124 lines
4.8 KiB
Python
Executable File
124 lines
4.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# type: ignore
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'''
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System tools like top/htop can only show current cpu usage values, so I write this script to do statistics jobs.
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Features:
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Use psutil library to sample cpu usage(avergage for all cores) of openpilot processes, at a rate of 5 samples/sec.
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Do cpu usage statistics periodically, 5 seconds as a cycle.
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Caculate the average cpu usage within this cycle.
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Caculate minumium/maximium/accumulated_average cpu usage as long term inspections.
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Monitor multiple processes simuteneously.
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Sample usage:
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root@localhost:/data/openpilot$ python selfdrive/debug/cpu_usage_stat.py boardd,ubloxd
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('Add monitored proc:', './boardd')
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('Add monitored proc:', 'python locationd/ubloxd.py')
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boardd: 1.96%, min: 1.96%, max: 1.96%, acc: 1.96%
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ubloxd.py: 0.39%, min: 0.39%, max: 0.39%, acc: 0.39%
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'''
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import psutil
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import time
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import os
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import sys
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import numpy as np
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import argparse
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import re
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from collections import defaultdict
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from selfdrive.manager.process_config import managed_processes
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# Do statistics every 5 seconds
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PRINT_INTERVAL = 5
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SLEEP_INTERVAL = 0.2
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monitored_proc_names = [
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# android procs
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'SurfaceFlinger', 'sensors.qcom'
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] + list(managed_processes.keys())
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cpu_time_names = ['user', 'system', 'children_user', 'children_system']
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timer = getattr(time, 'monotonic', time.time)
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def get_arg_parser():
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("proc_names", nargs="?", default='',
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help="Process names to be monitored, comma separated")
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parser.add_argument("--list_all", action='store_true',
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help="Show all running processes' cmdline")
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parser.add_argument("--detailed_times", action='store_true',
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help="show cpu time details (split by user, system, child user, child system)")
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return parser
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if __name__ == "__main__":
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args = get_arg_parser().parse_args(sys.argv[1:])
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if args.list_all:
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for p in psutil.process_iter():
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print('cmdline', p.cmdline(), 'name', p.name())
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sys.exit(0)
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if len(args.proc_names) > 0:
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monitored_proc_names = args.proc_names.split(',')
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monitored_procs = []
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stats = {}
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for p in psutil.process_iter():
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if p == psutil.Process():
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continue
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matched = any(l for l in p.cmdline() if any(pn for pn in monitored_proc_names if re.match(r'.*{}.*'.format(pn), l, re.M | re.I)))
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if matched:
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k = ' '.join(p.cmdline())
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print('Add monitored proc:', k)
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stats[k] = {'cpu_samples': defaultdict(list), 'min': defaultdict(lambda: None), 'max': defaultdict(lambda: None),
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'avg': defaultdict(lambda: 0.0), 'last_cpu_times': None, 'last_sys_time': None}
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stats[k]['last_sys_time'] = timer()
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stats[k]['last_cpu_times'] = p.cpu_times()
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monitored_procs.append(p)
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i = 0
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interval_int = int(PRINT_INTERVAL / SLEEP_INTERVAL)
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while True:
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for p in monitored_procs:
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k = ' '.join(p.cmdline())
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cur_sys_time = timer()
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cur_cpu_times = p.cpu_times()
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cpu_times = np.subtract(cur_cpu_times, stats[k]['last_cpu_times']) / (cur_sys_time - stats[k]['last_sys_time'])
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stats[k]['last_sys_time'] = cur_sys_time
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stats[k]['last_cpu_times'] = cur_cpu_times
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cpu_percent = 0
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for num, name in enumerate(cpu_time_names):
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stats[k]['cpu_samples'][name].append(cpu_times[num])
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cpu_percent += cpu_times[num]
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stats[k]['cpu_samples']['total'].append(cpu_percent)
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time.sleep(SLEEP_INTERVAL)
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i += 1
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if i % interval_int == 0:
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l = []
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for k, stat in stats.items():
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if len(stat['cpu_samples']) <= 0:
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continue
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for name, samples in stat['cpu_samples'].items():
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samples = np.array(samples)
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avg = samples.mean()
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c = samples.size
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min_cpu = np.amin(samples)
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max_cpu = np.amax(samples)
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if stat['min'][name] is None or min_cpu < stat['min'][name]:
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stat['min'][name] = min_cpu
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if stat['max'][name] is None or max_cpu > stat['max'][name]:
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stat['max'][name] = max_cpu
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stat['avg'][name] = (stat['avg'][name] * (i - c) + avg * c) / (i)
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stat['cpu_samples'][name] = []
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msg = f"avg: {stat['avg']['total']:.2%}, min: {stat['min']['total']:.2%}, max: {stat['max']['total']:.2%} {os.path.basename(k)}"
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if args.detailed_times:
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for stat_type in ['avg', 'min', 'max']:
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msg += f"\n {stat_type}: {[(name + ':' + str(round(stat[stat_type][name] * 100, 2))) for name in cpu_time_names]}"
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l.append((os.path.basename(k), stat['avg']['total'], msg))
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l.sort(key=lambda x: -x[1])
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for x in l:
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print(x[2])
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print('avg sum: {:.2%} over {} samples {} seconds\n'.format(
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sum(stat['avg']['total'] for k, stat in stats.items()), i, i * SLEEP_INTERVAL
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))
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