wehub-resource-sync
53 lines
2.4 KiB
Markdown
53 lines
2.4 KiB
Markdown
# TensorFlow Debugger (TFDBG)
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[TOC]
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TensorFlow Debugger (TFDBG) is a specialized debugger for TensorFlow's
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computation runtime. TFDBG in TensorFlow 2.x provides access to:
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- Tensor values during [eager](https://www.tensorflow.org/guide/eager) and
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[graph](https://www.tensorflow.org/api_docs/python/tf/Graph) execution.
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- Structure of computation graphs
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- Source code and stack traces associated with these execution and
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graph-execution events.
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## How to use TFDBG?
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TFDBG in TensorFlow 2.x consists of a Python API that enables dumping debug data
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to the file system (namely `tf.debugging.experimental.enable_dump_debug_info()`)
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and a TensorBoard-based GUI that provides an interactive visualization of the
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debug data (i.e., *TensorBoard Debugger V2 Plugin*).
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`enable_dump_debug_info()` offers a number of levels of tensor-value
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instrumentation varying in the amount of information dumped and the incurred
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performance overhead.
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See the API documentation of
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[`tf.debugging.experimental.enable_dump_debug_info()`](https://www.tensorflow.org/api_docs/python/tf/debugging/experimental/enable_dump_debug_info)
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For a detailed walkthrough of the GUI TensorBoard Debugger V2 Plugin, see
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[Debugging Numerical Issues in TensorFlow Programs Using TensorBoard Debugger
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V2](https://www.tensorflow.org/tensorboard/debugger_v2).
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## Known issues and limitations
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1. Using `tf.debugging.experimental.enable_dump_debug_info()` leads to
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performance penalty on your TensorFlow program. The amount of slowdown
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varied depending on whether you are using TensorFlow on CPU, GPUs, or TPUs.
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The performance penalty is the highest on TPUs, followed by GPUs, and lowest
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on CPU.
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2. `tf.debugging.experimental.enable_dump_debug_info()` is currently
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incompatible with
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[model saving/loading and checkpointing](https://www.tensorflow.org/tutorials/keras/save_and_load)
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## Legacy API for TensorFlow 1.x
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TensorFlow 1.x's execution paradigm is different from that of TensorFlow v2; it
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is based on the deprecated
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[`tf.Session`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/Session)
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If you are using TensorFlow 1.x, you can use the deprecated
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`tf_debug.LocalCLIDebugWrapperSession` wrapper for `tf.Session`
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to inspect tensor values and other types of debug information in a
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terminal-based command-line interface. For details, see
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[this blog post](https://developers.googleblog.com/2017/02/debug-tensorflow-models-with-tfdbg.html).
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