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(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 profile(graph,
run_meta=None,
op_log=None,
cmd='scope',
options=_DEFAULT_PROFILE_OPTIONS):
"""Print model statistics.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
timing and memory information when 'select' option contains
'micros' and 'bytes'.
op_log: tensorflow::tfprof::OpLog proto. users can use this proto to
group together ops and use a op_type to select the group.
cmd: string. Either 'op', 'scope', 'graph', 'code'.
'op' view organize outputs using operation type. (e.g. MatMul)
'scope' view organize outputs using graph node name scope.
'graph' view organize outputs using graph node inputs/outputs.
'code' view organize outputs using Python call stack.
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
If cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
If cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on options['output']
"""
if options == _DEFAULT_PROFILE_OPTIONS:
options = TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(graph,
op_log,
run_meta,
add_trace=cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if cmd == 'code' or cmd == 'op':
tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(), cmd.encode('utf-8'),
opts.SerializeToString()))
elif cmd == 'graph' or cmd == 'scope':
tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(), cmd.encode('utf-8'),
opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(None, None,
'unknown cmd: %s\n' % cmd)
return tfprof_node
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: A step uint64 used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, run_meta=run_meta, add_trace=False,
add_trainable_var=False)
# pylint: enable=protected-access
print_mdl.AddStep(
step, run_meta.SerializeToString(), op_log.SerializeToString())
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: A step uint64 used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, run_meta=run_meta, add_trace=False,
add_trainable_var=False)
# pylint: enable=protected-access
print_mdl.AddStep(
step, run_meta.SerializeToString(), op_log.SerializeToString())
def __init__(self, graph, op_log=None):
"""Constructor.
Args:
graph: tf.Graph.
op_log: optional. tensorflow::tfprof::OpLogProto proto. Used to define
extra op types.
"""
self._graph = graph
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, op_log=op_log)
# pylint: enable=protected-access
print_mdl.NewProfiler(
self._graph.as_graph_def(add_shapes=True).SerializeToString(),
op_log.SerializeToString())
def __init__(self, graph, op_log=None):
"""Constructor.
Args:
graph: tf.Graph.
op_log: optional. tensorflow::tfprof::OpLogProto proto. Used to define
extra op types.
"""
self._graph = graph
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(self._graph,
op_log=op_log)
# pylint: enable=protected-access
print_mdl.NewProfiler(
self._graph.as_graph_def(add_shapes=True).SerializeToString(),
op_log.SerializeToString())
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: int, A step used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, run_meta=run_meta)
# pylint: enable=protected-access
# TODO(xpan): P1: Better to find the current graph.
print_mdl.AddStep(
step,
self._graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta.SerializeToString(), op_log.SerializeToString())
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: int, A step used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(self._graph,
run_meta=run_meta)
# pylint: enable=protected-access
# TODO(xpan): P1: Better to find the current graph.
self._coverage = print_mdl.AddStep(
step,
self._graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta.SerializeToString(), op_log.SerializeToString())
def profile(graph,
run_meta=None,
op_log=None,
cmd='scope',
options=_DEFAULT_PROFILE_OPTIONS):
"""Profile model.
Tutorials and examples can be found in:
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.
op_log: tensorflow.tfprof.OpLogProto proto. User can assign "types" to
graph nodes with op_log. "types" allow user to flexibly group and
account profiles using options['accounted_type_regexes'].
cmd: string. Either 'op', 'scope', 'graph' or 'code'.
'op' view organizes profile using operation type. (e.g. MatMul)
'scope' view organizes profile using graph node name scope.
'graph' view organizes profile using graph node inputs/outputs.
'code' view organizes profile using Python call stack.
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
If cmd is 'scope' or 'graph', returns GraphNodeProto proto.
If cmd is 'op' or 'code', returns MultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on options['output']
"""
if options == _DEFAULT_PROFILE_OPTIONS:
options = (option_builder.ProfileOptionBuilder
.trainable_variables_parameter())
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, op_log, run_meta, add_trace=cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if cmd == 'code' or cmd == 'op':
tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
cmd.encode('utf-8'),
opts.SerializeToString()))
elif cmd == 'graph' or cmd == 'scope':
tfprof_node = tfprof_output_pb2.GraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
cmd.encode('utf-8'),
opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(
None, None, 'unknown cmd: %s\n' % cmd)
return tfprof_node
def profile(graph,
run_meta=None,
op_log=None,
cmd='scope',
options=_DEFAULT_PROFILE_OPTIONS):
"""Profile model.
Tutorials and examples can be found in:
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.
op_log: tensorflow.tfprof.OpLogProto proto. User can assign "types" to
graph nodes with op_log. "types" allow user to flexibly group and
account profiles using options['accounted_type_regexes'].
cmd: string. Either 'op', 'scope', 'graph' or 'code'.
'op' view organizes profile using operation type. (e.g. MatMul)
'scope' view organizes profile using graph node name scope.
'graph' view organizes profile using graph node inputs/outputs.
'code' view organizes profile using Python call stack.
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
If cmd is 'scope' or 'graph', returns GraphNodeProto proto.
If cmd is 'op' or 'code', returns MultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on options['output']
"""
if options == _DEFAULT_PROFILE_OPTIONS:
options = (option_builder.ProfileOptionBuilder.
trainable_variables_parameter())
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(graph,
op_log,
run_meta,
add_trace=cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if cmd == 'code' or cmd == 'op':
tfprof_node = tfprof_output_pb2.MultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(), cmd.encode('utf-8'),
opts.SerializeToString()))
elif cmd == 'graph' or cmd == 'scope':
tfprof_node = tfprof_output_pb2.GraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(), cmd.encode('utf-8'),
opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(None, None,
'unknown cmd: %s\n' % cmd)
return tfprof_node
def profile(graph,
run_meta=None,
op_log=None,
cmd='scope',
options=_DEFAULT_PROFILE_OPTIONS):
"""Print model statistics.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
timing and memory information when 'select' option contains
'micros' and 'bytes'.
op_log: tensorflow::tfprof::OpLog proto. users can use this proto to
group together ops and use a op_type to select the group.
cmd: string. Either 'op', 'scope', 'graph', 'code'.
'op' view organize outputs using operation type. (e.g. MatMul)
'scope' view organize outputs using graph node name scope.
'graph' view organize outputs using graph node inputs/outputs.
'code' view organize outputs using Python call stack.
options: A dict of options. See core/profiler/g3doc/options.md.
Returns:
If cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
If cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on options['output']
"""
if options == _DEFAULT_PROFILE_OPTIONS:
options = TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, op_log, run_meta, add_trace=cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if cmd == 'code' or cmd == 'op':
tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
cmd.encode('utf-8'),
opts.SerializeToString()))
elif cmd == 'graph' or cmd == 'scope':
tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
cmd.encode('utf-8'),
opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(
None, None, 'unknown cmd: %s\n' % cmd)
return tfprof_node
