
Tensorflow v1.2 released - yuanchuan
https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.md
======
jboggan
"RNNCell objects now subclass tf.layers.Layer. The strictness described in the
TensorFlow 1.1 release is gone: The first time an RNNCell is used, it caches
its scope. All future uses of the RNNCell will reuse variables from that same
scope. "

I'm so glad they fixed this, I've been running 1.0 for the last few months
because the 1.1 release broke their own RNN tutorials and a lot of seq2seq
code that is out there. I really, really love Tensorflow and understand it is
a fast moving project but I hope they do more regression testing on their
example code in the future. This is an exciting release though!

~~~
cmarschner
What is exciting about it, do you think?

~~~
jboggan
Other than unbreaking the feature I use most often? Haha, I think the new
versions of TensorBoard and the SavedModel CLI are great for getting a better
sense of what is going on under the hood. But I'm just generally excited by
the framework hitting new releases, clearing bugs, and becoming more mature.

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freefrancisco
Did they explain why they decided to stop supporting GPU for Mac OS X? That's
going to make a lot of developers think twice before upgrading.

~~~
tempay
> TensorFlow 1.1.0 will be the last time we release a binary with Mac GPU
> support. Going forward, we will stop testing on Mac GPU systems. We continue
> to welcome patches that maintain Mac GPU support, and we will try to keep
> the Mac GPU build working.

Sounds like a lack of external contributors maintaining it to me, are there
really that many users? Everyone I know on macOS uses docker (or some other
virtualisation) to run linux for small jobs and then connects remotely to
linux boxes when they need more computing power.

~~~
matt4077
Officially, there shouldn't be very many people for whom it's relevant. The
last Macs with Nvidia GPUs were sold around 2011 if I remember correctly.

Unofficially, there may be some people using Hackitoshs with rather beefy GPUs
for machine learning.

There's a lot you can do easily on a $500 GPU that should take too long on
CPU. And I prefer the shorter write/run/debug loop of working locally. It's
the same niche other machine learning workstations fill, only with the
preferred desktop OS.

There will also be external GPUs for Macs soon(ish), and those would be
perfect for tensorflow. I'm not sure at that point they'll want it running on
Macs again, and discontinuing support now may be the wrong decision.

~~~
swang
My late 2013 MBP has a Nvidia 750M

~~~
tomjakubowski
Doing a little binary search on Everymac, it seems that the last new MBP model
with Nvidia graphics was mid-2014. Both offerings with discrete graphics from
mid-2015 had AMD cards. I'm also fairly sure you could still buy those
mid-2014 MBPs well into 2015; I distinctly remember seeing both AMD and Nvidia
MBPs available in the online store around that time.

[http://www.everymac.com/systems/apple/macbook_pro/specs/macb...](http://www.everymac.com/systems/apple/macbook_pro/specs/macbook-
pro-core-i7-2.8-15-dual-graphics-mid-2014-retina-display-specs.html)

------
jonbaer
Note: As of version 1.2, TensorFlow no longer provides GPU support on Mac OS
X.

~~~
jswny
Is there any explanation for why they decided to do this? I would imagine they
just don't have the means to test on Mac anymore but I'd like to know why for
sure.

~~~
Houshalter
Macs don't have Nvidia GPUs and tensorflow is only supported on Nvidia.

~~~
dbecker
I have an MBP and an iMac, both of which came with nvidia GPU's. The MBP is
older, but I doubt either of these are atypical machines out there today.

~~~
mwfunk
They probably are atypical systems for people using TensorFlow professionally
though.

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startupdiscuss
Direct link to the list of differences:

[https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.m...](https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.md)

~~~
sctb
Thanks! We've updated the link from the homepage.

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claudiug
Can someone explain why should I pick this over scikit? I don't have any ML
exp. I found ML quite magically :/ and totally difficult to start if you don't
have a phd in mathematics

~~~
matt4077
Scikit doesn't support GPUs, which makes it infeasible to run the sort of deep
learning stuff that's currently making waves. The competitors to tensorflow
are torch, caffe, and maybe Microsoft's CN(something, but not "Y")K.

To get started, keras is an excellent library that's build on top of
tensorflow and has recently become an official part of it.

~~~
skynode
Microsoft CNTK. Originally Computational Network Toolkit. Abbreviation remains
but now Microsoft _Cognitive Toolkit_.

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maxpert
I wonder when would they start supporting OpenCL :( I want to use my Radeon
GPU

~~~
Capt-RogerOver
While direct support from the creators of TF would be the beste thing, be sure
to check out all the addon options, like [https://github.com/hughperkins/tf-
coriander](https://github.com/hughperkins/tf-coriander)

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wonderous
Site is desktop only:

"Oops. Since this experiment loads over 14,000 bird sounds, you'll need to
view it on a desktop computer."

~~~
anonfunction
I think you wanted to comment on
[https://news.ycombinator.com/item?id=14577014](https://news.ycombinator.com/item?id=14577014)

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mk321
What about Java?

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davidf18
Thank you for the release! There is an submitted issue because the Intel MKL
support does not work with Mac OS X, only Linux.

There should be some way of doing this manually. Any ideas?

~~~
matheist
I've managed to compile tensorflow with MKL on Mac OS X. The ingredients were
roughly:

1\. Download MKL from Intel's website, install to /usr/local/lib/

2\. Change tensorflow's configure script to look for the downloaded library on
OS X instead of just aborting.

3\. Possibly change some other bazel build files to look for .dylib instead of
.so files.

4\. Build with extra flags to look for the appropriate libraries.

I'm not sure if all these steps are necessary but they were sufficient.

The reason I had install to /usr/local/lib/ instead of Intel's suggestion of
/opt/intel/something was that, with the latter, even though I passed the
appropriate directory to the linker, I think there was still some intermediate
binary that wasn't seeing that path. Putting the dylibs in the default
directory solved that.

I can't contribute my patch because I did this on my employer's computer and
it'd be an enormous hassle to work out the licensing stuff.

