Source code for openquake.baselib.performance

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4

# Copyright (C) 2015-2017 GEM Foundation

# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake.  If not, see <>.
import os
import time
from datetime import datetime
from multiprocessing.connection import Client

import numpy

from openquake.baselib.general import humansize
from openquake.baselib import hdf5

import psutil
if psutil.__version__ > '2.0.0':  # Ubuntu 14.10
    def virtual_memory():
        return psutil.virtual_memory()

    def memory_info(proc):
        return proc.memory_info()

elif psutil.__version__ >= '1.2.1':  # Ubuntu 14.04
    def virtual_memory():
        return psutil.virtual_memory()

    def memory_info(proc):
        return proc.get_memory_info()

else:  # Ubuntu 12.04
[docs] def virtual_memory(): return psutil.phymem_usage()
[docs] def memory_info(proc): return proc.get_memory_info()
perf_dt = numpy.dtype([('operation', (bytes, 50)), ('time_sec', float), ('memory_mb', float), ('counts', int)]) # this is not thread-safe
[docs]class Monitor(object): """ Measure the resident memory occupied by a list of processes during the execution of a block of code. Should be used as a context manager, as follows:: with Monitor('do_something') as mon: do_something() print mon.mem At the end of the block the Monitor object will have the following 5 public attributes: .start_time: when the monitor started (a datetime object) .duration: time elapsed between start and stop (in seconds) .exc: usually None; otherwise the exception happened in the `with` block .mem: the memory delta in bytes The behaviour of the Monitor can be customized by subclassing it and by overriding the method on_exit(), called at end and used to display or store the results of the analysis. NB: if the .address attribute is set, it is possible for the monitor to send commands to that address, assuming there is a :class:`multiprocessing.connection.Listener` listening. """ address = None authkey = None calc_id = None def __init__(self, operation='dummy', hdf5path=None, autoflush=False, measuremem=False): self.operation = operation self.hdf5path = hdf5path self.autoflush = autoflush self.measuremem = measuremem self.mem = 0 self.duration = 0 self._start_time = self._stop_time = time.time() self.children = [] self.counts = 0 self.address = None @property def dt(self): """Last time interval measured""" return self._stop_time - self._start_time
[docs] def measure_mem(self): """A memory measurement (in bytes)""" proc = psutil.Process(os.getpid()) try: return memory_info(proc).rss except psutil.AccessDenied: # no access to information about this process pass
@property def start_time(self): """ Datetime instance recording when the monitoring started """ return datetime.fromtimestamp(self._start_time)
[docs] def get_data(self): """ :returns: an array of dtype perf_dt, with the information of the monitor (operation, time_sec, memory_mb, counts); the lenght of the array can be 0 (for counts=0) or 1 (otherwise). """ data = [] if self.counts: time_sec = self.duration memory_mb = self.mem / 1024. / 1024. if self.measuremem else 0 data.append((self.operation, time_sec, memory_mb, self.counts)) return numpy.array(data, perf_dt)
def __enter__(self): self.exc = None # exception self._start_time = time.time() if self.measuremem: self.start_mem = self.measure_mem() return self def __exit__(self, etype, exc, tb): self.exc = exc if self.measuremem: self.stop_mem = self.measure_mem() self.mem += self.stop_mem - self.start_mem self._stop_time = time.time() self.duration += self._stop_time - self._start_time self.counts += 1 self.on_exit()
[docs] def on_exit(self): "To be overridden in subclasses" if self.autoflush: self.flush()
[docs] def send(self, *args): """ Send a command to the listener. Add the .calc_id as last argument. """ if self.address: client = Client(self.address, authkey=self.authkey) try: client.send(args + (self.calc_id,)) finally: client.close()
[docs] def flush(self): """ Save the measurements on the performance file (or on stdout) """ for child in self.children: child.flush() data = self.get_data() if len(data) == 0: # no information return [] elif self.hdf5path: hdf5.extend3(self.hdf5path, 'performance_data', data) # reset monitor self.duration = 0 self.mem = 0 self.counts = 0 return data
# TODO: rename this as spawn; see what will break def __call__(self, operation='no operation', **kw): """ Return a child of the monitor usable for a different operation. """ child =, **kw) self.children.append(child) return child
[docs] def new(self, operation='no operation', **kw): """ Return a copy of the monitor usable for a different operation. """ self_vars = vars(self).copy() del self_vars['operation'] del self_vars['children'] del self_vars['counts'] new = self.__class__(operation) vars(new).update(self_vars) vars(new).update(kw) return new
def __repr__(self): calc_id = ' #%s ' % self.calc_id if self.calc_id else ' ' msg = '%s%s%s' % (self.__class__.__name__, calc_id, self.operation) if self.measuremem: return '<%s, duration=%ss, memory=%s>' % ( msg, self.duration, humansize(self.mem)) elif self.duration: return '<%s, duration=%ss>' % (msg, self.duration) else: return '<%s>' % msg