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TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning

Eric Nielsen
Michael Salib
Proceedings of the Reliable Machine Learning in the Wild - NIPS 2016 Workshop (2016)


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 for ML models written in TensorFlow. tfdbg provides features to inspect runtime dataflow graphs and the state of the intermediate graph elements ("tensors"), as well as simulating stepping on the graph. We will discuss the application of this debugger in development and testing use cases.