3 ms·

> So, in my view, TensorFlow chose the wrong substrate for their "more efficient" library. Instead, they should have developed their own language, where the who

• codetrotter 8y ago
  > You just described Swift for TensorFlow This one? https://www.tensorflow.org/api_docs/swift/ https://www.tensorflow.org/api_docs/swift/
• SalimoS 8y ago
  isn't work in progress ? https://github.com/tensorflow/swift/blob/master/docs/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/WhySwif...
• amelius 8y ago
  Well, apparently :) They say: We believe that machine learning tools are so important that they deserve a first-class language and a compiler. However, I'd like to see some numbers on how more efficient it is to build a graph in advance, given that the lion's share of the computations will be in tensor math anyway (which can be heavily optimized, and is independent of the graph).
  • liuliu 8y ago
    The problem is that if you build the graph as-you-go, dataflow graph optimizations cannot be done efficiently (some high level optimizations such as data layout optimization, automatic data / model parallelism etc.). Swift can do all these because the compiler can extract the graph out ahead of time.