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tree: ab46cb1ba36ba0e9ecedcf59a0dbca700debf12b [path history] [tgz]
  1. cli/
  2. examples/
  3. lib/
  4. wrappers/
  5. __init__.py
  6. BUILD
  7. README.md
tensorflow/python/debug/README.md

TensorFlow Debugger (TFDBG)

TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's computation graphs. It provides access to internal graph structures and tensor values at TensorFlow runtime.

Why TFDBG?

In TensorFlow‘s current computation-graph framework, almost all actual computation after graph construction happens in a single Python function, namely tf.Session.run. Basic Python debugging tools such as pdb cannot be used to debug Session.run, due to the fact that TensorFlow’s graph execution happens in the underlying C++ layer. C++ debugging tools such as gdb are not ideal either, because of their inability to recognize and organize the stack frames and variables in a way relevant to TensorFlow's operations, tensors and other graph constructs.

TFDBG addresses these limitations. Among the features provided by TFDBG, the following ones are designed to facilitate runtime debugging of TensorFlow models:

  • Easy access through session wrappers
  • Easy integration with common high-level APIs, such as TensorFlow Estimators and Keras
  • Inspection of runtime tensor values and node connections
  • Conditional breaking after runs that generate tensors satisfying given predicates, which makes common debugging tasks such as tracing the origin of infinities and NaNs easier
  • Association of nodes and tensors in graphs with Python source lines
  • Profiling of models at the level of graph nodes and Python source lines. (Omitted internal-only feature)
  • A gRPC-based remote debugging protocol, which allows us to build a browser-based graphical user interface (GUI) for TFDBG: the TensorBoard Debugger Plugin.

How to use TFDBG?

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