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

by alextp on 10/31/17, 5:55 PM with 35 comments

• by alextp on 10/31/17, 6:13 PM

  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.

• by chrisprobert on 10/31/17, 7:06 PM

  Announcing TensorFlow's new development roadmap mandate: copy everything PyTorch is doing :-)

• by congerous on 10/31/17, 10:37 PM

  TensorFlow: everything to all people.

  Eager is actually not as innocent as "open-source projects borrowing the best parts from each other", as some commenters here suggest.

  Google is attempting to dominate the machine-learning API and the Python ecosystem for scientific computing.

  The company that controls the API influences which apps are built on it and how. Think about how Google bundled Android services on top of Android, and how that posed an existential threat to other companies. That's what's coming for TensorFlow. Many developers are too naive to realize it, or too short-sighted to care.

• by sandGorgon on 10/31/17, 7:03 PM

  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 tensorflow and nobody could figure it out [Case #13619720].