102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
import os
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import pytest
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import torch
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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()
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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=1,
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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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with open(path_csv, 'r') as fp:
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lines = fp.readlines()
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header = lines[0].split()
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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 header])
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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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