Exactly, so I suspect that that's why they have designed the interface the way they have.
Thanks, -- Raul On Thu, May 25, 2017 at 1:24 PM, Skip Cave <[email protected]> wrote: > Latency probably isn't an issue with TensorFlow model training, which can > take hours or even days for a training run. Model execution latency could > be an issue in some applications, but often getting the answer in an hour > or so is acceptable for high-value answers which can't be obtained using > other methods > > Skip > > On May 25, 2017 11:41 AM, "Raul Miller" <[email protected]> wrote: > >> Their API looks.... overly complicated, right now. >> >> https://www.tensorflow.org/api_docs/ >> >> They do not even document the network API - instead, they document >> wrappers for that API for a few programming languages. So you'd have >> to take apart one of the wrappers (or add another layer of crud over >> top one of those wrappers - python, probably, since that's the only >> one they think is stable). >> >> My guess is that latency is high enough that they need this level of >> indirection to deflect people inclined to complain about such things. >> >> -- >> Raul >> >> >> On Thu, May 25, 2017 at 11:37 AM, Skip Cave <[email protected]> >> wrote: >> > Google has released the latest version of TensorFlow, their open-source >> > machine learning package at their recent Google I/O event. TensorFlow >> runs >> > on Android, iOS, Raspberry Pi and both Google and AWS cloud services, >> using >> > the same API. TensorFlow supports a wide range of CPUs, GPUs., and now >> TPUs >> > (Tensor Processing Units). Tensor is Google's name for vectors and >> > matrices. Google provides TensorFlow API support in four languages: >> Python, >> > C++, Java, & Go. Other groups have ported the TensorFlow API to Haskell, >> > Julia, C#, and R. TensorFlow is designed to hide the concurrent nature >> of >> > the underlying processes, so large numbers of parallel processes can be >> > treated as a single process at the top-level TensorFlow API. >> > >> > Check out the TensorFlow overview video given at Google I/O last week (36 >> > minutes) on YouTube at: >> > https://www.youtube.com/watch?v=OzAdKMPgUt4&list= >> TLGGqXCgIcW-mFUyNDA1MjAxNw >> > >> > Check out the basic TensorFlow machine learning formula at 18:36 in the >> > video. This works as a line of J code (getting rid of the square >> brackets, >> > and adding parenthesis to override J's right-to-left execution). >> > >> > Imagine! You can debug a machine-learning application on one's PC or >> > smartphone using a test dataset. Then you run the compute-intensive >> > big-data training in the cloud, running on dozens (or hundreds) of GPUs, >> or >> > now TPUs. Finally, one can run the trained models back on a local >> machine, >> > or keep the model execution in the cloud, if the model still needs lots >> of >> > processing. >> > >> > This seems like a perfect fit for a J TensorFlow library. Unfortunately, >> > implementation of such a library is way above my J skill level. >> > >> > Skip Cave >> > Cave Consulting LLC >> > ---------------------------------------------------------------------- >> > For information about J forums see http://www.jsoftware.com/forums.htm >> ---------------------------------------------------------------------- >> For information about J forums see http://www.jsoftware.com/forums.htm > ---------------------------------------------------------------------- > For information about J forums see http://www.jsoftware.com/forums.htm ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
