Tensorflow 1.5.0

[minimaxir]

The big feature is CUDA 9 and cuDNN 7 support, which promises double-speed training on Volta GPUs/FP16. (it should be noted that TF 1.5 does not support CUDA 9.1 yet, which I found out the hard way)

I updated my Keras container with the TF 1.5 RC, CUDA 9, and cuDNN 7 (https://github.com/minimaxir/keras-cntk-docker), but did not notice a significant speed increase on a K80 GPU (I'm unsure if Keras makes use of FP16 yet either).

[jorgemf]

Speed in FP16 under k80 is almost as FP32, the architecture doesn't work well with FP16. As you said, you need Volta to notice improvements with FP16.

The other two main features are: Eager execution and TensorFlow Lite

[puzzle]

You need Pascal hardware or later for FP16. Amazon's G3 Maxwell instances, which are newer than K80s, don't support it, either.

[make3]

k80 is pretty old now, 1080ti have x5 perf of k80 for one tenth of the price

[NelsonMinar]

Eager execution is appealing for folks new to learning TensorFlow. The deferred execution style is powerful, but if you just want to tinker in a REPL it's nice to have imperative programming. https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...

[pacala]

What if I have data-dependent computational graphs? For example, recursive NNs.

[yolobey]

I've been dreading version updates ever since they dropped Mac binary support. There are always obscure things to patch I have to find out by myself, the build easily wastes a whole day.

I think I'm either going to change my workflow and use another OS or switch fully to PyTorch.

[matt4077]

I have recently compiled the -rc1 on MacOS 10.12, and the process has basically remained unchanged from 1.4. Here's a gist with my notes: https://gist.github.com/MatthiasWinkelmann/41dd42b07f8bd794a...

(I still used CUDA 8, but 9 should also work. You just need to find the version of the command line tools it works with)

[wmf]

And zero mention of AMD support.

[htsh]

Until there's something like CUDA for AMD isn't this going to be difficult? I don't know the area well, just curious.

(How good is openCL when it comes to this sort of stuff? Could they support it without crazy effort?)

[arunmandal53]

For anyone who is having trouble with the installation, here's a tutorial to install TensorFlow 1.5.0 official pre-built pip package for both CPU and GPU version on Windows and ubuntu also there is tutorial to build tensorflow from source for cuda 9.1. http://www.python36.com

[zitterbewegung]

So is it an easy process to convert a tensorflow program from 1.4 to 1.5? When I tried converting something from 0.9 to 1.0 I couldn't figure it out.

[connorgreenwell]

For most cases it should just be a drop in replacement. IIRC they promise not to break the API between point releases (except tf.contrib.* which may change or disappear entirely...)

[jorgemf]

0.9 wasn't production ready and they didn't guarantee backward compatibility until 1.0. So nothing to change from 1.4 to 1.5, maybe you will have some warnings about features that will change in the future, but it will work.

[blueyes]

> Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

[htsh]

This is primarily pre-2011 CPUs, though? Looks like everything since Sandy Bridge and Bulldozer will be okay.

I guess we never know what's running on our cloud instances.