manner.
Args:
train_func (Callable): The training function to execute.
This can either take in no arguments or a ``config`` dict.
config (Optional[Dict]): Configurations to pass into
``train_func``. If None then an empty Dict will be created.
callbacks (Optional[List[TrainingCallback]]): A list of Callbacks
which will be executed during training. If this is not set,
currently there are NO default Callbacks.
dataset (Optional[Union[RayDataset, Dict[str, RayDataset]]]):
Distributed Ray :ref:`Dataset <dataset-api>` or
:ref:`DatasetPipeline <dataset-pipeline-api>` to pass into the
workers, which can be accessed from the training function via
``train.get_dataset_shard()``. Sharding will automatically be
handled by the Trainer. Multiple Datasets can be passed in as
a ``Dict`` that maps each name key to a Dataset value,
and each Dataset can be accessed from the training function
by passing in a `dataset_name` argument to
``train.get_dataset_shard()``.
checkpoint (Optional[Dict|str|Path]): The checkpoint data that
should be loaded onto each worker and accessed by the training
function via ``train.load_checkpoint()``. If this is a ``str``
or ``Path`` then the value is expected to be a path to a file
that contains a serialized checkpoint dict. If this is
``None`` then no checkpoint will be loaded.
checkpoint_strategy (Optional[CheckpointStrategy]): The
configurations for saving checkpoints.
Returns:
A list of results from the training function. Each value in the
list corresponds to the output of the training function from
each worker.
"""
# Create new log directory for this run.
self._run_id += 1
self.create_run_dir()
# TODO(matt): Set default callbacks.
callbacks = [] if callbacks is None else callbacks
finished_with_errors = False
for callback in callbacks:
callback.start_training(logdir=str(self.latest_run_dir),
config=config or {})
train_func = construct_train_func(train_func, config)
dataset_spec = _RayDatasetSpec(dataset_or_dict=dataset)
try:
iterator = TrainingIterator(
backend_executor=self._backend_executor,
backend_config=self._backend_config,
train_func=train_func,
dataset_spec=dataset_spec,
checkpoint_manager=self.checkpoint_manager,
checkpoint=checkpoint,
checkpoint_strategy=checkpoint_strategy,
run_dir=self.latest_run_dir,
)
for intermediate_result in iterator:
for callback in callbacks:
callback.process_results(intermediate_result)
assert iterator.is_finished()
return iterator.get_final_results()
finally:
for callback in callbacks:
callback.finish_training(error=finished_with_errors)