Tensorflow — Graphs and Sessions

Keshan Sodimana
Aug 12, 2018 · 3 min read
Image courtesy: tensorflow.org
  • Distributed execution. By using explicit edges to represent the values that flow between operations, it is possible for TensorFlow to partition your program across multiple devices (CPUs, GPUs, and TPUs) attached to different machines. TensorFlow inserts the necessary communication and coordination between devices.
  • Compilation. TensorFlow’s XLA compiler can use the information in your dataflow graph to generate faster code, for example, by fusing together adjacent operations.
  • Portability. The dataflow graph is a language-independent representation of the code in your model. You can build a dataflow graph in Python, store it in a SavedModel, and restore it in a C++ program for low-latency inference.
Graph generated by Tensorboard

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Keshan Sodimana

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Yet another happy soul…

Coinmonks

Coinmonks

Coinmonks is a technology-focused publication embracing decentralize technologies. We are Non-profit and education is our core value. Learn, Build and thrive. Our other project— https://coinmonks.com, https://cryptofi.co, https://coincodecap.com