/usr/lib/python2.7/dist-packages/memory_profiler.py is in python-memory-profiler 0.31+git20141019-1.
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# .. we'll use this to pass it to the child script ..
_clean_globals = globals().copy()
__version__ = '0.32'
_CMD_USAGE = "python -m memory_profiler script_file.py"
import time
import sys
import os
import pdb
import warnings
import linecache
import inspect
import subprocess
from copy import copy
# TODO: provide alternative when multprocessing is not available
try:
from multiprocessing import Process, Pipe
except ImportError:
from multiprocessing.dummy import Process, Pipe
_TWO_20 = float(2 ** 20)
has_psutil = False
# .. get available packages ..
try:
import psutil
has_psutil = True
except ImportError:
pass
def _get_memory(pid, timestamps=False, include_children=False):
# .. only for current process and only on unix..
if pid == -1:
pid = os.getpid()
# .. cross-platform but but requires psutil ..
if has_psutil:
process = psutil.Process(pid)
try:
mem_info = getattr(process, 'memory_info', process.get_memory_info)
mem = mem_info()[0] / _TWO_20
if include_children:
for p in process.get_children(recursive=True):
mem_info = getattr(p, 'memory_info', p.get_memory_info)
mem += mem_info()[0] / _TWO_20
if timestamps:
return (mem, time.time())
else:
return mem
except psutil.AccessDenied:
pass
# continue and try to get this from ps
# .. scary stuff ..
if os.name == 'posix':
if include_children:
raise NotImplementedError('The psutil module is required when to'
' monitor memory usage of children'
' processes')
warnings.warn("psutil module not found. memory_profiler will be slow")
# ..
# .. memory usage in MiB ..
# .. this should work on both Mac and Linux ..
# .. subprocess.check_output appeared in 2.7, using Popen ..
# .. for backwards compatibility ..
out = subprocess.Popen(['ps', 'v', '-p', str(pid)],
stdout=subprocess.PIPE
).communicate()[0].split(b'\n')
try:
vsz_index = out[0].split().index(b'RSS')
mem = float(out[1].split()[vsz_index]) / 1024
if timestamps:
return(mem, time.time())
else:
return mem
except:
if timestamps:
return (-1, time.time())
else:
return -1
else:
raise NotImplementedError('The psutil module is required for non-unix '
'platforms')
class MemTimer(Process):
"""
Fetch memory consumption from over a time interval
"""
def __init__(self, monitor_pid, interval, pipe, max_usage=False,
*args, **kw):
self.monitor_pid = monitor_pid
self.interval = interval
self.pipe = pipe
self.cont = True
self.max_usage = max_usage
self.n_measurements = 1
if "timestamps" in kw:
self.timestamps = kw["timestamps"]
del kw["timestamps"]
else:
self.timestamps = False
if "include_children" in kw:
self.include_children = kw["include_children"]
del kw["include_children"]
else:
self.include_children = False
# get baseline memory usage
self.mem_usage = [
_get_memory(self.monitor_pid, timestamps=self.timestamps,
include_children=self.include_children)]
super(MemTimer, self).__init__(*args, **kw)
def run(self):
self.pipe.send(0) # we're ready
stop = False
while True:
cur_mem = _get_memory(self.monitor_pid, timestamps=self.timestamps,
include_children=self.include_children)
if not self.max_usage:
self.mem_usage.append(cur_mem)
else:
self.mem_usage[0] = max(cur_mem, self.mem_usage[0])
self.n_measurements += 1
if stop:
break
stop = self.pipe.poll(self.interval)
# do one more iteration
self.pipe.send(self.mem_usage)
self.pipe.send(self.n_measurements)
def memory_usage(proc=-1, interval=.1, timeout=None, timestamps=False,
include_children=False, max_usage=False, retval=False,
stream=None):
"""
Return the memory usage of a process or piece of code
Parameters
----------
proc : {int, string, tuple, subprocess.Popen}, optional
The process to monitor. Can be given by an integer/string
representing a PID, by a Popen object or by a tuple
representing a Python function. The tuple contains three
values (f, args, kw) and specifies to run the function
f(*args, **kw).
Set to -1 (default) for current process.
interval : float, optional
Interval at which measurements are collected.
timeout : float, optional
Maximum amount of time (in seconds) to wait before returning.
max_usage : bool, optional
Only return the maximum memory usage (default False)
retval : bool, optional
For profiling python functions. Save the return value of the profiled
function. Return value of memory_usage becomes a tuple:
(mem_usage, retval)
timestamps : bool, optional
if True, timestamps of memory usage measurement are collected as well.
stream : File
if stream is a File opened with write access, then results are written
to this file instead of stored in memory and returned at the end of
the subprocess. Useful for long-running processes.
Implies timestamps=True.
Returns
-------
mem_usage : list of floating-poing values
memory usage, in MiB. It's length is always < timeout / interval
if max_usage is given, returns the two elements maximum memory and
number of measurements effectuated
ret : return value of the profiled function
Only returned if retval is set to True
"""
if stream is not None:
timestamps = True
if not max_usage:
ret = []
else:
ret = -1
if timeout is not None:
max_iter = int(timeout / interval)
elif isinstance(proc, int):
# external process and no timeout
max_iter = 1
else:
# for a Python function wait until it finishes
max_iter = float('inf')
if hasattr(proc, '__call__'):
proc = (proc, (), {})
if isinstance(proc, (list, tuple)):
if len(proc) == 1:
f, args, kw = (proc[0], (), {})
elif len(proc) == 2:
f, args, kw = (proc[0], proc[1], {})
elif len(proc) == 3:
f, args, kw = (proc[0], proc[1], proc[2])
else:
raise ValueError
while True:
child_conn, parent_conn = Pipe() # this will store MemTimer's results
p = MemTimer(os.getpid(), interval, child_conn, timestamps=timestamps,
max_usage=max_usage, include_children=include_children)
p.start()
parent_conn.recv() # wait until we start getting memory
returned = f(*args, **kw)
parent_conn.send(0) # finish timing
ret = parent_conn.recv()
n_measurements = parent_conn.recv()
if retval:
ret = ret, returned
p.join(5 * interval)
if n_measurements > 4 or interval < 1e-6:
break
interval /= 10.
elif isinstance(proc, subprocess.Popen):
# external process, launched from Python
line_count = 0
while True:
if not max_usage:
mem_usage = _get_memory(proc.pid, timestamps=timestamps,
include_children=include_children)
if stream is not None:
stream.write("MEM {0:.6f} {1:.4f}\n".format(*mem_usage))
else:
ret.append(mem_usage)
else:
ret = max([ret,
_get_memory(proc.pid,
include_children=include_children)])
time.sleep(interval)
line_count += 1
# flush every 50 lines. Make 'tail -f' usable on profile file
if line_count > 50:
line_count = 0
if stream is not None:
stream.flush()
if timeout is not None:
max_iter -= 1
if max_iter == 0:
break
if proc.poll() is not None:
break
else:
# external process
if max_iter == -1:
max_iter = 1
counter = 0
while counter < max_iter:
counter += 1
if not max_usage:
mem_usage = _get_memory(proc, timestamps=timestamps,
include_children=include_children)
if stream is not None:
stream.write("MEM {0:.6f} {1:.4f}\n".format(*mem_usage))
else:
ret.append(mem_usage)
else:
ret = max([ret,
_get_memory(proc, include_children=include_children)
])
time.sleep(interval)
# Flush every 50 lines.
if counter % 50 == 0 and stream is not None:
stream.flush()
if stream:
return None
return ret
# ..
# .. utility functions for line-by-line ..
def _find_script(script_name):
""" Find the script.
If the input is not a file, then $PATH will be searched.
"""
if os.path.isfile(script_name):
return script_name
path = os.getenv('PATH', os.defpath).split(os.pathsep)
for folder in path:
if not folder:
continue
fn = os.path.join(folder, script_name)
if os.path.isfile(fn):
return fn
sys.stderr.write('Could not find script {0}\n'.format(script_name))
raise SystemExit(1)
class _TimeStamperCM(object):
"""Time-stamping context manager."""
def __init__(self, timestamps):
self._timestamps = timestamps
def __enter__(self):
self._timestamps.append(_get_memory(os.getpid(), timestamps=True))
def __exit__(self, *args):
self._timestamps.append(_get_memory(os.getpid(), timestamps=True))
class TimeStamper:
""" A profiler that just records start and end execution times for
any decorated function.
"""
def __init__(self):
self.functions = {}
def __call__(self, func=None, precision=None):
if func is not None:
if not hasattr(func, "__call__"):
raise ValueError("Value must be callable")
self.add_function(func)
f = self.wrap_function(func)
f.__module__ = func.__module__
f.__name__ = func.__name__
f.__doc__ = func.__doc__
f.__dict__.update(getattr(func, '__dict__', {}))
return f
else:
def inner_partial(f):
return self.__call__(f, precision=precision)
return inner_partial
def timestamp(self, name="<block>"):
"""Returns a context manager for timestamping a block of code."""
# Make a fake function
func = lambda x: x
func.__module__ = ""
func.__name__ = name
self.add_function(func)
timestamps = []
self.functions[func].append(timestamps)
# A new object is required each time, since there can be several
# nested context managers.
return _TimeStamperCM(timestamps)
def add_function(self, func):
if not func in self.functions:
self.functions[func] = []
def wrap_function(self, func):
""" Wrap a function to timestamp it.
"""
def f(*args, **kwds):
# Start time
timestamps = [_get_memory(os.getpid(), timestamps=True)]
self.functions[func].append(timestamps)
try:
result = func(*args, **kwds)
finally:
# end time
timestamps.append(_get_memory(os.getpid(), timestamps=True))
return result
return f
def show_results(self, stream=None):
if stream is None:
stream = sys.stdout
for func, timestamps in self.functions.items():
function_name = "%s.%s" % (func.__module__, func.__name__)
for ts in timestamps:
stream.write("FUNC %s %.4f %.4f %.4f %.4f\n" % (
(function_name,) + ts[0] + ts[1]))
class LineProfiler(object):
""" A profiler that records the amount of memory for each line """
def __init__(self, **kw):
self.code_map = {}
self.enable_count = 0
self.max_mem = kw.get('max_mem', None)
self.prevline = None
self.include_children = kw.get('include_children', False)
def __call__(self, func=None, precision=1):
if func is not None:
self.add_function(func)
f = self.wrap_function(func)
f.__module__ = func.__module__
f.__name__ = func.__name__
f.__doc__ = func.__doc__
f.__dict__.update(getattr(func, '__dict__', {}))
return f
else:
def inner_partial(f):
return self.__call__(f, precision=precision)
return inner_partial
def add_code(self, code, toplevel_code=None):
if code not in self.code_map:
self.code_map[code] = {}
for subcode in filter(inspect.iscode, code.co_consts):
self.add_code(subcode)
def add_function(self, func):
""" Record line profiling information for the given Python function.
"""
try:
# func_code does not exist in Python3
code = func.__code__
except AttributeError:
warnings.warn("Could not extract a code object for the object %r"
% func)
else:
self.add_code(code)
def wrap_function(self, func):
""" Wrap a function to profile it.
"""
def f(*args, **kwds):
self.enable_by_count()
try:
result = func(*args, **kwds)
finally:
self.disable_by_count()
return result
return f
def run(self, cmd):
""" Profile a single executable statement in the main namespace.
"""
# TODO: can this be removed ?
import __main__
main_dict = __main__.__dict__
return self.runctx(cmd, main_dict, main_dict)
def runctx(self, cmd, globals, locals):
""" Profile a single executable statement in the given namespaces.
"""
self.enable_by_count()
try:
exec(cmd, globals, locals)
finally:
self.disable_by_count()
return self
def enable_by_count(self):
""" Enable the profiler if it hasn't been enabled before.
"""
if self.enable_count == 0:
self.enable()
self.enable_count += 1
def disable_by_count(self):
""" Disable the profiler if the number of disable requests matches the
number of enable requests.
"""
if self.enable_count > 0:
self.enable_count -= 1
if self.enable_count == 0:
self.disable()
def trace_memory_usage(self, frame, event, arg):
"""Callback for sys.settrace"""
if (event in ('call', 'line', 'return')
and frame.f_code in self.code_map):
if event != 'call':
# "call" event just saves the lineno but not the memory
mem = _get_memory(-1, include_children=self.include_children)
# if there is already a measurement for that line get the max
old_mem = self.code_map[frame.f_code].get(self.prevline, 0)
self.code_map[frame.f_code][self.prevline] = max(mem, old_mem)
self.prevline = frame.f_lineno
if self._original_trace_function is not None:
(self._original_trace_function)(frame, event, arg)
return self.trace_memory_usage
def trace_max_mem(self, frame, event, arg):
# run into PDB as soon as memory is higher than MAX_MEM
if event in ('line', 'return') and frame.f_code in self.code_map:
c = _get_memory(-1)
if c >= self.max_mem:
t = ('Current memory {0:.2f} MiB exceeded the maximum'
''.format(c) + 'of {0:.2f} MiB\n'.format(self.max_mem))
sys.stdout.write(t)
sys.stdout.write('Stepping into the debugger \n')
frame.f_lineno -= 2
p = pdb.Pdb()
p.quitting = False
p.stopframe = frame
p.returnframe = None
p.stoplineno = frame.f_lineno - 3
p.botframe = None
return p.trace_dispatch
if self._original_trace_function is not None:
(self._original_trace_function)(frame, event, arg)
return self.trace_max_mem
def __enter__(self):
self.enable_by_count()
def __exit__(self, exc_type, exc_val, exc_tb):
self.disable_by_count()
def enable(self):
self._original_trace_function = sys.gettrace()
if self.max_mem is not None:
sys.settrace(self.trace_max_mem)
else:
sys.settrace(self.trace_memory_usage)
def disable(self):
sys.settrace(self._original_trace_function)
def show_results(prof, stream=None, precision=1):
if stream is None:
stream = sys.stdout
template = '{0:>6} {1:>12} {2:>12} {3:<}'
for code in prof.code_map:
lines = prof.code_map[code]
if not lines:
# .. measurements are empty ..
continue
filename = code.co_filename
if filename.endswith((".pyc", ".pyo")):
filename = filename[:-1]
stream.write('Filename: ' + filename + '\n\n')
if not os.path.exists(filename):
stream.write('ERROR: Could not find file ' + filename + '\n')
if any([filename.startswith(k) for k in
("ipython-input", "<ipython-input")]):
print("NOTE: %mprun can only be used on functions defined in "
"physical files, and not in the IPython environment.")
continue
all_lines = linecache.getlines(filename)
sub_lines = inspect.getblock(all_lines[code.co_firstlineno - 1:])
linenos = range(code.co_firstlineno,
code.co_firstlineno + len(sub_lines))
header = template.format('Line #', 'Mem usage', 'Increment',
'Line Contents')
stream.write(header + '\n')
stream.write('=' * len(header) + '\n')
mem_old = lines[min(lines.keys())]
float_format = '{0}.{1}f'.format(precision + 4, precision)
template_mem = '{0:' + float_format + '} MiB'
for line in linenos:
mem = ''
inc = ''
if line in lines:
mem = lines[line]
inc = mem - mem_old
mem_old = mem
mem = template_mem.format(mem)
inc = template_mem.format(inc)
stream.write(template.format(line, mem, inc, all_lines[line - 1]))
stream.write('\n\n')
# A lprun-style %mprun magic for IPython.
def magic_mprun(self, parameter_s=''):
""" Execute a statement under the line-by-line memory profiler from the
memory_profiler module.
Usage:
%mprun -f func1 -f func2 <statement>
The given statement (which doesn't require quote marks) is run via the
LineProfiler. Profiling is enabled for the functions specified by the -f
options. The statistics will be shown side-by-side with the code through
the pager once the statement has completed.
Options:
-f <function>: LineProfiler only profiles functions and methods it is told
to profile. This option tells the profiler about these functions. Multiple
-f options may be used. The argument may be any expression that gives
a Python function or method object. However, one must be careful to avoid
spaces that may confuse the option parser. Additionally, functions defined
in the interpreter at the In[] prompt or via %run currently cannot be
displayed. Write these functions out to a separate file and import them.
One or more -f options are required to get any useful results.
-T <filename>: dump the text-formatted statistics with the code
side-by-side out to a text file.
-r: return the LineProfiler object after it has completed profiling.
-c: If present, add the memory usage of any children process to the report.
"""
try:
from StringIO import StringIO
except ImportError: # Python 3.x
from io import StringIO
# Local imports to avoid hard dependency.
from distutils.version import LooseVersion
import IPython
ipython_version = LooseVersion(IPython.__version__)
if ipython_version < '0.11':
from IPython.genutils import page
from IPython.ipstruct import Struct
from IPython.ipapi import UsageError
else:
from IPython.core.page import page
from IPython.utils.ipstruct import Struct
from IPython.core.error import UsageError
# Escape quote markers.
opts_def = Struct(T=[''], f=[])
parameter_s = parameter_s.replace('"', r'\"').replace("'", r"\'")
opts, arg_str = self.parse_options(parameter_s, 'rf:T:c', list_all=True)
opts.merge(opts_def)
global_ns = self.shell.user_global_ns
local_ns = self.shell.user_ns
# Get the requested functions.
funcs = []
for name in opts.f:
try:
funcs.append(eval(name, global_ns, local_ns))
except Exception as e:
raise UsageError('Could not find function %r.\n%s: %s' % (name,
e.__class__.__name__, e))
include_children = 'c' in opts
profile = LineProfiler(include_children=include_children)
for func in funcs:
profile(func)
# Add the profiler to the builtins for @profile.
try:
import builtins
except ImportError: # Python 3x
import __builtin__ as builtins
if 'profile' in builtins.__dict__:
had_profile = True
old_profile = builtins.__dict__['profile']
else:
had_profile = False
old_profile = None
builtins.__dict__['profile'] = profile
try:
try:
profile.runctx(arg_str, global_ns, local_ns)
message = ''
except SystemExit:
message = "*** SystemExit exception caught in code being profiled."
except KeyboardInterrupt:
message = ("*** KeyboardInterrupt exception caught in code being "
"profiled.")
finally:
if had_profile:
builtins.__dict__['profile'] = old_profile
# Trap text output.
stdout_trap = StringIO()
show_results(profile, stdout_trap)
output = stdout_trap.getvalue()
output = output.rstrip()
if ipython_version < '0.11':
page(output, screen_lines=self.shell.rc.screen_length)
else:
page(output)
print(message,)
text_file = opts.T[0]
if text_file:
with open(text_file, 'w') as pfile:
pfile.write(output)
print('\n*** Profile printout saved to text file %s. %s' % (text_file,
message))
return_value = None
if 'r' in opts:
return_value = profile
return return_value
def _func_exec(stmt, ns):
# helper for magic_memit, just a function proxy for the exec
# statement
exec(stmt, ns)
# a timeit-style %memit magic for IPython
def magic_memit(self, line=''):
"""Measure memory usage of a Python statement
Usage, in line mode:
%memit [-r<R>t<T>i<I>] statement
Options:
-r<R>: repeat the loop iteration <R> times and take the best result.
Default: 1
-t<T>: timeout after <T> seconds. Default: None
-i<I>: Get time information at an interval of I times per second.
Defaults to 0.1 so that there is ten measurements per second.
-c: If present, add the memory usage of any children process to the report.
Examples
--------
::
In [1]: import numpy as np
In [2]: %memit np.zeros(1e7)
maximum of 1: 76.402344 MiB per loop
In [3]: %memit np.ones(1e6)
maximum of 1: 7.820312 MiB per loop
In [4]: %memit -r 10 np.empty(1e8)
maximum of 10: 0.101562 MiB per loop
"""
opts, stmt = self.parse_options(line, 'r:t:i:c', posix=False, strict=False)
repeat = int(getattr(opts, 'r', 1))
if repeat < 1:
repeat == 1
timeout = int(getattr(opts, 't', 0))
if timeout <= 0:
timeout = None
interval = float(getattr(opts, 'i', 0.1))
include_children = 'c' in opts
# I've noticed we get less noisier measurements if we run
# a garbage collection first
import gc
gc.collect()
mem_usage = 0
counter = 0
baseline = memory_usage()[0]
while counter < repeat:
counter += 1
tmp = memory_usage((_func_exec, (stmt, self.shell.user_ns)),
timeout=timeout, interval=interval, max_usage=True,
include_children=include_children)
mem_usage = max(mem_usage, tmp[0])
if mem_usage:
print('peak memory: %.02f MiB, increment: %.02f MiB' %
(mem_usage, mem_usage - baseline))
else:
print('ERROR: could not read memory usage, try with a lower interval '
'or more iterations')
def load_ipython_extension(ip):
"""This is called to load the module as an IPython extension."""
ip.define_magic('mprun', magic_mprun)
ip.define_magic('memit', magic_memit)
def profile(func=None, stream=None, precision=1):
"""
Decorator that will run the function and print a line-by-line profile
"""
if func is not None:
def wrapper(*args, **kwargs):
prof = LineProfiler()
val = prof(func)(*args, **kwargs)
show_results(prof, stream=stream, precision=precision)
return val
return wrapper
else:
def inner_wrapper(f):
return profile(f, stream=stream, precision=precision)
return inner_wrapper
if __name__ == '__main__':
from optparse import OptionParser
parser = OptionParser(usage=_CMD_USAGE, version=__version__)
parser.disable_interspersed_args()
parser.add_option(
"--pdb-mmem", dest="max_mem", metavar="MAXMEM",
type="float", action="store",
help="step into the debugger when memory exceeds MAXMEM")
parser.add_option(
'--precision', dest="precision", type="int",
action="store", default=3,
help="precision of memory output in number of significant digits")
parser.add_option("-o", dest="out_filename", type="str",
action="store", default=None,
help="path to a file where results will be written")
parser.add_option("--timestamp", dest="timestamp", default=False,
action="store_true",
help="""print timestamp instead of memory measurement for
decorated functions""")
if not sys.argv[1:]:
parser.print_help()
sys.exit(2)
(options, args) = parser.parse_args()
sys.argv[:] = args # Remove every memory_profiler arguments
if options.timestamp:
prof = TimeStamper()
else:
prof = LineProfiler(max_mem=options.max_mem)
__file__ = _find_script(args[0])
try:
if sys.version_info[0] < 3:
# we need to ovewrite the builtins to have profile
# globally defined (global variables is not enough
# for all cases, e.g. a script that imports another
# script where @profile is used)
import __builtin__
__builtin__.__dict__['profile'] = prof
ns = copy(_clean_globals)
ns['profile'] = prof # shadow the profile decorator defined above
execfile(__file__, ns, ns)
else:
import builtins
builtins.__dict__['profile'] = prof
ns = copy(_clean_globals)
ns['profile'] = prof # shadow the profile decorator defined above
exec(compile(open(__file__).read(), __file__, 'exec'), ns, ns)
finally:
if options.out_filename is not None:
out_file = open(options.out_filename, "a")
else:
out_file = sys.stdout
if options.timestamp:
prof.show_results(stream=out_file)
else:
show_results(prof, precision=options.precision, stream=out_file)
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