def produce_timeline_profile(profile_dir, resources_dir, profile_cnt, options):
"""Produces a timeline profile."""
timeline_path = os.path.join(resources_dir, PROFILER_COMMON_PREFIX + 'timeline')
if not os.path.isfile(timeline_path):
profiles = {}
log_path = os.path.join(PROFILER_TMP_DIR, PROFILER_TMP_NAME)
options['output'] = 'timeline:outfile=' + log_path
opts = model_analyzer._build_options(options) # pylint: disable=protected-access
for idx, prof in enumerate(gfile.ListDirectory(profile_dir)):
prof_file = os.path.join(profile_dir, prof)
if not os.path.isfile(prof_file):
continue
chosen_profile = os.path.join(resources_dir, PROFILER_COMMON_PREFIX + 'timeline_' + prof)
profiles[prof] = chosen_profile
if os.path.isfile(chosen_profile):
if idx == 0:
target_ts = get_timestamp(chosen_profile)
continue
tf.logging.info("Parse profile context %r" % prof_file)
remove_tmp_files()
# Parse profile context
ProfilerFromFile(prof_file.encode('utf-8'))
pwtf.Profile(options['view'].encode('utf-8'), opts.SerializeToString())
DeleteProfiler()
if idx == 0:
prof_names = get_informative_profiles(PROFILER_TMP_DIR, profile_cnt)
target_ts = get_timestamp(os.path.join(PROFILER_TMP_DIR, prof_names[0]))
else:
prof_names = get_profiles_by_timestamp(PROFILER_TMP_DIR, target_ts, profile_cnt)
tf.logging.info("Choose %r as the most informative profile context for %r" % (prof_names, prof))
gen_profile([os.path.join(PROFILER_TMP_DIR, name) for name in prof_names], chosen_profile)
merge_profiles(profiles, timeline_path)
return load_profile(timeline_path)
def serialize_data(self, graph, run_metadata_list):
devnull = open(os.devnull, 'w')
f = io.BytesIO()
with stdout_redirector(f): #remove 'Parsing Inputs...'
with RedirectStdStreams(stderr=devnull): #this stops a meaningless error on stderr
profiler = Profiler(graph)
for i, run_metadata in enumerate(run_metadata_list):
profiler.add_step(i+1, run_metadata)
#use these to print to stdout
# profiler.profile_name_scope(ALL_OPTIONS_PRINT)
all_opts = _build_options(ALL_OPTIONS)
perf_opts = _build_options(PERF_OPTIONS)
with stderr_redirector(f): #this stops some meaningless errors on stderr
self._scope_all_stats_str = print_mdl.Profile('scope'.encode('utf-8'), all_opts.SerializeToString())
self._op_all_stats_str = print_mdl.Profile('op'.encode('utf-8'), all_opts.SerializeToString())
self._op_perf_stats_str = print_mdl.Profile('op'.encode('utf-8'), perf_opts.SerializeToString())
def produce_other_profile(profile_dir, resources_dir, profile_cnt, options):
other_path = os.path.join(resources_dir, PROFILER_COMMON_PREFIX + options['view'])
options['output'] = 'file:outfile=' + other_path
opts = model_analyzer._build_options(options) # pylint: disable=protected-access
# Create profiler from the first profile context.
profile_context = get_first_profile_context(profile_dir)
ProfilerFromFile(profile_context.encode('utf-8'))
pwtf.Profile(options['view'].encode('utf-8'), opts.SerializeToString())
DeleteProfiler()
return load_profile(other_path)
def profile_operations(self, options):
"""Profile the statistics of the Operation types (e.g. MatMul, Conv2D).
Args:
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
a MultiGraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.Profile('op'.encode('utf-8'), opts.SerializeToString()))
return tfprof_node
def advise(self, options=ALL_ADVICE): # pylint: disable=dangerous-default-value
"""Automatically detect problems and generate reports.
Args:
options: A dict of options.
Returns:
A Advise proto that conains the reports from all checkers.
"""
advise_pb = tfprof_output_pb2.AdviceProto()
opts = _build_advisor_options(options)
advise_pb.ParseFromString(
print_mdl.Profile('advise'.encode('utf-8'), opts.SerializeToString()))
return advise_pb
def profile_graph(self, options):
"""Profile the statistics of graph nodes, organized by dataflow graph.
Args:
options: A dict of profiler options.
Returns:
a TFGraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
return tfprof_node
def profile_name_scope(self, options):
"""Profile the statistics of graph nodes, organized by name scope.
Args:
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
a GraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.GraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.Profile('scope'.encode('utf-8'), opts.SerializeToString()))
return tfprof_node
def profile_python_codes(self, options):
"""Profile the statistics of the Python codes.
Hint: set options['show_name_regexes'] = ['.*my_code.py.*']
Args:
options: A dict of profiler options.
Returns:
a TFMultiGraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.Profile('code'.encode('utf-8'), opts.SerializeToString()))
return tfprof_node
def profile_graph(self, options):
"""Profile the statistics of graph nodes, organized by dataflow graph.
Args:
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
a GraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.GraphNodeProto()
try:
tfprof_node.ParseFromString(
print_mdl.Profile('graph'.encode('utf-8'), opts.SerializeToString()))
except message.DecodeError as e:
sys.stderr.write('Cannot parse returned proto: %s.\n' % e)
return tfprof_node
def profile_operations(self, options):
"""Profile the statistics of the Operation types (e.g. MatMul, Conv2D).
Args:
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
a MultiGraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
try:
tfprof_node.ParseFromString(
print_mdl.Profile('op'.encode('utf-8'), opts.SerializeToString()))
except message.DecodeError as e:
sys.stderr.write('Cannot parse returned proto: %s.\n' % e)
return tfprof_node
def profile_python(self, options):
"""Profile the statistics of the Python codes.
By default, it shows the call stack from root. To avoid
redundant output, you may use options to filter as below
options['show_name_regexes'] = ['.*my_code.py.*']
Args:
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
a MultiGraphNodeProto that records the results.
"""
opts = _build_options(options)
tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.Profile('code'.encode('utf-8'), opts.SerializeToString()))
return tfprof_node
def produce_pprof_profile(profile_dir, resources_dir, profile_cnt, options):
image_path = os.path.join(resources_dir, PROFILER_PPROF_IMAGE_NAME)
if not os.path.isfile(image_path):
tmp_path = os.path.join(resources_dir, PROFILER_COMMON_PREFIX + options['view'])
if not os.path.isfile(tmp_path):
options['view'] = 'code'
options['output'] = 'pprof:outfile=' + tmp_path
opts = model_analyzer._build_options(options) # pylint: disable=protected-access
# Create profiler from the first profile context.
profile_context = get_first_profile_context(profile_dir)
ProfilerFromFile(profile_context.encode('utf-8'))
pwtf.Profile(options['view'].encode('utf-8'), opts.SerializeToString())
DeleteProfiler()
"""Produces a pprof profile."""
subprocess.call([
'pprof', '-svg', '--nodecount=100', '--sample_index=1',
'-output={}'.format(image_path), tmp_path
])
return load_profile(image_path)
def handle_profile_api():
"""Handles profile API requests."""
options = json.loads(flask.request.args.get('options'))
# Determine view and output format.
if options['view'] == 'pprof':
output_format = 'pprof'
options['view'] = 'code'
elif options['view'] == 'graph':
output_format = 'timeline'
else:
output_format = 'file'
# Produce a profile.
options['output'] = output_format + ':outfile=' + PROFILER_LOG_PATH
opts = model_analyzer._build_options(options) # pylint: disable=protected-access
pwtf.Profile(options['view'].encode('utf-8'), opts.SerializeToString())
if output_format == 'pprof':
return produce_pprof_profile()
elif output_format == 'timeline':
return produce_timeline_profile()
else:
return load_profile(PROFILER_LOG_PATH)
