/usr/lib/python3/dist-packages/memory_profiler.py is in python3-memory-profiler 0.52-1.
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1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 | """Profile the memory usage of a Python program"""
# .. we'll use this to pass it to the child script ..
_CLEAN_GLOBALS = globals().copy()
__version__ = '0.52.0'
_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
import logging
import traceback
if sys.platform == "win32":
# any value except signal.CTRL_C_EVENT and signal.CTRL_BREAK_EVENT
# can be used to kill a process unconditionally in Windows
SIGKILL = -1
else:
from signal import SIGKILL
import psutil
# TODO: provide alternative when multiprocessing is not available
try:
from multiprocessing import Process, Pipe
except ImportError:
from multiprocessing.dummy import Process, Pipe
try:
from IPython.core.magic import Magics, line_cell_magic, magics_class
except ImportError:
# ipython_version < '0.13'
Magics = object
line_cell_magic = lambda func: func
magics_class = lambda cls: cls
PY2 = sys.version_info[0] == 2
_TWO_20 = float(2 ** 20)
if PY2:
import __builtin__ as builtins
else:
import builtins
def unicode(x, *args):
return str(x)
# .. get available packages ..
try:
import tracemalloc
has_tracemalloc = True
except ImportError:
has_tracemalloc = False
class MemitResult(object):
"""memit magic run details.
Object based on IPython's TimeitResult
"""
def __init__(self, mem_usage, baseline, repeat, timeout, interval,
include_children):
self.mem_usage = mem_usage
self.baseline = baseline
self.repeat = repeat
self.timeout = timeout
self.interval = interval
self.include_children = include_children
def __str__(self):
max_mem = max(self.mem_usage)
inc = max_mem - self.baseline
return 'peak memory: %.02f MiB, increment: %.02f MiB' % (max_mem, inc)
def _repr_pretty_(self, p, cycle):
msg = str(self)
p.text(u'<MemitResult : ' + msg + u'>')
def _get_child_memory(process, meminfo_attr=None):
"""
Returns a generator that yields memory for all child processes.
"""
# Convert a pid to a process
if isinstance(process, int):
if process == -1: process = os.getpid()
process = psutil.Process(process)
if not meminfo_attr:
# Use the psutil 2.0 attr if the older version isn't passed in.
meminfo_attr = 'memory_info' if hasattr(process, 'memory_info') else 'get_memory_info'
# Select the psutil function get the children similar to how we selected
# the memory_info attr (a change from excepting the AttributeError).
children_attr = 'children' if hasattr(process, 'children') else 'get_children'
# Loop over the child processes and yield their memory
try:
for child in getattr(process, children_attr)(recursive=True):
yield getattr(child, meminfo_attr)()[0] / _TWO_20
except (psutil.NoSuchProcess, psutil.AccessDenied):
# https://github.com/fabianp/memory_profiler/issues/71
yield 0.0
def _get_memory(pid, backend, timestamps=False, include_children=False, filename=None):
# .. low function to get memory consumption ..
if pid == -1:
pid = os.getpid()
def tracemalloc_tool():
# .. cross-platform but but requires Python 3.4 or higher ..
stat = next(filter(lambda item: str(item).startswith(filename),
tracemalloc.take_snapshot().statistics('filename')))
mem = stat.size / _TWO_20
if timestamps:
return mem, time.time()
else:
return mem
def ps_util_tool():
# .. cross-platform but but requires psutil ..
process = psutil.Process(pid)
try:
# avoid using get_memory_info since it does not exists
# in psutil > 2.0 and accessing it will cause exception.
meminfo_attr = 'memory_info' if hasattr(process, 'memory_info') \
else 'get_memory_info'
mem = getattr(process, meminfo_attr)()[0] / _TWO_20
if include_children:
mem += sum(_get_child_memory(process, meminfo_attr))
if timestamps:
return mem, time.time()
else:
return mem
except psutil.AccessDenied:
pass
# continue and try to get this from ps
def posix_tool():
# .. scary stuff ..
if include_children:
raise NotImplementedError((
"The psutil module is required to monitor the "
"memory usage of child 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
if backend == 'tracemalloc' and \
(filename is None or filename == '<unknown>'):
raise RuntimeError(
'There is no access to source file of the profiled function'
)
tools = {'tracemalloc': tracemalloc_tool,
'psutil': ps_util_tool,
'posix': posix_tool}
return tools[backend]()
class MemTimer(Process):
"""
Fetch memory consumption from over a time interval
"""
def __init__(self, monitor_pid, interval, pipe, backend, max_usage=False,
*args, **kw):
self.monitor_pid = monitor_pid
self.interval = interval
self.pipe = pipe
self.cont = True
self.backend = backend
self.max_usage = max_usage
self.n_measurements = 1
self.timestamps = kw.pop("timestamps", False)
self.include_children = kw.pop("include_children", False)
# get baseline memory usage
self.mem_usage = [
_get_memory(self.monitor_pid, self.backend, 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, self.backend, 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, multiprocess=False, max_usage=False,
retval=False, stream=None, backend=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.
include_children : bool, optional
if True, sum the memory of all forked processes as well
multiprocess : bool, optional
if True, track the memory usage of all forked processes.
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-point 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
"""
backend = choose_backend(backend)
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 callable(proc):
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, backend,
timestamps=timestamps,
max_usage=max_usage,
include_children=include_children)
p.start()
parent_conn.recv() # wait until we start getting memory
# When there is an exception in the "proc" - the (spawned) monitoring processes don't get killed.
# Therefore, the whole process hangs indefinitely. Here, we are ensuring that the process gets killed!
try:
returned = f(*args, **kw)
parent_conn.send(0) # finish timing
ret = parent_conn.recv()
n_measurements = parent_conn.recv()
if retval:
ret = ret, returned
except Exception:
parent = psutil.Process(os.getpid())
for child in parent.children(recursive=True):
os.kill(child.pid, SIGKILL)
p.join(0)
raise
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, backend, timestamps=timestamps,
include_children=include_children)
if stream is not None:
stream.write("MEM {0:.6f} {1:.4f}\n".format(*mem_usage))
# Write children to the stream file
if multiprocess:
for idx, chldmem in enumerate(_get_child_memory(proc.pid)):
stream.write("CHLD {0} {1:.6f} {2:.4f}\n".format(idx, chldmem, time.time()))
else:
# Create a nested list with the child memory
if multiprocess:
mem_usage = [mem_usage]
for chldmem in _get_child_memory(proc.pid):
mem_usage.append(chldmem)
# Append the memory usage to the return value
ret.append(mem_usage)
else:
ret = max(ret,
_get_memory(
proc.pid, backend, 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, backend, timestamps=timestamps,
include_children=include_children)
if stream is not None:
stream.write("MEM {0:.6f} {1:.4f}\n".format(*mem_usage))
# Write children to the stream file
if multiprocess:
for idx, chldmem in enumerate(_get_child_memory(proc.pid)):
stream.write("CHLD {0} {1:.6f} {2:.4f}\n".format(idx, chldmem, time.time()))
else:
# Create a nested list with the child memory
if multiprocess:
mem_usage = [mem_usage]
for chldmem in _get_child_memory(proc.pid):
mem_usage.append(chldmem)
# Append the memory usage to the return value
ret.append(mem_usage)
else:
ret = max([ret,
_get_memory(proc, backend, 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, filename, backend):
self.timestamps = timestamps
self.filename = filename
self.backend = backend
def __enter__(self):
self.timestamps.append(
_get_memory(os.getpid(), self.backend, timestamps=True, filename=self.filename))
def __exit__(self, *args):
self.timestamps.append(
_get_memory(os.getpid(), self.backend, timestamps=True, filename=self.filename))
class TimeStamper:
""" A profiler that just records start and end execution times for
any decorated function.
"""
def __init__(self, backend):
self.functions = {}
self.backend = backend
def __call__(self, func=None, precision=None):
if func is not None:
if not callable(func):
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.
try:
filename = inspect.getsourcefile(func)
except TypeError:
filename = '<unknown>'
return _TimeStamperCM(timestamps, filename, self.backend)
def add_function(self, func):
if func not in self.functions:
self.functions[func] = []
def wrap_function(self, func):
""" Wrap a function to timestamp it.
"""
def f(*args, **kwds):
# Start time
try:
filename = inspect.getsourcefile(func)
except TypeError:
filename = '<unknown>'
timestamps = [
_get_memory(os.getpid(), self.backend, timestamps=True, filename=filename)]
self.functions[func].append(timestamps)
try:
return func(*args, **kwds)
finally:
# end time
timestamps.append(_get_memory(os.getpid(), self.backend, timestamps=True,
filename=filename))
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 CodeMap(dict):
def __init__(self, include_children, backend):
self.include_children = include_children
self._toplevel = []
self.backend = backend
def add(self, code, toplevel_code=None):
if code in self:
return
if toplevel_code is None:
filename = code.co_filename
if filename.endswith((".pyc", ".pyo")):
filename = filename[:-1]
if not os.path.exists(filename):
print('ERROR: Could not find file ' + filename)
if filename.startswith(("ipython-input", "<ipython-input")):
print(
"NOTE: %mprun can only be used on functions defined in"
" physical files, and not in the IPython environment.")
return
toplevel_code = code
(sub_lines, start_line) = inspect.getsourcelines(code)
linenos = range(start_line,
start_line + len(sub_lines))
self._toplevel.append((filename, code, linenos))
self[code] = {}
else:
self[code] = self[toplevel_code]
for subcode in filter(inspect.iscode, code.co_consts):
self.add(subcode, toplevel_code=toplevel_code)
def trace(self, code, lineno, prev_lineno):
memory = _get_memory(-1, self.backend, include_children=self.include_children,
filename=code.co_filename)
prev_value = self[code].get(lineno, None)
previous_memory = prev_value[1] if prev_value else 0
previous_inc = prev_value[0] if prev_value else 0
prev_line_value = self[code].get(prev_lineno, None) if prev_lineno else None
prev_line_memory = prev_line_value[1] if prev_line_value else 0
#inc = (memory-prev_line_memory)
#print('trace lineno=%(lineno)s prev_lineno=%(prev_lineno)s mem=%(memory)s prev_inc=%(previous_inc)s inc=%(inc)s' % locals())
self[code][lineno] = (previous_inc + (memory-prev_line_memory), max(memory, previous_memory))
def items(self):
"""Iterate on the toplevel code blocks."""
for (filename, code, linenos) in self._toplevel:
measures = self[code]
if not measures:
continue # skip if no measurement
line_iterator = ((line, measures.get(line)) for line in linenos)
yield (filename, line_iterator)
class LineProfiler(object):
""" A profiler that records the amount of memory for each line """
def __init__(self, **kw):
include_children = kw.get('include_children', False)
backend = kw.get('backend', 'psutil')
self.code_map = CodeMap(
include_children=include_children, backend=backend)
self.enable_count = 0
self.max_mem = kw.get('max_mem', None)
self.prevlines = []
self.backend = choose_backend(kw.get('backend', None))
self.prev_lineno = None
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_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.code_map.add(code)
def wrap_function(self, func):
""" Wrap a function to profile it.
"""
def f(*args, **kwds):
self.enable_by_count()
try:
return func(*args, **kwds)
finally:
self.disable_by_count()
return f
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 frame.f_code in self.code_map:
if event == 'call':
# "call" event just saves the lineno but not the memory
self.prevlines.append(frame.f_lineno)
elif event == 'line':
# trace needs current line and previous line
self.code_map.trace(frame.f_code, self.prevlines[-1], self.prev_lineno)
# saving previous line
self.prev_lineno = self.prevlines[-1]
self.prevlines[-1] = frame.f_lineno
elif event == 'return':
lineno = self.prevlines.pop()
self.code_map.trace(frame.f_code, lineno, self.prev_lineno)
self.prev_lineno = 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, self.backend, filename=frame.f_code.co_filename)
if c >= self.max_mem:
t = ('Current memory {0:.2f} MiB exceeded the '
'maximum of {1:.2f} MiB\n'.format(c, 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 (filename, lines) in prof.code_map.items():
header = template.format('Line #', 'Mem usage', 'Increment',
'Line Contents')
stream.write(u'Filename: ' + filename + '\n\n')
stream.write(header + u'\n')
stream.write(u'=' * len(header) + '\n')
all_lines = linecache.getlines(filename)
float_format = u'{0}.{1}f'.format(precision + 4, precision)
template_mem = u'{0:' + float_format + '} MiB'
for (lineno, mem) in lines:
if mem:
inc = mem[0]
mem = mem[1]
mem = template_mem.format(mem)
inc = template_mem.format(inc)
else:
mem = u''
inc = u''
tmp = template.format(lineno, mem, inc, all_lines[lineno - 1])
stream.write(unicode(tmp, 'UTF-8'))
stream.write(u'\n\n')
def _func_exec(stmt, ns):
# helper for magic_memit, just a function proxy for the exec
# statement
exec(stmt, ns)
@magics_class
class MemoryProfilerMagics(Magics):
# A lprun-style %mprun magic for IPython.
@line_cell_magic
def mprun(self, parameter_s='', cell=None):
""" Execute a statement under the line-by-line memory profiler from the
memory_profiler module.
Usage, in line mode:
%mprun -f func1 -f func2 <statement>
Usage, in cell mode:
%%mprun -f func1 -f func2 [statement]
code...
code...
In cell mode, the additional code lines are appended to the (possibly
empty) statement in the first line. Cell mode allows you to easily
profile multiline blocks without having to put them in a separate
function.
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.
"""
from io import StringIO
from memory_profiler import show_results, LineProfiler
# 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
if cell is not None:
arg_str += '\n' + cell
# 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.
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:
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
# a timeit-style %memit magic for IPython
@line_cell_magic
def memit(self, line='', cell=None):
"""Measure memory usage of a Python statement
Usage, in line mode:
%memit [-r<R>t<T>i<I>] statement
Usage, in cell mode:
%%memit [-r<R>t<T>i<I>] setup_code
code...
code...
This function can be used both as a line and cell magic:
- In line mode you can measure a single-line statement (though multiple
ones can be chained with using semicolons).
- In cell mode, the statement in the first line is used as setup code
(executed but not measured) and the body of the cell is measured.
The cell body has access to any variables created in the setup code.
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.
-o: If present, return a object containing memit run details
-q: If present, be quiet and do not output a result.
Examples
--------
::
In [1]: %memit range(10000)
peak memory: 21.42 MiB, increment: 0.41 MiB
In [2]: %memit range(1000000)
peak memory: 52.10 MiB, increment: 31.08 MiB
In [3]: %%memit l=range(1000000)
...: len(l)
...:
peak memory: 52.14 MiB, increment: 0.08 MiB
"""
from memory_profiler import memory_usage, _func_exec
opts, stmt = self.parse_options(line, 'r:t:i:coq', posix=False,
strict=False)
if cell is None:
setup = 'pass'
else:
setup = stmt
stmt = cell
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
return_result = 'o' in opts
quiet = 'q' in opts
# I've noticed we get less noisier measurements if we run
# a garbage collection first
import gc
gc.collect()
_func_exec(setup, self.shell.user_ns)
mem_usage = []
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.append(tmp[0])
result = MemitResult(mem_usage, baseline, repeat, timeout, interval,
include_children)
if not quiet:
if mem_usage:
print(result)
else:
print('ERROR: could not read memory usage, try with a '
'lower interval or more iterations')
if return_result:
return result
@classmethod
def register_magics(cls, ip):
from distutils.version import LooseVersion
import IPython
ipython_version = LooseVersion(IPython.__version__)
if ipython_version < '0.13':
try:
_register_magic = ip.define_magic
except AttributeError: # ipython 0.10
_register_magic = ip.expose_magic
_register_magic('mprun', cls.mprun.__func__)
_register_magic('memit', cls.memit.__func__)
else:
ip.register_magics(cls)
# commenting out due to failures with some versions of IPython
# see https://github.com/fabianp/memory_profiler/issues/106
# # Ensuring old interface of magics expose for IPython 0.10
# magic_mprun = MemoryProfilerMagics().mprun.__func__
# magic_memit = MemoryProfilerMagics().memit.__func__
def load_ipython_extension(ip):
"""This is called to load the module as an IPython extension."""
MemoryProfilerMagics.register_magics(ip)
def profile(func=None, stream=None, precision=1, backend='psutil'):
"""
Decorator that will run the function and print a line-by-line profile
"""
backend = choose_backend(backend)
if backend == 'tracemalloc' and has_tracemalloc:
if not tracemalloc.is_tracing():
tracemalloc.start()
if func is not None:
def wrapper(*args, **kwargs):
prof = LineProfiler(backend=backend)
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,
backend=backend)
return inner_wrapper
def choose_backend(new_backend=None):
"""
Function that tries to setup backend, chosen by user, and if failed,
setup one of the allowable backends
"""
_backend = 'no_backend'
all_backends = [
('psutil', True),
('posix', os.name == 'posix'),
('tracemalloc', has_tracemalloc),
]
backends_indices = dict((b[0], i) for i, b in enumerate(all_backends))
if new_backend is not None:
all_backends.insert(0, all_backends.pop(backends_indices[new_backend]))
for n_backend, is_available in all_backends:
if is_available:
_backend = n_backend
break
if _backend != new_backend and new_backend is not None:
warnings.warn('{0} can not be used, {1} used instead'.format(
new_backend, _backend))
return _backend
# Insert in the built-ins 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)
def exec_with_profiler(filename, profiler, backend, passed_args=[]):
from runpy import run_module
builtins.__dict__['profile'] = profiler
ns = dict(_CLEAN_GLOBALS, profile=profiler)
_backend = choose_backend(backend)
sys.argv = [filename] + passed_args
if PY2:
execfile(filename, ns, ns)
else:
if _backend == 'tracemalloc' and has_tracemalloc:
tracemalloc.start()
try:
with open(filename) as f:
exec(compile(f.read(), filename, 'exec'), ns, ns)
finally:
if has_tracemalloc and tracemalloc.is_tracing():
tracemalloc.stop()
def run_module_with_profiler(module, profiler, backend, passed_args=[]):
from runpy import run_module
builtins.__dict__['profile'] = profiler
ns = dict(_CLEAN_GLOBALS, profile=profiler)
_backend = choose_backend(backend)
sys.argv = [module] + passed_args
if PY2:
run_module(module, run_name="__main__", init_globals=ns)
else:
if _backend == 'tracemalloc' and has_tracemalloc:
tracemalloc.start()
try:
run_module(module, run_name="__main__", init_globals=ns)
finally:
if has_tracemalloc and tracemalloc.is_tracing():
tracemalloc.stop()
class LogFile(object):
"""File-like object to log text using the `logging` module and the log
report can be customised."""
def __init__(self, name=None, reportIncrementFlag=False):
"""
:param name: name of the logger module
reportIncrementFlag: This must be set to True if only the steps
with memory increments are to be reported
:type self: object
name: string
reportIncrementFlag: bool
"""
self.logger = logging.getLogger(name)
self.reportIncrementFlag = reportIncrementFlag
def write(self, msg, level=logging.INFO):
if self.reportIncrementFlag:
if "MiB" in msg and float(msg.split("MiB")[1].strip()) > 0:
self.logger.log(level, msg)
elif msg.__contains__("Filename:") or msg.__contains__(
"Line Contents"):
self.logger.log(level, msg)
else:
self.logger.log(level, msg)
def flush(self):
for handler in self.logger.handlers:
handler.flush()
if __name__ == '__main__':
from argparse import ArgumentParser, REMAINDER
parser = ArgumentParser(usage=_CMD_USAGE)
parser.add_argument('--version', action='version', version=__version__)
parser.add_argument(
'--pdb-mmem', dest='max_mem', metavar='MAXMEM',
type=float, action='store',
help='step into the debugger when memory exceeds MAXMEM')
parser.add_argument(
'--precision', dest='precision', type=int,
action='store', default=3,
help='precision of memory output in number of significant digits')
parser.add_argument('-o', dest='out_filename', type=str,
action='store', default=None,
help='path to a file where results will be written')
parser.add_argument('--timestamp', dest='timestamp', default=False,
action='store_true',
help='''print timestamp instead of memory measurement for
decorated functions''')
parser.add_argument('--backend', dest='backend', type=str, action='store',
choices=['tracemalloc', 'psutil', 'posix'], default='psutil',
help='backend using for getting memory info '
'(one of the {tracemalloc, psutil, posix})')
parser.add_argument("program", nargs=REMAINDER,
help='python script or module followed by command line arguements to run')
args = parser.parse_args()
if len(args.program) == 0:
print("A program to run must be provided. Use -h for help")
sys.exit(1)
target = args.program[0]
script_args = args.program[1:]
_backend = choose_backend(args.backend)
if args.timestamp:
prof = TimeStamper(_backend)
else:
prof = LineProfiler(max_mem=args.max_mem, backend=_backend)
try:
if args.program[0].endswith('.py'):
script_filename = _find_script(args.program[0])
exec_with_profiler(script_filename, prof, args.backend, script_args)
else:
run_module_with_profiler(target, prof, args.backend, script_args)
finally:
if args.out_filename is not None:
out_file = open(args.out_filename, "a")
else:
out_file = sys.stdout
if args.timestamp:
prof.show_results(stream=out_file)
else:
show_results(prof, precision=args.precision, stream=out_file)
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