def run_single(
rank: int,
config_json: str,
world_size: int,
dist_init_method: str,
summary_writer: SummaryWriter,
):
config = config_from_json(PyTextConfig, config_json)
if rank != 0:
summary_writer = None
train_model(config, dist_init_method, rank, rank, world_size,
summary_writer)
def train(context):
"""Train a model and save the best snapshot."""
config = parse_config(context.obj.load_config())
print("\n===Starting training...")
if config.distributed_world_size == 1:
train_model(config)
else:
train_model_distributed(config)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
config.task.data_handler.test_path,
)
def train(context):
"""Train a model and save the best snapshot."""
config_json = context.obj.load_config()
config = parse_config(Mode.TRAIN, config_json)
print("\n===Starting training...")
if config.distributed_world_size == 1:
train_model(config)
else:
train_model_distributed(config)
print("\n=== Starting testing...")
test_config = TestConfig(
load_snapshot_path=config.save_snapshot_path,
test_path=config.task.data_handler.test_path,
use_cuda_if_available=config.use_cuda_if_available,
)
test_model(test_config)
def run_single(
rank: int,
config_json: str,
world_size: int,
dist_init_method: str,
metadata: CommonMetadata,
):
config = config_from_json(PyTextConfig, config_json)
summary_writer = SummaryWriter(
) if rank != 0 and config.use_tensorboard else None
try:
train_model(config, dist_init_method, rank, rank, world_size,
summary_writer, metadata)
finally:
if summary_writer is not None:
summary_writer.close()
def run_single(
rank: int,
config_json: str,
world_size: int,
dist_init_method: Optional[str],
metadata: Optional[CommonMetadata],
metric_channels: Optional[List[Channel]],
):
config = config_from_json(PyTextConfig, config_json)
if rank != 0:
metric_channels = []
train_model(
config=config,
dist_init_url=dist_init_method,
device_id=rank,
rank=rank,
world_size=world_size,
metric_channels=metric_channels,
metadata=metadata,
)
def train(context):
"""Train a model and save the best snapshot."""
config = context.obj.load_config()
print("\n===Starting training...")
summary_writer = SummaryWriter() if config.use_tensorboard else None
try:
if config.distributed_world_size == 1:
train_model(config, summary_writer=summary_writer)
else:
train_model_distributed(config, summary_writer)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
config.task.data_handler.test_path,
summary_writer,
)
finally:
if summary_writer is not None:
summary_writer.close()
def train(context):
"""Train a model and save the best snapshot."""
config = context.obj.load_config()
print("\n===Starting training...")
metric_channels = []
if config.use_tensorboard:
metric_channels.append(TensorBoardChannel())
try:
if config.distributed_world_size == 1:
train_model(config, metric_channels=metric_channels)
else:
train_model_distributed(config, metric_channels)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
test_path=None,
metric_channels=metric_channels,
)
finally:
for mc in metric_channels:
mc.close()
def run_single(rank, config_json: str, world_size: int, dist_init_method: str):
config = config_from_json(PyTextConfig, config_json)
train_model(config, dist_init_method, rank, rank, world_size)
