Eager Execution: An imperative, define-by-run interface to TensorFlow

Post by alextp » Tue, Oct 31, 2017, 5:55 PM UTC

Post by josh11b » Tue, Oct 31, 2017, 6:18 PM UTC

alextp wrote: post #2

You can read out more about it in the blog post ( https://research.googleblog.com/2017/10/eager-execution-impe... ) or the README ( https://github.com/tensorflow/tensorflow/tree/master/tensorf... ). This is still a preview release, so you may hit some rough edges. Looking forward to your feedback as you try it out.

Post by chrisprobert » Tue, Oct 31, 2017, 7:06 PM UTC

Post by sandGorgon » Tue, Oct 31, 2017, 7:27 PM UTC

alextp wrote: post #6

sandGorgon wrote: post #4

Hey guys, if I could request... Please fix the serialization story for tensorflow. There 6 googleable methods to export from tensorflow and nobody knows what will work on the cloud, what can be exported from cloudml and what can be loaded on Android. It has to be consistent and there has to be one way to do it. I personally have a 10 message thread with Google cloud support on exporting a Cloud trained model to tenso…

Did you try using SavedModel? It should be seamless to use downstream with tensorflow serving and it's not that hard to get estimators to spit those out.

Post by alextp » Tue, Oct 31, 2017, 7:32 PM UTC

sandGorgon wrote: post #7

alextp wrote: post #6

Earlier quoted context omitted.

Did you try using SavedModel? It should be seamless to use downstream with tensorflow serving and it's not that hard to get estimators to spit those out.

I really wish. https://github.com/tensorflow/tensorflow/issues/12750 In fact if you dig up the case, then even official support told me that savedmodel needs some freezing using bazel otherwise it doesn't work. The github page and stackoverflow are full of these. If you can, please take the message to the other side :( I don't think the cloud guys (where training will happen in distributed mode) talk to the android g…