And zero mention of AMD support.
Tensorflow 1.5.0
21–30 of 30 posts
Re: Tensorflow 1.5.0
#22Eager 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...
What if I have data-dependent computational graphs? For example, recursive NNs.
Re: Tensorflow 1.5.0
#23And zero mention of AMD support.
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?)
Re: Tensorflow 1.5.0
#24> Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs.
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.
Re: Tensorflow 1.5.0
#25And zero mention of AMD support.
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?)
AMD is porting TensorFlow, but these release notes give the impression that upstream is not helping in any way. https://github.com/ROCmSoftwarePlatform/hiptensorflow/blob/h...
Re: Tensorflow 1.5.0
#26Eager 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...
What if I have data-dependent computational graphs? For example, recursive NNs.
I'm a member of the team that works on eager execution.
When eager execution is enabled, you no longer need to worry about graphs: operations are executed immediately. The upshot is that eager execution lets you implement dynamic models, like recursive NNs, using Python control flow. We've published some example implementations of such models on Github:
https://github.com/tensorflow/tensorflow/tree/master/tensorf...
I'd be happy to answer other questions about eager execution, and feedback is welcome.
EDIT: Just because you don't have to worry about graphs doesn't mean that graph construction and eager execution aren't related; take a look at our research blog post for more information if you're curious about the ways in which they relate to each other (https://research.googleblog.com/2017/10/eager-execution-impe...).
Re: Tensorflow 1.5.0
#27So 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.
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...)
That promise is only for the Python API, not C++/Java.
Re: Tensorflow 1.5.0
#28Earlier quoted context omitted.
What if I have data-dependent computational graphs? For example, recursive NNs.
I'm a member of the team that works on eager execution. When eager execution is enabled, you no longer need to worry about graphs: operations are executed immediately. The upshot is that eager execution lets you implement dynamic models, like recursive NNs, using Python control flow. We've published some example implementations of such models on Github: https://github.com/tensorflow/tensorflow/tree/master/tensorf...…
Looking forward to trying out eager!
Re: Tensorflow 1.5.0
#29For 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
Re: Tensorflow 1.5.0
#30> Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs.
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.
IIRC since the beginning tensorflow has required at least sm 3.0 support (Kepler or newer). I imagine the combination of a pre-AVX cpu and Kepler or newer gpu is uncommon.