Class Profiler
Defined in tensorflow/python/profiler/model_analyzer.py
.
TensorFlow multi-step profiler.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/README.md
Typical use case:
# Currently we are only allowed to create 1 profiler per process.
profiler = Profiler(sess.graph)
for i in xrange(total_steps):
if i % 10000 == 0:
run_meta = tf.RunMetadata()
_ = sess.run(...,
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_meta)
profiler.add_step(i, run_meta)
# Profile the parameters of your model.
profiler.profile_name_scope(options=(option_builder.ProfileOptionBuilder
.trainable_variables_parameter()))
# Or profile the timing of your model operations.
opts = option_builder.ProfileOptionBuilder.time_and_memory()
profiler.profile_operations(options=opts)
# Or you can generate a timeline:
opts = (option_builder.ProfileOptionBuilder(
option_builder.ProfileOptionBuilder.time_and_memory())
.with_step(i)
.with_timeline_output(filename).build())
profiler.profile_graph(options=opts)
else:
_ = sess.run(...)
# Auto detect problems and generate advice.
profiler.advise()
__init__
__init__(
graph=None,
op_log=None
)
Constructor.
Args:
graph
: tf.Graph. If None and eager execution is not enabled, use default graph.op_log
: optional. tensorflow::tfprof::OpLogProto proto. Used to define extra op types.
Methods
tf.profiler.Profiler.add_step
add_step(
step,
run_meta
)
Add statistics of a step.
Args:
step
: int, An id used to group one or more differentrun_meta
together. When profiling with the profile_xxx APIs, user can use thestep
id in theoptions
to profile theserun_meta
together.run_meta
: RunMetadata proto that contains statistics of a session run.
tf.profiler.Profiler.advise
advise(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.
tf.profiler.Profiler.profile_graph
profile_graph(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.
tf.profiler.Profiler.profile_name_scope
profile_name_scope(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.
tf.profiler.Profiler.profile_operations
profile_operations(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.
tf.profiler.Profiler.profile_python
profile_python(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.
tf.profiler.Profiler.serialize_to_string
serialize_to_string()
Serialize the ProfileProto to a binary string.
Users can write it to file for offline analysis by tfprof commandline or graphical interface.
Returns:
ProfileProto binary string.