71 lines
2.2 KiB
Python
71 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 numpy as np
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import pytest
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import XLAStatsMonitor
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from pytorch_lightning.loggers import CSVLogger
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from pytorch_lightning.loggers.csv_logs import ExperimentWriter
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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@RunIf(tpu=True)
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def test_xla_stats_monitor(tmpdir):
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"""Test XLA stats are logged using a logger."""
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model = BoringModel()
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xla_stats = XLAStatsMonitor()
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logger = CSVLogger(tmpdir)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=5,
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accelerator="tpu",
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devices=8,
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callbacks=[xla_stats],
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logger=logger,
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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path_csv = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE)
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met_data = np.genfromtxt(path_csv, delimiter=",", names=True, deletechars="", replace_space=" ")
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fields = ["avg. free memory (MB)", "avg. peak memory (MB)"]
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for f in fields:
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assert any(f in h for h in met_data.dtype.names)
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@RunIf(tpu=True)
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def test_xla_stats_monitor_no_logger(tmpdir):
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"""Test XLAStatsMonitor with no logger in Trainer."""
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model = BoringModel()
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xla_stats = XLAStatsMonitor()
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trainer = Trainer(
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default_root_dir=tmpdir, callbacks=[xla_stats], max_epochs=1, accelerator="tpu", devices=[1], logger=False
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)
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with pytest.raises(MisconfigurationException, match="Trainer that has no logger."):
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trainer.fit(model)
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