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TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning
unpublished
Debuggability is important in the development of machine-learning (ML) systems. Several widely-used ML libraries, such as TensorFlow and Theano, are based on dataflow graphs. While offering important benefits such as facilitating distributed training, the dataflow graph paradigm makes the debugging of model issues more challenging compared to debugging in the more conventional procedural paradigm. In this paper, we present the design of the TensorFlow Debugger (tfdbg), a specialized debugger
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