GitHub

  • Linux GPU: Python 2 ( build history ) / Python 3.4 ( build history ) / Python 3.5 ( build history )
  • Latest commit 55b0159 Jan 1, 2017 yifeif committed on GitHub Merge pull request #6588 from terrytangyuan/run_config_flag
  • TensorFlow is an open source software library for numerical computation using data flow graphs.
  • Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them.
  • TensorFlow also includes TensorBoard, a data visualization toolkit.

tensorflow – Computation using data flow graphs for scalable machine learning
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The Good, Bad, & Ugly of TensorFlow

The Good, Bad, and Ugly of #TensorFlow. #BigData #DeepLearning #MachineLearning  #AI

  • If you are deploying a model to a cloud environment, you want to know that your model can execute on the hardware available to it, without unpredictable interactions with other code that may access the same hardware.
  • For example, the Udacity tutorials and the RNN tutorial using Penn TreeBank data to build a language model are very illustrative, thanks to their simplicity.
  • For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
  • But good programmers know it is much harder to write code that humans will use, versus code that a machine can compile and execute.
  • We appreciate their strategy of integrating new features and tests first so early adopters can try things before they are documented.

A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff) from Dan Kuster, one of indico’s deep learning researchers.
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