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test_ddp.py
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test_ddp.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from tests.accelerators import ddp_model, DDPLauncher
from tests.utilities.distributed import call_training_script
@pytest.mark.parametrize('cli_args', [
pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
# call the script
std, err = call_training_script(ddp_model, cli_args, 'fit', tmpdir, timeout=120)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
@pytest.mark.parametrize('cli_args', [
pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
# call the script
call_training_script(ddp_model, cli_args, 'test', tmpdir)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
@pytest.mark.parametrize('cli_args', [
pytest.param('--max_epochs 1 --gpus 2 --accelerator ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
# call the script
call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)
# load the results of the script
result_path = os.path.join(tmpdir, 'ddp.result')
result = torch.load(result_path)
# verify the file wrote the expected outputs
assert result['status'] == 'complete'
model_outs = result['result']
for out in model_outs:
assert out['test_acc'] > 0.90
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@DDPLauncher.run(
"--max_epochs [max_epochs] --gpus 2 --accelerator [accelerator]",
max_epochs=["1"],
accelerator=["ddp", "ddp_spawn"]
)
def test_cli_to_pass(tmpdir, args=None):
"""
This test verify we can call function using test_cli name
"""
return '1'