def test_horovod_transfer_batch_to_gpu(tmpdir):
class TestTrainingStepModel(EvalModelTemplate):
def training_step(self, batch, *args, **kwargs):
x, y = batch
assert str(x.device) != 'cpu'
assert str(y.device) != 'cpu'
return super(TestTrainingStepModel, self).training_step(batch, *args, **kwargs)
def validation_step(self, batch, *args, **kwargs):
x, y = batch
assert str(x.device) != 'cpu'
assert str(y.device) != 'cpu'
return super(TestTrainingStepModel, self).validation_step(batch, *args, **kwargs)
hparams = EvalModelTemplate.get_default_hparams()
model = TestTrainingStepModel(**hparams)
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,
distributed_backend='horovod'
)
tpipes.run_model_test_without_loggers(trainer_options, model)
def test_lbfgs_cpu_model(tmpdir):
"""Test each of the trainer options."""
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,
)
hparams = EvalModelTemplate.get_default_hparams()
hparams.update(optimizer_name='lbfgs', learning_rate=0.004)
model = EvalModelTemplate(**hparams)
model.configure_optimizers = model.configure_optimizers__lbfgs
tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.25)
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,
progress_bar_refresh_rate=0,
limit_train_batches=0.01,
limit_val_batches=0.01,
)
model = EvalModelTemplate()
tpipes.run_model_test_without_loggers(trainer_options, model)
# test freeze on cpu
model.freeze()
model.unfreeze()
