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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.
Raises | |
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ValueError
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When invoked from graph mode. |