tf.summary.trace

Starts a trace to record computation graphs and profiling information.

tf.summary.trace_on(
    graph=True, profiler=False
)

Used in the notebooks

Used in the guide	Used in the tutorials
Introduction to modules, layers, and models	Examining the TensorFlow Graph

Must be invoked in eager mode.

When enabled, TensorFlow runtime will collection information that can later be exported and consumed by TensorBoard. The trace is activated across the entire TensorFlow runtime and affects all threads of execution.

To stop the trace and export the collected information, use tf.summary.trace_export. To stop the trace without exporting, use tf.summary.trace_off.

Args
`graph`	If True, enables collection of executed graphs. It includes ones from tf.function invocation and ones from the legacy graph mode. The default is True.
`profiler`	If True, enables the advanced profiler. Enabling profiler implicitly enables the graph collection. The profiler may incur a high memory overhead. The default is False.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.summary / trace_export

Used in the notebooks

Args