def test_cpu_model(tmpdir):
"""Make sure model trains on CPU."""
tutils.reset_seed()
trainer_options = dict(
default_save_path=tmpdir,
show_progress_bar=False,
logger=tutils.get_test_tube_logger(tmpdir),
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4
)
model, hparams = tutils.get_model()
tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_all_features_cpu_model(tmpdir):
"""Test each of the trainer options."""
tutils.reset_seed()
trainer_options = dict(default_save_path=tmpdir,
gradient_clip_val=1.0,
overfit_pct=0.20,
track_grad_norm=2,
show_progress_bar=False,
logger=tutils.get_test_tube_logger(tmpdir),
accumulate_grad_batches=2,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4)
model, hparams = tutils.get_model()
tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_multi_gpu_none_backend(tmpdir):
"""Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus='-1')
with pytest.warns(UserWarning):
tutils.run_model_test(trainer_options, model)
def test_single_gpu_model(tmpdir):
"""Make sure single GPU works (DP mode)."""
tutils.reset_seed()
if not torch.cuda.is_available():
warnings.warn('test_single_gpu_model cannot run.'
' Rerun on a GPU node to run this test')
return
model, hparams = tutils.get_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus=1)
tutils.run_model_test(trainer_options, model)
def test_multi_gpu_model_ddp(tmpdir):
"""Make sure DDP works."""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
tutils.run_model_test(trainer_options, model)
def test_cpu_model_with_amp(tmpdir):
"""Make sure model trains on CPU."""
tutils.reset_seed()
trainer_options = dict(
default_save_path=tmpdir,
show_progress_bar=False,
logger=tutils.get_test_tube_logger(tmpdir),
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4,
precision=16
)
model, hparams = tutils.get_model()
with pytest.raises((MisconfigurationException, ModuleNotFoundError)):
tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_amp_gpu_dp(tmpdir):
"""Make sure DP + AMP work."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_model()
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus='0, 1', # test init with gpu string
distributed_backend='dp',
precision=16)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
def test_default_logger_callbacks_cpu_model(tmpdir):
"""Test each of the trainer options."""
tutils.reset_seed()
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gradient_clip_val=1.0,
overfit_pct=0.20,
show_progress_bar=False,
train_percent_check=0.01,
val_percent_check=0.01,
)
model, hparams = tutils.get_model()
tutils.run_model_test_no_loggers(trainer_options, model)
# test freeze on cpu
model.freeze()
model.unfreeze()
def test_multi_gpu_model_dp(tmpdir):
"""Make sure DP works."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
distributed_backend='dp',
max_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus='-1')
tutils.run_model_test(trainer_options, model)
# test memory helper functions
memory.get_memory_profile('min_max')
def test_early_stopping_cpu_model(tmpdir):
"""Test each of the trainer options."""
tutils.reset_seed()
stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
trainer_options = dict(
default_save_path=tmpdir,
early_stop_callback=stopping,
gradient_clip_val=1.0,
overfit_pct=0.20,
track_grad_norm=2,
show_progress_bar=True,
logger=tutils.get_test_tube_logger(tmpdir),
train_percent_check=0.1,
val_percent_check=0.1,
)
model, hparams = tutils.get_model()
tutils.run_model_test(trainer_options, model, on_gpu=False)
# test freeze on cpu
model.freeze()
model.unfreeze()
def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
"""Make sure DDP works with dataloaders passed to fit()"""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
fit_options = dict(train_dataloader=model.train_dataloader(),
val_dataloaders=model.val_dataloader())
trainer = Trainer(**trainer_options)
result = trainer.fit(model, **fit_options)
assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
