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How to use monitor_cpu_and_memory method in locust

Best Python code snippet using locust

runners.py

Source:runners.py

...267            stop_group.kill(block=True)268        logger.debug(269            "%g users have been stopped, %g still running", sum(user_classes_stop_count.values()), self.user_count270        )271    def monitor_cpu_and_memory(self) -> NoReturn:272        process = psutil.Process()273        while True:274            self.current_cpu_usage = process.cpu_percent()275            self.current_memory_usage = process.memory_info().rss276            if self.current_cpu_usage > CPU_WARNING_THRESHOLD:277                self.environment.events.cpu_warning.fire(environment=self.environment, cpu_usage=self.current_cpu_usage)278                if not self.cpu_warning_emitted:279                    logging.warning(280                        f"CPU usage above {CPU_WARNING_THRESHOLD}%! This may constrain your throughput and may even give inconsistent response time measurements! See https://docs.locust.io/en/stable/running-locust-distributed.html for how to distribute the load over multiple CPU cores or machines"281                    )282                    self.cpu_warning_emitted = True283            gevent.sleep(CPU_MONITOR_INTERVAL)284    @abstractmethod285    def start(self, user_count: int, spawn_rate: float, wait: bool = False) -> None:...

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