tf.compat.v1.profiler.ProfileOptionBuilder

Option Builder for Profiling API.

tf.compat.v1.profiler.ProfileOptionBuilder(
    options=None
)

For tutorial on the options, see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md

# Users can use pre-built options:
opts = (
    tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())

# Or, build your own options:
opts = (tf.compat.v1.profiler.ProfileOptionBuilder()
    .with_max_depth(10)
    .with_min_micros(1000)
    .select(['accelerator_micros'])
    .with_stdout_output()
    .build()

# Or customize the pre-built options:
opts = (tf.compat.v1.profiler.ProfileOptionBuilder(
    tf.profiler.ProfileOptionBuilder.time_and_memory())
    .with_displaying_options(show_name_regexes=['.*rnn.*'])
    .build())

# Finally, profiling with the options:
_ = tf.compat.v1.profiler.profile(tf.compat.v1.get_default_graph(),
                        run_meta=run_meta,
                        cmd='scope',
                        options=opts)

Args
options	Optional initial option dict to start with.

Methods

account_displayed_op_only

View source

account_displayed_op_only(
    is_true
)

Whether only account the statistics of displayed profiler nodes.

Args
is_true	If true, only account statistics of nodes eventually displayed by the outputs. Otherwise, a node's statistics are accounted by its parents as long as it's types match 'account_type_regexes', even if it is hidden from the output, say, by hide_name_regexes.

Returns
self

build

View source

build()

Build a profiling option.

Returns
A dict of profiling options.

float_operation

View source

@staticmethod
float_operation()

Options used to profile float operations.

Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/profile_model_architecture.md on the caveats of calculating float operations.

Returns
A dict of profiling options.

order_by

View source

order_by(
    attribute
)

Order the displayed profiler nodes based on a attribute.

Supported attribute includes micros, bytes, occurrence, params, etc. https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md

Args
attribute	An attribute the profiler node has.

Returns
self

select

View source

select(
    attributes
)

Select the attributes to display.

See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/options.md for supported attributes.

Args
attributes	A list of attribute the profiler node has.

Returns
self

time_and_memory

View source

@staticmethod
time_and_memory(
    min_micros=1, min_bytes=1, min_accelerator_micros=0, min_cpu_micros=0,
    min_peak_bytes=0, min_residual_bytes=0, min_output_bytes=0
)

Show operation time and memory consumptions.

Args
min_micros	Only show profiler nodes with execution time no less than this. It sums accelerator and cpu times.
min_bytes	Only show profiler nodes requested to allocate no less bytes than this.
min_accelerator_micros	Only show profiler nodes spend no less than this time on accelerator (e.g. GPU).
min_cpu_micros	Only show profiler nodes spend no less than this time on cpu.
min_peak_bytes	Only show profiler nodes using no less than this bytes at peak (high watermark). For profiler nodes consist of multiple graph nodes, it sums the graph nodes' peak_bytes.
min_residual_bytes	Only show profiler nodes have no less than this bytes not being de-allocated after Compute() ends. For profiler nodes consist of multiple graph nodes, it sums the graph nodes' residual_bytes.
min_output_bytes	Only show profiler nodes have no less than this bytes output. The output are not necessarily allocated by this profiler nodes.

Returns
A dict of profiling options.

trainable_variables_parameter

View source

@staticmethod
trainable_variables_parameter()

Options used to profile trainable variable parameters.

Normally used together with 'scope' view.

Returns
A dict of profiling options.

with_accounted_types

View source

with_accounted_types(
    account_type_regexes
)

Selectively counting statistics based on node types.

Here, 'types' means the profiler nodes' properties. Profiler by default consider device name (e.g. /job:xx/.../device:GPU:0) and operation type (e.g. MatMul) as profiler nodes' properties. User can also associate customized 'types' to profiler nodes through OpLogProto proto.

For example, user can select profiler nodes placed on gpu:0 with: account_type_regexes=['.*gpu:0.*']

If none of a node's properties match the specified regexes, the node is not displayed nor accounted.

Args
account_type_regexes	A list of regexes specifying the types.

Returns
self.

with_empty_output

View source

with_empty_output()

Do not generate side-effect outputs.

with_file_output

View source

with_file_output(
    outfile
)

Print the result to a file.

with_max_depth

View source

with_max_depth(
    max_depth
)

Set the maximum depth of display.

The depth depends on profiling view. For 'scope' view, it's the depth of name scope hierarchy (tree), for 'op' view, it's the number of operation types (list), etc.

Args
max_depth	Maximum depth of the data structure to display.

Returns
self

with_min_execution_time

View source

with_min_execution_time(
    min_micros=0, min_accelerator_micros=0, min_cpu_micros=0
)

Only show profiler nodes consuming no less than 'min_micros'.

Args
min_micros	Only show profiler nodes with execution time no less than this. It sums accelerator and cpu times.
min_accelerator_micros	Only show profiler nodes spend no less than this time on accelerator (e.g. GPU).
min_cpu_micros	Only show profiler nodes spend no less than this time on cpu.

Returns
self

with_min_float_operations

View source

with_min_float_operations(
    min_float_ops
)

Only show profiler nodes consuming no less than 'min_float_ops'.

Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/profile_model_architecture.md on the caveats of calculating float operations.

Args
min_float_ops	Only show profiler nodes with float operations no less than this.

Returns
self

with_min_memory

View source

with_min_memory(
    min_bytes=0, min_peak_bytes=0, min_residual_bytes=0, min_output_bytes=0
)

Only show profiler nodes consuming no less than 'min_bytes'.

Args
min_bytes	Only show profiler nodes requested to allocate no less bytes than this.
min_peak_bytes	Only show profiler nodes using no less than this bytes at peak (high watermark). For profiler nodes consist of multiple graph nodes, it sums the graph nodes' peak_bytes.
min_residual_bytes	Only show profiler nodes have no less than this bytes not being de-allocated after Compute() ends. For profiler nodes consist of multiple graph nodes, it sums the graph nodes' residual_bytes.
min_output_bytes	Only show profiler nodes have no less than this bytes output. The output are not necessarily allocated by this profiler nodes.

Returns
self

with_min_occurrence

View source

with_min_occurrence(
    min_occurrence
)

Only show profiler nodes including no less than 'min_occurrence' graph nodes.

A "node" means a profiler output node, which can be a python line (code view), an operation type (op view), or a graph node (graph/scope view). A python line includes all graph nodes created by that line, while an operation type includes all graph nodes of that type.

Args
min_occurrence	Only show nodes including no less than this.

Returns
self

with_min_parameters

View source

with_min_parameters(
    min_params
)

Only show profiler nodes holding no less than 'min_params' parameters.

'Parameters' normally refers the weights of in TensorFlow variables. It reflects the 'capacity' of models.

Args
min_params	Only show profiler nodes holding number parameters no less than this.

Returns
self

with_node_names