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default test logger (#1478)

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* default test logger

* fix tests

* spawn

* try

* simplify tests

* simplify tests

* formatting

* loggers

* loggers

* revert to TestTube

* default

* default

* wraps

* world size

* optim imports
This commit is contained in:
Jirka Borovec
2020-04-21 20:33:10 -04:00
committed by GitHub
parent bafdeca42f
commit c1c6e3b6c9
26 changed files with 136 additions and 264 deletions
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tests/models/test_gpu.py
+21 -69
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@@ -7,39 +7,17 @@ import tests.base.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.core import memory
from pytorch_lightning.trainer.distrib_parts import (
parse_gpu_ids,
determine_root_gpu_device,
)
from pytorch_lightning.trainer.distrib_parts import parse_gpu_ids, determine_root_gpu_device
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.base import LightningTestModel
PRETEND_N_OF_GPUS = 16
@pytest.mark.spawn
@pytest.mark.parametrize("backend", ['dp', 'ddp', 'ddp2'])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp2(tmpdir):
"""Make sure DDP2 works."""
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_default_model()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=2,
weights_summary=None,
distributed_backend='ddp2'
)
tutils.run_model_test(trainer_options, model)
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp(tmpdir):
def test_multi_gpu_model(tmpdir, backend):
"""Make sure DDP works."""
tutils.reset_seed()
@@ -48,15 +26,20 @@ def test_multi_gpu_model_ddp(tmpdir):
model, hparams = tutils.get_default_model()
trainer_options = dict(
default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp'
distributed_backend=backend,
)
tutils.run_model_test(trainer_options, model)
# tutils.run_model_test(trainer_options, model)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result
# test memory helper functions
memory.get_memory_profile('min_max')
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@@ -91,7 +74,7 @@ def test_cpu_slurm_save_load(tmpdir):
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
logger = tutils.get_default_logger(tmpdir)
version = logger.version
trainer_options = dict(
@@ -106,7 +89,7 @@ def test_cpu_slurm_save_load(tmpdir):
real_global_step = trainer.global_step
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
assert result == 1, 'cpu model failed to complete'
# predict with trained model before saving
# make a prediction
@@ -130,7 +113,7 @@ def test_cpu_slurm_save_load(tmpdir):
assert os.path.exists(saved_filepath)
# new logger file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False, version=version)
logger = tutils.get_default_logger(tmpdir, version=version)
trainer_options = dict(
max_epochs=1,
@@ -175,28 +158,6 @@ def test_multi_gpu_none_backend(tmpdir):
tutils.run_model_test(trainer_options, model)
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_dp(tmpdir):
"""Make sure DP works."""
tutils.reset_seed()
model, hparams = tutils.get_default_model()
trainer_options = dict(
default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
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')
@pytest.fixture
def mocked_device_count(monkeypatch):
def device_count():
@@ -249,21 +210,18 @@ def test_root_gpu_property(mocked_device_count, gpus, expected_root_gpu, distrib
@pytest.mark.gpus_param_tests
@pytest.mark.parametrize([
'gpus', 'expected_root_gpu', "distributed_backend"], [
@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
pytest.param(None, None, None, id="None is None"),
pytest.param(None, None, "ddp", id="None is None"),
pytest.param(0, None, "ddp", id="None is None"),
])
def test_root_gpu_property_0_passing(
mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
def test_root_gpu_property_0_passing(mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
# Asking for a gpu when non are available will result in a MisconfigurationException
@pytest.mark.gpus_param_tests
@pytest.mark.parametrize([
'gpus', 'expected_root_gpu', "distributed_backend"], [
@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
pytest.param(1, None, "ddp"),
pytest.param(3, None, "ddp"),
pytest.param(3, None, "ddp"),
@@ -272,8 +230,7 @@ def test_root_gpu_property_0_passing(
pytest.param(-1, None, "ddp"),
pytest.param('-1', None, "ddp")
])
def test_root_gpu_property_0_raising(
mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
def test_root_gpu_property_0_raising(mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
with pytest.raises(MisconfigurationException):
Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu
@@ -325,11 +282,10 @@ def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, gpus):
@pytest.mark.gpus_param_tests
@pytest.mark.parametrize("gpus", [''])
def test_parse_gpu_fail_on_empty_string(mocked_device_count, gpus):
def test_parse_gpu_fail_on_empty_string(mocked_device_count):
# This currently results in a ValueError instead of MisconfigurationException
with pytest.raises(ValueError):
parse_gpu_ids(gpus)
parse_gpu_ids('')
@pytest.mark.gpus_param_tests
@@ -350,7 +306,3 @@ def test_parse_gpu_fail_on_non_existant_id_2(mocked_device_count):
def test_parse_gpu_returns_None_when_no_devices_are_available(mocked_device_count_0, gpus):
with pytest.raises(MisconfigurationException):
parse_gpu_ids(gpus)
# if __name__ == '__main__':
# pytest.main([__file__])