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  5. __init__.py
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  7. README.md
tensorflow/python/debug/README.md

TensorFlow Debugger (TFDBG)

TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's computation runtime. TFDBG in TensorFlow 2.x provides access to:

  • Tensor values during eager and graph execution.
  • Structure of computation graphs
  • Source code and stack traces associated with these execution and graph-execution events.

How to use TFDBG?

TFDBG in TensorFlow 2.x consists of a Python API that enables dumping debug data to the file system (namely tf.debugging.experimental.enable_dump_debug_info()) and a TensorBoard-based GUI that provides an interactive visualization of the debug data (i.e., TensorBoard Debugger V2 Plugin).

enable_dump_debug_info() offers a number of levels of tensor-value instrumentation varying in the amount of information dumped and the incurred performance overhead.

See the API documentation of tf.debugging.experimental.enable_dump_debug_info()

For a detailed walkthrough of the GUI TensorBoard Debugger V2 Plugin, see Debugging Numerical Issues in TensorFlow Programs Using TensorBoard Debugger V2.

Known issues and limitations

  1. Using tf.debugging.experimental.enable_dump_debug_info() leads to performance penalty on your TensorFlow program. The amount of slowdown varied depending on whether you are using TensorFlow on CPU, GPUs, or TPUs. The performance penalty is the highest on TPUs, followed by GPUs, and lowest on CPU.
  2. tf.debugging.experimental.enable_dump_debug_info() is currently incompatible with model saving/loading and checkpointing

Legacy API for TensorFlow 1.x

TensorFlow 1.x's execution paradigm is different from that of TensorFlow v2; it is based on the deprecated tf.Session If you are using TensorFlow 1.x, you can use the deprecated tf_debug.LocalCLIDebugWrapperSession wrapper for tf.Session to inspect tensor values and other types of debug information in a terminal-based command-line interface. For details, see this blog post.