6af6b3b1ea
Update references in source files and docs in tensorflow and related projects. PiperOrigin-RevId: 201766994
56 lines
2.7 KiB
Markdown
56 lines
2.7 KiB
Markdown
# TensorFlow Debugger (TFDBG)
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[TOC]
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TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's computation
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graphs. It provides access to internal graph structures and tensor values at
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TensorFlow runtime.
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<!-- TODO(cais): Add release notes starting from 1.3. -->
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## Why TFDBG?
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In TensorFlow's current
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[computation-graph framework](https://www.tensorflow.org/get_started/get_started#the_computational_graph),
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almost all actual computation after graph construction happens in a single
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Python function, namely
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[tf.Session.run](https://www.tensorflow.org/api_docs/python/tf/Session#run).
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Basic Python debugging tools such as [pdb](https://docs.python.org/2/library/pdb.html)
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cannot be used to debug `Session.run`, due to the fact that TensorFlow's graph
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execution happens in the underlying C++ layer. C++ debugging tools such as
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[gdb](https://www.gnu.org/software/gdb/) are not ideal either, because of their
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inability to recognize and organize the stack frames and variables in a way
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relevant to TensorFlow's operations, tensors and other graph constructs.
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TFDBG addresses these limitations. Among the features provided by TFDBG, the
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following ones are designed to facilitate runtime debugging of TensorFlow
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models:
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* Easy access through session wrappers
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* Easy integration with common high-level APIs, such as
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[TensorFlow Estimators](https://www.tensorflow.org/guide/estimators) and
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[Keras](https://keras.io/)
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* Inspection of runtime tensor values and node connections
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* Conditional breaking after runs that generate tensors satisfying given
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predicates, which makes common debugging tasks such as tracing the origin
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of infinities and [NaNs](https://en.wikipedia.org/wiki/NaN) easier
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* Association of nodes and tensors in graphs with Python source lines
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* Profiling of models at the level of graph nodes and Python source lines.
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(Omitted internal-only feature)
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* A [gRPC](https://grpc.io/)-based remote debugging protocol, which allows us to
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build a browser-based graphical user interface (GUI) for TFDBG: the
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[TensorBoard Debugger Plugin](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
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## How to use TFDBG?
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* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/guide/debugger.
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* For information on the web GUI of TFDBG (TensorBoard Debugger Plugin), see
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[this README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
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* For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg.
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## Related Publications
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* Cai, S., Breck E., Nielsen E., Salib M., Sculley D. (2016) TensorFlow Debugger:
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Debugging Dataflow Graphs for Machine Learning. https://research.google.com/pubs/pub45789.html
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