3 ms·

TensorFlow 1.11 released

55 points by jamesblonde 8y ago

jamesblonde 8y ago
The main highlight is a new version of AllReduce native to TensorFlow. This, like Horovod, builds a ring and has both native and NCCL implementations. Early results (albeit on a single host) show that it outperforms Horovod: https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/7T05tNV08Us https://groups.google.com/a/tensorflow.org/forum/#!topic/dis...
- minimaxir 8y ago
  And it looks like it's natively supported via Keras too? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/distribute https://github.com/tensorflow/tensorflow/tree/master/tensorf...
  - jamesblonde 8y ago
    Yes, and with the estimator API, too.
SloopJon 8y ago
Looks like Python 3.7 is not supported yet: https://github.com/tensorflow/tensorflow/issues/20517 https://github.com/tensorflow/tensorflow/issues/20517 Somewhat off topic: I'm rediscovering the dance required to set up a new Windows 10 laptop with Python, CUDA, and TensorFlow. If I understand correctly, pyenv does not work on Windows. Is there something similar? Also, if I installed TensorFlow in WSL, it wouldn't be able to use the GPU, would it?
- breckuh 8y ago
  Python 3.7 is supported if you build master from source. But that can be a lot of work and though it may solve your TF problem, it may create other problems with other Python packages. In my experience conda is the way to go for setting up your environment. I did not have great luck with pyenv, YMMV. I don't know about WSL. Maybe setup a dual boot with Ubuntu on your system?
- transpostmeta 8y ago
  No, CUDA is not supported by WSL. They hope they can support it at some point, though. Use Conda on Linux.
- Gimpei 8y ago
  You can use cygwin, which does work with the GPU. Not sure if that works with pyenv but it does work with pipenv...
- lostmsu 8y ago
  I am using conda environments.
breckuh 8y ago
> Add multi-GPU DistributionStrategy support in tf.keras. Users can now use fit, evaluate and predict to distribute their model on multiple GPUs. Is this different than the existing multi_gpu_model? (https://keras.io/utils/#multi_gpu_model https://keras.io/utils/#multi_gpu_model)
dairychris 8y ago
Does it still have that horrible java build dependency?

55 points by jamesblonde 8y ago

The main highlight is a new version of AllReduce native to TensorFlow. This, like Horovod, builds a ring and has both native and NCCL implementations. Early results (albeit on a single host) show that it outperforms Horovod: https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/7T05tNV08Us https://groups.google.com/a/tensorflow.org/forum/#!topic/dis...

minimaxir 8y ago
And it looks like it's natively supported via Keras too? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/distribute https://github.com/tensorflow/tensorflow/tree/master/tensorf...
- jamesblonde 8y ago
  Yes, and with the estimator API, too.

SloopJon 8y ago

Looks like Python 3.7 is not supported yet: https://github.com/tensorflow/tensorflow/issues/20517 https://github.com/tensorflow/tensorflow/issues/20517 Somewhat off topic: I'm rediscovering the dance required to set up a new Windows 10 laptop with Python, CUDA, and TensorFlow. If I understand correctly, pyenv does not work on Windows. Is there something similar? Also, if I installed TensorFlow in WSL, it wouldn't be able to use the GPU, would it?

breckuh 8y ago
Python 3.7 is supported if you build master from source. But that can be a lot of work and though it may solve your TF problem, it may create other problems with other Python packages. In my experience conda is the way to go for setting up your environment. I did not have great luck with pyenv, YMMV. I don't know about WSL. Maybe setup a dual boot with Ubuntu on your system?
transpostmeta 8y ago
No, CUDA is not supported by WSL. They hope they can support it at some point, though. Use Conda on Linux.
Gimpei 8y ago
You can use cygwin, which does work with the GPU. Not sure if that works with pyenv but it does work with pipenv...
lostmsu 8y ago
I am using conda environments.

breckuh 8y ago

> Add multi-GPU DistributionStrategy support in tf.keras. Users can now use fit, evaluate and predict to distribute their model on multiple GPUs. Is this different than the existing multi_gpu_model? (https://keras.io/utils/#multi_gpu_model https://keras.io/utils/#multi_gpu_model)

dairychris 8y ago

Does it still have that horrible java build dependency?