def test_horovod_transfer_batch_to_gpu(tmpdir):
class TestTrainingStepModel(BoringModel):
def training_step(self, batch, *args, **kwargs):
assert str(batch.device) != 'cpu'
return super(TestTrainingStepModel,
self).training_step(batch, *args, **kwargs)
def validation_step(self, batch, *args, **kwargs):
assert str(batch.device) != 'cpu'
return super(TestTrainingStepModel,
self).validation_step(batch, *args, **kwargs)
model = TestTrainingStepModel()
trainer_options = dict(
default_root_dir=str(tmpdir),
progress_bar_refresh_rate=0,
max_epochs=1,
limit_train_batches=0.4,
limit_val_batches=0.2,
gpus=1,
deterministic=True,
accelerator='horovod',
)
tpipes.run_model_test_without_loggers(trainer_options, model)
def test_default_logger_callbacks_cpu_model(tmpdir):
"""Test each of the trainer options."""
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
gradient_clip_val=1.0,
overfit_batches=0.20,
enable_progress_bar=False,
limit_train_batches=0.01,
limit_val_batches=0.01,
)
model = BoringModel()
tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
# test freeze on cpu
model.freeze()
model.unfreeze()
def test_lbfgs_cpu_model(tmpdir):
"""Test each of the trainer options. Testing LBFGS optimizer"""
class ModelSpecifiedOptimizer(BoringModel):
def __init__(self, optimizer_name, learning_rate):
super().__init__()
self.optimizer_name = optimizer_name
self.learning_rate = learning_rate
self.save_hyperparameters()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
progress_bar_refresh_rate=0,
weights_summary="top",
limit_train_batches=0.2,
limit_val_batches=0.2,
)
model = ModelSpecifiedOptimizer(optimizer_name="LBFGS",
learning_rate=0.004)
tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
def test_horovod_transfer_batch_to_gpu(tmpdir):
class TestTrainingStepModel(BoringModel):
def training_step(self, batch, *args, **kwargs):
assert str(batch.device) != "cpu"
return super().training_step(batch, *args, **kwargs)
def validation_step(self, batch, *args, **kwargs):
assert str(batch.device) != "cpu"
return super().validation_step(batch, *args, **kwargs)
model = TestTrainingStepModel()
trainer_options = dict(
default_root_dir=str(tmpdir),
enable_progress_bar=False,
max_epochs=1,
limit_train_batches=0.4,
limit_val_batches=0.2,
gpus=1,
strategy="horovod",
)
tpipes.run_model_test_without_loggers(trainer_options, model)
