tf.data.experimental.enable_debug_mode

Enables debug mode for tf.data.

tf.data.experimental.enable_debug_mode()

Example usage with pdb module:

import tensorflow as tf
import pdb

tf.data.experimental.enable_debug_mode()

def func(x):
  # Python 3.7 and older requires `pdb.Pdb(nosigint=True).set_trace()`
  pdb.set_trace()
  x = x + 1
  return x

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.map(func)

for item in dataset:
  print(item)

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.