def advise(graph, run_meta=None, options=_DEFAULT_ADVISE_OPTIONS):
"""Auto profile and advise.
Builds profiles and automatically check anomalies of various
aspects. For more details:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
Args:
graph: required tf.Graph.
run_meta: optional tensorflow.RunMetadata proto. It is necessary to
to support run time information profiling, such as time and memory.
options: see ALL_ADVICE example above. Default checks everything.
Returns:
Returns AdviceProto proto
"""
if options == _DEFAULT_ADVISE_OPTIONS:
options = ALL_ADVICE.copy()
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(graph,
None,
run_meta,
add_trace=True)
# pylint: enable=protected-access
run_meta_str = run_meta.SerializeToString() if run_meta else b''
opts = _build_advisor_options(options)
ret = tfprof_output_pb2.AdviceProto()
ret.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(), 'advise'.encode('utf-8'),
opts.SerializeToString()))
return ret
def advise(self, options):
"""Automatically detect problems and generate reports.
Args:
options: A dict of options. See ALL_ADVICE example above.
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
