Python memory_profiler.memory_usage函数代码示例

def checkpoint_memory():
    '''This test is meant to be run manually, since it depends on
    memory_profiler and its behavior may vary.'''
    try:
        from memory_profiler import memory_usage
    except ImportError:
        return

    def f(a):
        for _ in range(10):
            a = np.sin(a**2 + 1)
        return a
    checkpointed_f = checkpoint(f)

    def testfun(f, x):
        for _ in range(5):
            x = f(x)
        return np.sum(x)
    gradfun = grad(testfun, 1)

    A = npr.RandomState(0).randn(100000)
    max_usage              = max(memory_usage((gradfun, (f,              A))))
    max_checkpointed_usage = max(memory_usage((gradfun, (checkpointed_f, A))))

    assert max_checkpointed_usage < max_usage / 2.

def trace_calls_and_returns(frame, event, arg):
    
    global just_returned
    co = frame.f_code
    func_name = co.co_name

    if just_returned:
        just_returned = False
        print 'just_returned'
        print 'mem before gc ' + str(memory_usage(-1))
     #   gc.collect()
        print 'mem after gc ' + str(memory_usage(-1))
    
    if func_name == 'memory_usage':
        return
    if func_name == 'write':
        # Ignore write() calls from print statements
        return
    line_no = frame.f_lineno
    filename = co.co_filename
    if event == 'call':
        print 'Call to %s on line %s of %s' % (func_name, line_no, filename)
        return trace_calls_and_returns
    elif event == 'return':
        print 'return of %s => %s' % (func_name, arg)
        just_returned = True
        print 'mem before gc ' + str(memory_usage(-1))
       # gc.collect()
        print 'mem after gc ' + str(memory_usage(-1))
    elif event == 'line':
        print 'a line event ' + str(line_no)
    return

  def assertNotIncreasingMemory(self,
                                f,
                                num_iters=100000,
                                increase_threshold_absolute_mb=10):
    """Assert memory usage doesn't increase beyond given threshold for f."""

    with context.eager_mode():
      # Warm up.
      f()

      # Wait for background threads to start up and take over memory.
      # FIXME: The nature of this test leaves few other options. Maybe there
      # is a better way to do this.
      time.sleep(4)

      initial = memory_profiler.memory_usage(-1)[0]

      for _ in six.moves.range(num_iters):
        f()

      increase = memory_profiler.memory_usage(-1)[0] - initial

      assert increase < increase_threshold_absolute_mb, (
          "Increase is too high. Initial memory usage: %f MB. Increase: %f MB. "
          "Maximum allowed increase: %f") % (initial, increase,
                                             increase_threshold_absolute_mb)

def memory_profile(function, *args, **kwargs):
    gc.collect()
    baseline = memory_profiler.memory_usage()[0]
    max_usage = memory_profiler.memory_usage(
        (function, args, kwargs),
        max_usage=True,
    )
    return max_usage[0] - baseline

def run_tests_if_main(measure_mem=False):
    """Run tests in a given file if it is run as a script."""
    local_vars = inspect.currentframe().f_back.f_locals
    if not local_vars.get('__name__', '') == '__main__':
        return
    # we are in a "__main__"
    try:
        import faulthandler
        faulthandler.enable()
    except Exception:
        pass
    with warnings.catch_warnings(record=True):  # memory_usage internal dep.
        mem = int(round(max(memory_usage(-1)))) if measure_mem else -1
    if mem >= 0:
        print('Memory consumption after import: %s' % mem)
    t0 = time.time()
    peak_mem, peak_name = mem, 'import'
    max_elapsed, elapsed_name = 0, 'N/A'
    count = 0
    for name in sorted(list(local_vars.keys()), key=lambda x: x.lower()):
        val = local_vars[name]
        if name.startswith('_'):
            continue
        elif callable(val) and name.startswith('test'):
            count += 1
            doc = val.__doc__.strip() if val.__doc__ else name
            sys.stdout.write('%s ... ' % doc)
            sys.stdout.flush()
            try:
                t1 = time.time()
                if measure_mem:
                    with warnings.catch_warnings(record=True):  # dep warn
                        mem = int(round(max(memory_usage((val, (), {})))))
                else:
                    val()
                    mem = -1
                if mem >= peak_mem:
                    peak_mem, peak_name = mem, name
                mem = (', mem: %s MB' % mem) if mem >= 0 else ''
                elapsed = int(round(time.time() - t1))
                if elapsed >= max_elapsed:
                    max_elapsed, elapsed_name = elapsed, name
                sys.stdout.write('time: %0.3f sec%s\n' % (elapsed, mem))
                sys.stdout.flush()
            except Exception as err:
                if 'skiptest' in err.__class__.__name__.lower():
                    sys.stdout.write('SKIP (%s)\n' % str(err))
                    sys.stdout.flush()
                else:
                    raise
    elapsed = int(round(time.time() - t0))
    sys.stdout.write('Total: %s tests\n• %0.3f sec (%0.3f sec for %s)\n• '
                     'Peak memory %s MB (%s)\n'
                     % (count, elapsed, max_elapsed, elapsed_name, peak_mem,
                        peak_name))

def watch_memory():
    # bring in the global memory usage value from the previous iteration
    global previous_call_memory_usage, peak_memory_usage, keep_watching, \
           watching_memory, input_cells
    new_memory_usage = memory_profiler.memory_usage()[0]
    memory_delta = new_memory_usage - previous_call_memory_usage
    keep_watching = False
    peaked_memory_usage = max(0, peak_memory_usage - new_memory_usage)
    # calculate time delta using global t1 (from the pre-run event) and current
    # time
    time_delta_secs = time.time() - t1
    num_commands = len(input_cells) - 1
    cmd = "In [{}]".format(num_commands)
    # convert the results into a pretty string
    output_template = ("{cmd} used {memory_delta:0.4f} MiB RAM in "
                       "{time_delta:0.2f}s, peaked {peaked_memory_usage:0.2f} "
                       "MiB above current, total RAM usage "
                       "{memory_usage:0.2f} MiB")
    output = output_template.format(time_delta=time_delta_secs,
                                    cmd=cmd,
                                    memory_delta=memory_delta,
                                    peaked_memory_usage=peaked_memory_usage,
                                    memory_usage=new_memory_usage)
    if watching_memory:
        print(str(output))
    previous_call_memory_usage = new_memory_usage

 def watch_memory(self):
     if not self.watching_memory:
         return
     # calculate time delta using global t1 (from the pre-run
     # event) and current time
     self.time_delta = time.time() - self.t1
     new_memory_usage = memory_profiler.memory_usage()[0]
     self.memory_delta = new_memory_usage - self.previous_call_memory_usage
     self.keep_watching = False
     self.peaked_memory_usage = max(0, self.peak_memory_usage - new_memory_usage)
     num_commands = len(self.input_cells) - 1
     cmd = "In [{}]".format(num_commands)
     # convert the results into a pretty string
     output_template = ("{cmd} used {memory_delta:0.3f} MiB RAM in "
                        "{time_delta:0.3f}s, peaked {peaked_memory_usage:0.3f} "
                        "MiB above current, total RAM usage "
                        "{memory_usage:0.3f} MiB")
     output = output_template.format(
         time_delta=self.time_delta,
         cmd=cmd,
         memory_delta=self.memory_delta,
         peaked_memory_usage=self.peaked_memory_usage,
         memory_usage=new_memory_usage)
     print(str(output))
     self.previous_call_memory_usage = new_memory_usage

    def run_example(self, theInput, theOutput):
        # Import program (decrapted in 3.4, no other way at the moment)
        from importlib.machinery import SourceFileLoader
        solution = SourceFileLoader("solution", self.programPath).load_module()
        
        # Feed the input
        with PatchStd(theInput) as std:            
            # Start time counter
            startTime = time.time()
            
            # Run the program
            solution.main()
            
            # Get end time
            endTime = time.time() - startTime

            # Get memory (include current tests ~14MB but more or less is that)
            mem = memory_usage(max_usage=True)
        
            # Check output
            actual_output = std.getStdOut().getvalue()
            self.assertEqual(actual_output, theOutput)
            
            # Print time (not do before because output is not yet retrieved)
            std.restore()
            print("\tTime:   %.3f sec" % endTime)
            print("\tMemory: %.3f MB" % mem)
            
            # Show errors if any
            errors = std.getStdErr().getvalue()
            if errors != '':
                print("\t" + errors)

def test_memory_leak():
    """https://github.com/emcconville/wand/pull/127"""
    minimum = 1.0
    with Color('NONE') as nil_color:
        minimum = ctypes.sizeof(nil_color.raw)
    consumes = memory_usage((color_memory_leak, (), {}))
    assert consumes[-1] - consumes[0] <= minimum

def test(prefix, item_ct, loop_ct):
    h5 = h5py.File(sys.argv[1], "a")
    cc = h5.id.get_mdc_config()
    print(cc.max_size)
    print(h5.id.get_access_plist().get_cache())
    cc.min_size = 1024 * 1024
    cc.max_size = 1024 * 1024
    h5.id.set_mdc_config(cc)
    # h5.create_group(b'\xff')  # force new compact-or-indexed group with high byte
    # del h5[b'\xff']
    print("start  %s %.3f" % (prefix, time.time() - start))
    path_i = 0
    for i in range(loop_ct):
        data = np.zeros(item_ct, dtype=np.float32)
        i_str = "%05d" % i
        # path = ''
        # path = '/'.join((prefix, i_str[0], i_str[1], i_str[2], i_str[3]))
        path = str(path_i)
        # print(path + '/' + str(i))
        ds = h5.create_dataset(path + "/" + str(i), data=data)
        # ds = h5.create_dataset(path + '/' + str(i), (item_ct,), dtype=np.float32)
        # ds[:] = data
        # h5.create_group(path + '/' + i_str)
        if i != 0 and i % close_interval == 0:
            path_i += 1
            # print(i, '%.1f' % (time.time() - start), h5.id.get_mdc_size(), h5.id.get_mdc_hit_rate())
            print(
                i,
                "%.1f" % (time.time() - start),
                h5.id.get_mdc_size(),
                h5.id.get_mdc_hit_rate(),
                memory_profiler.memory_usage(-1),
            )
    print("end    %s %.3f" % (prefix, time.time() - start))
    h5.close()

def memorySummary():
    global memLog
    global memList
    global memMax
    if memLog:
        gc.collect()
        try:
            from memory_profile import memory_usage
            mem = memory_usage(-1)[0]
        except:
            raise
        # except:
#             try:
#                 mem = int(os.popen('ps -p %d -o vsz|tail -1' % os.getpid()).read())/1024.0
#             except:
#                 try:
#                     #CLE doesn't have full ps functionality
#                     #try to find first occurence of pid, then get memory usage slot
#                     #mwf debug
#                     #import pdb
#                     #pdb.set_trace()
#                     plist = os.popen('ps ').read()
#                     pr = plist.split('%s' % os.getpid())
#                     #print 'pr = %s ' % pr
#                     #print 'pr[1] = %s ' % pr[1]
#                     mem = int(pr[1].split()[1])/1024.0
#                 except:
#                     logEvent("memory function doesn't work on this platform\n")
#                     mem = 0
        if mem > 0:
            for pair in memList:
                logEvent(`pair[0]`+"  %"+`100.0*pair[1]/memMax`)

def watch_memory():
    import time

    # bring in the global memory usage value from the previous iteration
    global previous_call_memory_usage, peak_memory_usage, keep_watching
    nbr_commands = len(In)
    new_memory_usage = memory_profiler.memory_usage()[0]
    memory_delta = new_memory_usage - previous_call_memory_usage
    keep_watching = False
    peaked_memory_usage = max(0, peak_memory_usage - new_memory_usage)
    # calculate time delta using global t1 (from the pre-run event) and current
    # time
    time_delta_secs = time.time() - t1
    cmd = In[nbr_commands - 1]
    # convert the results into a pretty string
    output_template = "'{cmd}' used {memory_delta:0.4f} MiB RAM in {time_delta:0.2f}s, peaked {peaked_memory_usage:0.2f} MiB above current, total RAM usage {memory_usage:0.2f} MiB"
    output = output_template.format(
        time_delta=time_delta_secs,
        cmd=cmd,
        memory_delta=memory_delta,
        peaked_memory_usage=peaked_memory_usage,
        memory_usage=new_memory_usage,
    )
    print(str(output))
    previous_call_memory_usage = new_memory_usage

def f(a, v):
    print "running", os.getpid(), len(a)
    print "in process", memory_profiler.memory_usage()
    #a[0] = 'x'
    print "did arr get changed in process?", a[:5]
    v.value = len(a)
    time.sleep(5)

	def __init__(self, method, n=1, timeout=120.0):
		from memory_profiler import memory_usage
		import time
		from util import avg

		self.timeout = timeout

		max_memory_list = []
		avg_memory_list = []
		time_list = []

		for i in range(n):

			runner = self._Runner(memory_usage, {'proc': method, 'interval': 0.01, 'retval': True}, timeout)
			baseline = avg(memory_usage((lambda: None, ())))  # baseline memory usage measurement

			start_time = time.time()
			runner.run()  # actual running of the method
			end_time = time.time()

			if runner.result is not None:
				mem_use, self.result = runner.result
				memory = map(lambda x: x - baseline, mem_use)
			else:  # there was a timeout
				import sys
				memory = [-sys.maxint]
				self.result = None

			max_memory_list.append(max(memory))
			avg_memory_list.append(avg(memory))
			time_list.append((end_time - start_time) * 1000)

		self.max_memory = avg(max_memory_list)
		self.avg_memory = avg(avg_memory_list)
		self.time = avg(time_list)

def custom_minimize(function, algorithm, bounds = None, guess = None):

	def iter_minimize(): # lightweight version of iter_minimize for a single optimization method

		start = timeit.default_timer()
		
		result = 0
		
		# some minimization techniques do not require an initial guess
		if guess is not None:
			result = algorithm(function, guess) 

		else:
			result = algorithm(function, bounds)

		iterations = -1

		if 'nit' in result.keys():    
			iterations = result.get('nit')

		stop = timeit.default_timer()

		return iterations, start, stop, result

	#tracks amount of memory used  by current process (-1) every interval (.2 seconds)
	current_memory = memory_usage(-1, interval=.2) 
	most_mem = np.append(most_mem, max(current_memory))
	
	num_iters, start, stop, result = iter_minimize()

	exec_time = stop-start

	print '{0} took {1} seconds. The result, {2} was found at ({3})'.format(algorithm.__name__,exec_time,result.fun,result.x)
	print '{0} used {1} megabytes and took {2} iterations'.format(algorithm.__name__,most_mem,num_iters)
	print