120 lines
3.8 KiB
Python
120 lines
3.8 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 os
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import pytest
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import torch
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import numpy as np
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import GPUStatsMonitor
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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.base import EvalModelTemplate
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_gpu_stats_monitor(tmpdir):
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"""
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Test GPU stats are logged using a logger.
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"""
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model = EvalModelTemplate()
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gpu_stats = GPUStatsMonitor(intra_step_time=True)
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logger = CSVLogger(tmpdir)
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log_every_n_steps = 2
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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=7,
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log_every_n_steps=log_every_n_steps,
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gpus=1,
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callbacks=[gpu_stats],
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logger=logger
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)
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results = trainer.fit(model)
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assert results
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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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batch_time_data = met_data['batch_time/intra_step (ms)']
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batch_time_data = batch_time_data[~np.isnan(batch_time_data)]
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assert batch_time_data.shape[0] == trainer.global_step // log_every_n_steps
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fields = [
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'utilization.gpu',
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'memory.used',
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'memory.free',
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'utilization.memory'
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]
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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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@pytest.mark.skipif(torch.cuda.is_available(), reason="test requires CPU machine")
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def test_gpu_stats_monitor_cpu_machine(tmpdir):
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"""
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Test GPUStatsMonitor on CPU machine.
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"""
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with pytest.raises(MisconfigurationException, match='NVIDIA driver is not installed'):
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gpu_stats = GPUStatsMonitor()
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_gpu_stats_monitor_no_logger(tmpdir):
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"""
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Test GPUStatsMonitor with no logger in Trainer.
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"""
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model = EvalModelTemplate()
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gpu_stats = GPUStatsMonitor()
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trainer = Trainer(
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default_root_dir=tmpdir,
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callbacks=[gpu_stats],
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max_epochs=1,
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gpus=1,
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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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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_gpu_stats_monitor_no_gpu_warning(tmpdir):
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"""
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Test GPUStatsMonitor raises a warning when not training on GPU device.
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"""
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model = EvalModelTemplate()
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gpu_stats = GPUStatsMonitor()
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trainer = Trainer(
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default_root_dir=tmpdir,
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callbacks=[gpu_stats],
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max_steps=1,
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gpus=None
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)
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with pytest.raises(MisconfigurationException, match='not running on GPU'):
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trainer.fit(model)
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def test_gpu_stats_monitor_parse_gpu_stats():
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logs = GPUStatsMonitor._parse_gpu_stats('1,2', [[3, 4, 5], [6, 7]], [('gpu', 'a'), ('memory', 'b')])
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expected = {'gpu_id: 1/gpu (a)': 3, 'gpu_id: 1/memory (b)': 4, 'gpu_id: 2/gpu (a)': 6, 'gpu_id: 2/memory (b)': 7}
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assert logs == expected
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