5 ms·

Tensorflow 1.5.0 release canidate includes “eager execution”

42 points by supermdguy 9y ago

CardenB 9y ago
What is eager execution?
- euphoria 9y ago
  https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo... > Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run().
  - Waterluvian 9y ago
    This sounds like it would make mucking about with TensorFlow in the REPL far easier.
    - atupis 9y ago
      And debuging. Usually with tensorflow project I found my self at debuging hell where I really dont have any clue where the buggy line is.
  - CardenB 9y ago
    This is great! No more constant `eval` spam :)
- forgot-my-pw 9y ago
  Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run(). https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...
- deleted 9y ago
  [deleted]
- asafira 9y ago
  I definitely recommend reading the documentation for that info: https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo... It's actually pretty clear (to anyone that has used TensorFlow)
- stablemap 9y ago
  Here’s the announcement from October, with examples and links to more documentation. It feels like a response to PyTorch. https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html https://research.googleblog.com/2017/10/eager-execution-impe...
IronWolve 9y ago
Good to see it, been running tf-nightly, cuda 9.1 nvidia gpu drivers with cuda 8. The entire dependency issues between drivers/apps/libraries have been incredibly annoying.
- Svenstaro 9y ago
  Some Linux distro package modern versions of tensorflow, cuda, cudnn and nvidia which all work in tandem which makes this a whole lot less annoying to deal with.
NelsonMinar 9y ago
Good news for me: "Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs." I just this morning gave up trying to compile Tensorflow from sources with AVX. I'm sure I could do it given the time but after 5 minutes it was clearly a more complicated project. Shame it may break older CPUs though, I wonder what the challenge is in releasing two versions?
- danieljh 9y ago
  Here is how I build architecture optimized TensorFlow 1.4 binaries for specific AWS instance types: https://gist.github.com/daniel-j-h/b410dd21d4949a54c83d0c3864437f0c https://gist.github.com/daniel-j-h/b410dd21d4949a54c83d0c386... The build script needs slight adaptions between major TensorFlow releases; otherwise works great for me so far. During boot time you can then figure out the instance architecture you are and fetch the appropriate wheel. Hope it helps.

42 points by supermdguy 9y ago

CardenB 9y ago

What is eager execution?

euphoria 9y ago
https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo... > Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run().
- Waterluvian 9y ago
  This sounds like it would make mucking about with TensorFlow in the REPL far easier.
  - atupis 9y ago
    And debuging. Usually with tensorflow project I found my self at debuging hell where I really dont have any clue where the buggy line is.
- CardenB 9y ago
  This is great! No more constant `eval` spam :)
forgot-my-pw 9y ago
Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run(). https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...
deleted 9y ago
[deleted]
asafira 9y ago
I definitely recommend reading the documentation for that info: https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo... It's actually pretty clear (to anyone that has used TensorFlow)
stablemap 9y ago
Here’s the announcement from October, with examples and links to more documentation. It feels like a response to PyTorch. https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html https://research.googleblog.com/2017/10/eager-execution-impe...

IronWolve 9y ago

Good to see it, been running tf-nightly, cuda 9.1 nvidia gpu drivers with cuda 8. The entire dependency issues between drivers/apps/libraries have been incredibly annoying.

Svenstaro 9y ago
Some Linux distro package modern versions of tensorflow, cuda, cudnn and nvidia which all work in tandem which makes this a whole lot less annoying to deal with.

NelsonMinar 9y ago

Good news for me: "Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs." I just this morning gave up trying to compile Tensorflow from sources with AVX. I'm sure I could do it given the time but after 5 minutes it was clearly a more complicated project. Shame it may break older CPUs though, I wonder what the challenge is in releasing two versions?

danieljh 9y ago
Here is how I build architecture optimized TensorFlow 1.4 binaries for specific AWS instance types: https://gist.github.com/daniel-j-h/b410dd21d4949a54c83d0c3864437f0c https://gist.github.com/daniel-j-h/b410dd21d4949a54c83d0c386... The build script needs slight adaptions between major TensorFlow releases; otherwise works great for me so far. During boot time you can then figure out the instance architecture you are and fetch the appropriate wheel. Hope it helps.