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added test for no dist sampler

This commit is contained in:
William Falcon 2019-07-24 17:10:14 -04:00
parent 164751c918
commit d1d33e8db6
1 changed files with 181 additions and 181 deletions
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362
tests/test_models.py
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@ -48,187 +48,187 @@ def test_ddp_sampler_error():
with pytest.raises(MisconfigurationException):
trainer.get_dataloaders(model)
def test_cpu_model():
"""
Make sure model trains on CPU
:return:
"""
trainer_options = dict(
progress_bar=False,
experiment=get_exp(),
max_nb_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4
)
model, hparams = get_model()
run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
def test_all_features_cpu_model():
"""
Test each of the trainer options
:return:
"""
trainer_options = dict(
gradient_clip=1.0,
overfit_pct=0.20,
track_grad_norm=2,
print_nan_grads=True,
progress_bar=False,
experiment=get_exp(),
max_nb_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4
)
model, hparams = get_model()
run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
def test_early_stopping_cpu_model():
"""
Test each of the trainer options
:return:
"""
stopping = EarlyStopping()
trainer_options = dict(
early_stop_callback=stopping,
gradient_clip=1.0,
overfit_pct=0.20,
track_grad_norm=2,
print_nan_grads=True,
progress_bar=False,
experiment=get_exp(),
max_nb_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4
)
model, hparams = get_model()
run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
def test_single_gpu_model():
"""
Make sure single GPU works (DP mode)
:return:
"""
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 = get_model()
trainer_options = dict(
progress_bar=False,
max_nb_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus=[0]
)
run_gpu_model_test(trainer_options, model, hparams)
def test_multi_gpu_model_dp():
"""
Make sure DP works
:return:
"""
if not torch.cuda.is_available():
warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a GPU node to run this test')
return
if not torch.cuda.device_count() > 1:
warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
return
model, hparams = get_model()
trainer_options = dict(
progress_bar=False,
max_nb_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus=[0, 1]
)
run_gpu_model_test(trainer_options, model, hparams)
def test_amp_gpu_dp():
"""
Make sure DP + AMP work
:return:
"""
if not torch.cuda.is_available():
warnings.warn('test_amp_gpu_dp cannot run. Rerun on a GPU node to run this test')
return
if not torch.cuda.device_count() > 1:
warnings.warn('test_amp_gpu_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
return
model, hparams = get_model()
trainer_options = dict(
max_nb_epochs=1,
gpus='0, 1', # test init with gpu string
distributed_backend='dp',
use_amp=True
)
with pytest.raises(MisconfigurationException):
run_gpu_model_test(trainer_options, model, hparams)
def test_multi_gpu_model_ddp():
"""
Make sure DDP works
:return:
"""
if not torch.cuda.is_available():
warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a GPU node to run this test')
return
if not torch.cuda.device_count() > 1:
warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
return
os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
model, hparams = get_model()
trainer_options = dict(
progress_bar=False,
max_nb_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus=[0, 1],
distributed_backend='ddp'
)
run_gpu_model_test(trainer_options, model, hparams)
def test_amp_gpu_ddp():
"""
Make sure DDP + AMP work
:return:
"""
if not torch.cuda.is_available():
warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
return
if not torch.cuda.device_count() > 1:
warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
return
os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
model, hparams = get_model()
trainer_options = dict(
progress_bar=True,
max_nb_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
use_amp=True
)
run_gpu_model_test(trainer_options, model, hparams)
#
# def test_cpu_model():
# """
# Make sure model trains on CPU
# :return:
# """
#
# trainer_options = dict(
# progress_bar=False,
# experiment=get_exp(),
# max_nb_epochs=1,
# train_percent_check=0.4,
# val_percent_check=0.4
# )
#
# model, hparams = get_model()
# run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
#
#
# def test_all_features_cpu_model():
# """
# Test each of the trainer options
# :return:
# """
#
# trainer_options = dict(
# gradient_clip=1.0,
# overfit_pct=0.20,
# track_grad_norm=2,
# print_nan_grads=True,
# progress_bar=False,
# experiment=get_exp(),
# max_nb_epochs=1,
# train_percent_check=0.4,
# val_percent_check=0.4
# )
#
# model, hparams = get_model()
# run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
#
#
# def test_early_stopping_cpu_model():
# """
# Test each of the trainer options
# :return:
# """
#
# stopping = EarlyStopping()
# trainer_options = dict(
# early_stop_callback=stopping,
# gradient_clip=1.0,
# overfit_pct=0.20,
# track_grad_norm=2,
# print_nan_grads=True,
# progress_bar=False,
# experiment=get_exp(),
# max_nb_epochs=1,
# train_percent_check=0.4,
# val_percent_check=0.4
# )
#
# model, hparams = get_model()
# run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
#
#
# def test_single_gpu_model():
# """
# Make sure single GPU works (DP mode)
# :return:
# """
# 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 = get_model()
#
# trainer_options = dict(
# progress_bar=False,
# max_nb_epochs=1,
# train_percent_check=0.1,
# val_percent_check=0.1,
# gpus=[0]
# )
#
# run_gpu_model_test(trainer_options, model, hparams)
#
#
# def test_multi_gpu_model_dp():
# """
# Make sure DP works
# :return:
# """
# if not torch.cuda.is_available():
# warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a GPU node to run this test')
# return
# if not torch.cuda.device_count() > 1:
# warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
# return
# model, hparams = get_model()
# trainer_options = dict(
# progress_bar=False,
# max_nb_epochs=1,
# train_percent_check=0.1,
# val_percent_check=0.1,
# gpus=[0, 1]
# )
#
# run_gpu_model_test(trainer_options, model, hparams)
#
#
# def test_amp_gpu_dp():
# """
# Make sure DP + AMP work
# :return:
# """
# if not torch.cuda.is_available():
# warnings.warn('test_amp_gpu_dp cannot run. Rerun on a GPU node to run this test')
# return
# if not torch.cuda.device_count() > 1:
# warnings.warn('test_amp_gpu_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
# return
# model, hparams = get_model()
# trainer_options = dict(
# max_nb_epochs=1,
# gpus='0, 1', # test init with gpu string
# distributed_backend='dp',
# use_amp=True
# )
# with pytest.raises(MisconfigurationException):
# run_gpu_model_test(trainer_options, model, hparams)
#
#
# def test_multi_gpu_model_ddp():
# """
# Make sure DDP works
# :return:
# """
# if not torch.cuda.is_available():
# warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a GPU node to run this test')
# return
# if not torch.cuda.device_count() > 1:
# warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
# return
#
# os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
# model, hparams = get_model()
# trainer_options = dict(
# progress_bar=False,
# max_nb_epochs=1,
# train_percent_check=0.1,
# val_percent_check=0.1,
# gpus=[0, 1],
# distributed_backend='ddp'
# )
#
# run_gpu_model_test(trainer_options, model, hparams)
#
#
# def test_amp_gpu_ddp():
# """
# Make sure DDP + AMP work
# :return:
# """
# if not torch.cuda.is_available():
# warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
# return
# if not torch.cuda.device_count() > 1:
# warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
# return
#
# os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
#
# model, hparams = get_model()
# trainer_options = dict(
# progress_bar=True,
# max_nb_epochs=1,
# gpus=[0, 1],
# distributed_backend='ddp',
# use_amp=True
# )
#
# run_gpu_model_test(trainer_options, model, hparams)
# ------------------------------------------------------------------------