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gpu_monitor.py
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gpu_monitor.py
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#
# Copyright (c) 2019, Neptune Labs Sp. z o.o.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
from neptune.vendor.pynvml import (
NVMLError,
nvmlDeviceGetCount,
nvmlDeviceGetHandleByIndex,
nvmlDeviceGetMemoryInfo,
nvmlDeviceGetUtilizationRates,
nvmlInit,
)
_logger = logging.getLogger(__name__)
class GPUMonitor(object):
nvml_error_printed = False
def get_card_count(self):
return self.__nvml_get_or_else(nvmlDeviceGetCount, default=0)
def get_card_usage_percent(self, card_index):
# pylint: disable=no-member
# pylint incorrectly detects that function nvmlDeviceGetUtilizationRates returns str
return self.__nvml_get_or_else(
lambda: float(nvmlDeviceGetUtilizationRates(nvmlDeviceGetHandleByIndex(card_index)).gpu)
)
def get_card_used_memory_in_bytes(self, card_index):
# pylint: disable=no-member
# pylint incorrectly detects that function nvmlDeviceGetMemoryInfo returns str
return self.__nvml_get_or_else(
lambda: nvmlDeviceGetMemoryInfo(nvmlDeviceGetHandleByIndex(card_index)).used
)
def get_top_card_memory_in_bytes(self):
def read_top_card_memory_in_bytes():
# pylint: disable=no-member
# pylint incorrectly detects that function nvmlDeviceGetMemoryInfo returns str
return self.__nvml_get_or_else(
lambda: [
nvmlDeviceGetMemoryInfo(nvmlDeviceGetHandleByIndex(card_index)).total
for card_index in range(nvmlDeviceGetCount())
],
default=0,
)
memory_per_card = read_top_card_memory_in_bytes()
if not memory_per_card:
return 0
return max(memory_per_card)
def __nvml_get_or_else(self, getter, default=None):
try:
nvmlInit()
return getter()
except NVMLError as e:
if not GPUMonitor.nvml_error_printed:
warning = (
"Info (NVML): %s. GPU usage metrics may not be reported. For more information, "
"see https://docs-legacy.neptune.ai/logging-and-managing-experiment-results"
"/logging-experiment"
"-data.html#hardware-consumption "
)
_logger.warning(warning, e)
GPUMonitor.nvml_error_printed = True
return default