clearml-server/apiserver/bll/statistics/resource_monitor.py

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from datetime import datetime
import operator
from threading import Thread, Lock
from time import sleep
import attr
import psutil
from utilities.threads_manager import ThreadsManager
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class ResourceMonitor(Thread):
@attr.s(auto_attribs=True)
class Sample:
cpu_usage: float = 0.0
mem_used_gb: float = 0
mem_free_gb: float = 0
@classmethod
def _apply(cls, op, *samples):
return cls(
**{
field: op(*(getattr(sample, field) for sample in samples))
for field in attr.fields_dict(cls)
}
)
def min(self, sample):
return self._apply(min, self, sample)
def max(self, sample):
return self._apply(max, self, sample)
def avg(self, sample, count):
res = self._apply(lambda x: x * count, self)
res = self._apply(operator.add, res, sample)
res = self._apply(lambda x: x / (count + 1), res)
return res
def __init__(self, sample_interval_sec=5):
super(ResourceMonitor, self).__init__(daemon=True)
self.sample_interval_sec = sample_interval_sec
self._lock = Lock()
self._clear()
def _clear(self):
sample = self._get_sample()
self._avg = sample
self._min = sample
self._max = sample
self._clear_time = datetime.utcnow()
self._count = 1
@classmethod
def _get_sample(cls) -> Sample:
return cls.Sample(
cpu_usage=psutil.cpu_percent(),
mem_used_gb=psutil.virtual_memory().used / (1024 ** 3),
mem_free_gb=psutil.virtual_memory().free / (1024 ** 3),
)
def run(self):
while not ThreadsManager.terminating:
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sleep(self.sample_interval_sec)
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sample = self._get_sample()
with self._lock:
self._min = self._min.min(sample)
self._max = self._max.max(sample)
self._avg = self._avg.avg(sample, self._count)
self._count += 1
def get_stats(self) -> dict:
""" Returns current resource statistics and clears internal resource statistics """
with self._lock:
min_ = attr.asdict(self._min)
max_ = attr.asdict(self._max)
avg = attr.asdict(self._avg)
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interval = datetime.utcnow() - self._clear_time
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self._clear()
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return {
"interval_sec": interval.total_seconds(),
"num_cores": psutil.cpu_count(),
**{
k: {"min": v, "max": max_[k], "avg": avg[k]}
for k, v in min_.items()
}
}