77 lines
2.2 KiB
Python
77 lines
2.2 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 os
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import torch
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from pytorch_lightning import Trainer
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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def test_model_torch_save(tmpdir):
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"""Test to ensure torch save does not fail for model and trainer."""
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model = BoringModel()
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num_epochs = 1
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=num_epochs,
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)
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temp_path = os.path.join(tmpdir, 'temp.pt')
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trainer.fit(model)
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# Ensure these do not fail
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torch.save(trainer.model, temp_path)
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torch.save(trainer, temp_path)
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trainer = torch.load(temp_path)
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@RunIf(skip_windows=True)
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def test_model_torch_save_ddp_cpu(tmpdir):
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"""Test to ensure torch save does not fail for model and trainer using cpu ddp."""
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model = BoringModel()
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num_epochs = 1
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=num_epochs,
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accelerator="ddp_cpu",
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num_processes=2,
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logger=False,
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)
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temp_path = os.path.join(tmpdir, 'temp.pt')
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trainer.fit(model)
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# Ensure these do not fail
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torch.save(trainer.model, temp_path)
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torch.save(trainer, temp_path)
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@RunIf(min_gpus=2)
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def test_model_torch_save_ddp_cuda(tmpdir):
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"""Test to ensure torch save does not fail for model and trainer using gpu ddp."""
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model = BoringModel()
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num_epochs = 1
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=num_epochs,
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accelerator="ddp_spawn",
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gpus=2,
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)
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temp_path = os.path.join(tmpdir, 'temp.pt')
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trainer.fit(model)
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# Ensure these do not fail
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torch.save(trainer.model, temp_path)
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torch.save(trainer, temp_path)
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