TensorFlow multi-step profiler.
tf.compat.v1.profiler.Profiler(
graph=None, op_log=None
)
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.compat.v1.RunMetadata()
_ = sess.run(...,
options=tf.compat.v1.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()
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
add_step
View source
add_step(
step, run_meta
)
Add statistics of a step.
Args |
step
|
int, An id used to group one or more different run_meta together.
When profiling with the profile_xxx APIs, user can use the step
id in the options to profile these run_meta together.
|
run_meta
|
RunMetadata proto that contains statistics of a session run.
|
advise
View source
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.
|
profile_graph
View source
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.
|
profile_name_scope
View source
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.
|
profile_operations
View source
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.
|
profile_python
View source
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.
|
serialize_to_string
View source
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.
|