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 View source 
with_node_names(
    start_name_regexes=None, show_name_regexes=None, hide_name_regexes=None,
    trim_name_regexes=None
)
 Regular expressions used to select profiler nodes to display. After 'with_accounted_types' is evaluated, 'with_node_names' are evaluated as follows: For a profile data structure, profiler first finds the profiler nodes matching 'start_name_regexes', and starts displaying profiler nodes from there. Then, if a node matches 'show_name_regexes' and doesn't match 'hide_name_regexes', it's displayed. If a node matches 'trim_name_regexes', profiler stops further searching that branch.
 


 Args
  start_name_regexes   list of node name regexes to start displaying.  
  show_name_regexes   list of node names regexes to display.  
  hide_name_regexes   list of node_names regexes that should be hidden.  
  trim_name_regexes   list of node name regexes from where to stop.   
 


 Returns   self  
 with_pprof_output View source 
with_pprof_output(
    pprof_file
)
 Generate a pprof profile gzip file. To use the pprof file: pprof -png --nodecount=100 --sample_index=1 
 


 Args
  pprof_file   filename for output, usually suffixed with .pb.gz.   
 


 Returns   self.  
 with_stdout_output View source 
with_stdout_output()
 Print the result to stdout. with_step View source 
with_step(
    step
)
 Which profile step to use for profiling. The 'step' here refers to the step defined by Profiler.add_step() API.
 


 Args
  step   When multiple steps of profiles are available, select which step's profile to use. If -1, use average of all available steps.   
 


 Returns   self  
 with_timeline_output View source 
with_timeline_output(
    timeline_file
)
 Generate a timeline json file.