99 lines
2.8 KiB
Python
99 lines
2.8 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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import tests.base.develop_pipelines as tpipes
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import tests.base.develop_utils as tutils
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from pytorch_lightning.callbacks import EarlyStopping
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from pytorch_lightning.core import memory
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from tests.base import EvalModelTemplate
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import pytorch_lightning as pl
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PRETEND_N_OF_GPUS = 16
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_early_stop_dp(tmpdir):
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"""Make sure DDP works. with early stopping"""
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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callbacks=[EarlyStopping()],
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max_epochs=50,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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distributed_backend='dp',
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)
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model = EvalModelTemplate()
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tpipes.run_model_test(trainer_options, model)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_multi_gpu_model_dp(tmpdir):
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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distributed_backend='dp',
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progress_bar_refresh_rate=0
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)
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model = EvalModelTemplate()
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tpipes.run_model_test(trainer_options, model)
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# test memory helper functions
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memory.get_memory_profile('min_max')
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_dp_test(tmpdir):
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tutils.set_random_master_port()
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
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model = EvalModelTemplate()
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trainer = pl.Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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distributed_backend='dp',
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)
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trainer.fit(model)
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assert 'ckpt' in trainer.checkpoint_callback.best_model_path
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results = trainer.test()
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assert 'test_acc' in results[0]
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old_weights = model.c_d1.weight.clone().detach().cpu()
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results = trainer.test(model)
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assert 'test_acc' in results[0]
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# make sure weights didn't change
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new_weights = model.c_d1.weight.clone().detach().cpu()
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assert torch.all(torch.eq(old_weights, new_weights))
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