IDE breakpoint in TensorFlow Dataset API mapped py_function?

Question

I'm using the Tensorflow Dataset API to prepare my data for input into my network. During this process, I have some custom Python functions which are mapped to the dataset using tf.py_function. I want to be able to debug the data going into these functions and what happens to that data inside these functions. When a py_function is called, this calls back to the main Python process (according to this answer). Since this function is in Python, and in the main process, I would expect a regular IDE breakpoint to be able stop in this process. However, this doesn't seem to be the case (example below where the breakpoint does not halt execution). Is there a way to drop into a breakpoint within a py_function used by the Dataset map?

Example where the breakpoint does not halt execution

import tensorflow as tf

def add_ten(example, label):
    example_plus_ten = example + 10  # Breakpoint here.
    return example_plus_ten, label

examples = [10, 20, 30, 40, 50, 60, 70, 80]
labels =   [ 0,  0,  1,  1,  1,  1,  0,  0]

examples_dataset = tf.data.Dataset.from_tensor_slices(examples)
labels_dataset = tf.data.Dataset.from_tensor_slices(labels)
dataset = tf.data.Dataset.zip((examples_dataset, labels_dataset))
dataset = dataset.map(map_func=lambda example, label: tf.py_function(func=add_ten, inp=[example, label],
                                                                     Tout=[tf.int32, tf.int32]))
dataset = dataset.batch(2)
example_and_label = next(iter(dataset))

Answer 1

Tensorflow 2.0 implementation of tf.data.Dataset opens a C threads for each call without notifying your debugger. Use pydevd's to manually set a tracing function that will connect to your default debugger server and start feeding it the debug data.

import pydevd
pydevd.settrace()

Example with your code:

import tensorflow as tf
import pydevd

def add_ten(example, label):
    pydevd.settrace(suspend=False)
    example_plus_ten = example + 10  # Breakpoint here.
    return example_plus_ten, label

examples = [10, 20, 30, 40, 50, 60, 70, 80]
labels =   [ 0,  0,  1,  1,  1,  1,  0,  0]

examples_dataset = tf.data.Dataset.from_tensor_slices(examples)
labels_dataset = tf.data.Dataset.from_tensor_slices(labels)
dataset = tf.data.Dataset.zip((examples_dataset, labels_dataset))
dataset = dataset.map(map_func=lambda example, label: tf.py_function(func=add_ten, inp=[example, label],
                                                                     Tout=[tf.int32, tf.int32]))
dataset = dataset.batch(2)
example_and_label = next(iter(dataset))

Note: If you are using IDE which already bundles pydevd (such as PyDev or PyCharm) you do not have to install pydevd separately, it will picked up during the debug session.