Google brings 45 teraflops tensor flow processors to its compute cloud

Google brings 45 teraflops tensor flow processors to its compute cloud  #ai

  • Google has developed its second-generation tensor processor—four 45-teraflops chips packed onto a 180 TFLOPS tensor processor unit (TPU) module, to be used for machine learning and artificial intelligence—and the company is bringing it to the cloud.
  • The new TPUs are optimized for both workloads, allowing the same chips to be used for both training and making inferences.
  • Quite how floating point performance maps to these integer workloads isn’t clear, and the ability to use the new TPU for training suggests that Google may be using 16-bit floating point instead.
  • But as a couple of points of comparison: AMD’s forthcoming Vega GPU should offer 13 TFLOPS of single precision, 25 TFLOPS of half-precision performance, and the machine-learning accelerators that Nvidia announced recently—the Volta GPU-based Tesla V100—can offer 15 TFLOPS single precision and 120 TFLOPS for “deep learning” workloads.
  • Microsoft has been using FPGAs for similar workloads, though, again, a performance comparison is tricky; the company has performed demonstrations of more than 1 exa-operations per second (that is, 1018 operations), though it didn’t disclose how many chips that used or the nature of each operation.

Up to 256 chips can be joined together for 11.5 petaflops of machine-learning power.
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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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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

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  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

Visually Linking #AI, #MachineLearning, #DeepLearning, #BigData and #DataScience @gilpress

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  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

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  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

Visually Linking #AI, #MachineLearning, #DeepLearning, #BigData and #DataScience

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  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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GitHub

  • The tensorflow package will be built against the default version of python found in the PATH .
  • The tensorflow package provides access to the complete TensorFlow API from within R.
  • The tensorflow package provides code completion and inline help for the TensorFlow API when running within the RStudio IDE.
  • The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs.

tensorflow – TensorFlow for R
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