tf.data.experimental.enable_debug_mode

Enables debug mode for tf.data.

tf.data.experimental.enable_debug_mode()

Example usage:

import tensorflow as tf

tf.data.experimental.enable_debug_mode()
ds = ... # input pipeline definition

The effect of debug mode is two-fold:

1) Any transformations that would introduce asynchrony, parallelism, or non-determinism to the input pipeline execution will be forced to execute synchronously, sequentially, and deterministically.

2) Any user-defined functions passed into tf.data transformations such as map will be wrapped in tf.py_function so that their body is executed "eagerly" as a Python function as opposed to a traced TensorFlow graph, which is the default behavior. Note that even when debug mode is enabled, the user-defined function is still traced to infer the shape and type of its outputs; as a consequence, any print statements or breakpoints will be triggered once during the tracing before the actual execution of the input pipeline.

ValueError When invoked from graph mode.