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Tensorflow v1.2 released
- startupdiscuss 9y agoDirect link to the list of differences: https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.md https://github.com/tensorflow/tensorflow/blob/r1.2/RELEASE.m...
- sctb 9y agoThanks! We've updated the link from the homepage.
- jonbaer 9y agoNote: As of version 1.2, TensorFlow no longer provides GPU support on Mac OS X.
- apetresc 9y agoTo be a bit more precise, the changelog says: > 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. In other words, it still works (at least for now), and they'll accept patches to keep it working - they're just not going to explicitly test for it anymore.
- deleted 9y ago[deleted]
- jswny 9y agoIs 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 9y agoMacs don't have Nvidia GPUs and tensorflow is only supported on Nvidia.
- dbecker 9y agoI 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 9y agoThey probably are atypical systems for people using TensorFlow professionally though.
- danieldk 9y agoI am not sure what the current progress on OpenCL support is, but Tensorflow's configure script definitely asks whether you want to install Tensorflow with OpenCL support.
- dgacmu 9y agoThe last Mac to ship with an NVidia GPU was the 2014 MBP. That was three years ago, and is starting to exit most companies' hardware refresh cycle. You can buy an external thunderbolt box and try putting an nvidia GPU in it, but the driver support isn't always functional. For an example of some of the hoops you have to go through, see: https://9to5mac.com/2017/04/11/hands-on-powering-the-macbook-pro-with-an-egpu-using-nvidias-new-pascal-drivers/ https://9to5mac.com/2017/04/11/hands-on-powering-the-macbook... And I'm sure you can imagine the reasons a large company wouldn't set up a Hackintosh. :) The writing is on the wall for being able to maintain a reliable test cluster, at least, for now, and broken tests are very very bad for being able to develop rapidly.
- visarga 9y agoApple puts GPUs in these things? Sorry, I thought it only put Intel Irises. /s
- jboggan 9y ago"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 9y agoWhat is exciting about it, do you think?
- jboggan 9y agoOther 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.
- probably_wrong 9y agoI wouldn't upgrade just yet if I were you, because part of the seq2seq code is still broken - more specifically, "there seems to be a problem with deepcopy of RNNCell"[1]. The bug is still open[2], and has been for a while. [1] https://stackoverflow.com/a/44594376 https://stackoverflow.com/a/44594376 [2] https://github.com/tensorflow/tensorflow/issues/8191 https://github.com/tensorflow/tensorflow/issues/8191
- freefrancisco 9y agoDid 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 9y ago> 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.
- guard0g 9y agoIt just means OSX users (like me) have to compile from source - no different from PyTorch. Happy to provide compiled binaries for 10.12, though it's a bit of a chore to get Xcode clang and CUDA to play nice together.
- matt4077 9y agoFor now–I've thought about getting involved in keeping that going, even trying to set up a travis build. But bazel, I, and C++, we simply don't enjoy the time we spend together.
- matt4077 9y agoOfficially, 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.
- wonderous 9y agoSite is desktop only: "Oops. Since this experiment loads over 14,000 bird sounds, you'll need to view it on a desktop computer."
- anonfunction 9y agoI think you wanted to comment on https://news.ycombinator.com/item?id=14577014 https://news.ycombinator.com/item?id=14577014
- davidf18 9y agoThank 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?
- matt4077 9y agoAre there any comparisons of MKL to the GPU versions? Because this appears to be an attempt by Intel to stay relevant in the field, and I'm sceptical when the hardware vendors are creating implementations that they couldn't get projects to do themselves. If you're working on Mac without an NVIDIA GPU, the best bet may be openCL. I've seen a lot of commits for that, and when it's ready I'd be surprised if even MacBook GPUs didn't run laps around CPUs.
- matheist 9y agoI'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.
- claudiug 9y agoCan 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 9y agoScikit 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 9y agoMicrosoft CNTK. Originally Computational Network Toolkit. Abbreviation remains but now Microsoft Cognitive Toolkit.
- jasperkoops 9y agoScikit is for non-deep learning machinelearning algorithms only (It does support neural networks in the latest version, but only uses the cpu to train data). TLDR: Use Tensorflow for deeplearning, use Scikit for other ML algorithms.
- RockyMcNuts 9y agoTensorFlow is low-level. scikit or sklearn is high level off-the-shelf ML, apply this algorithm to this dataset with these parameters. TFlearn is a high-level off-the-shelf library built on TensorFlow, giving you some of the benefits e.g. GPU. It's hard to get state of the art results using off-the-shelf algorithms, unless your problem is very vanilla you typically need to get under the hood and do custom hyperparameters and tuning. That's why ML competitions like Kaggle are interesting, there are so many ways to skin the cat you can't capture them all in off-the-shelf libraries. But you can get very useful results with a lot less effort using sklearn and TFlearn off-the-shelf.
- 9y ago
- maxpert 9y agoI wonder when would they start supporting OpenCL :( I want to use my Radeon GPU
- snovv_crash 9y agoAgreed. Hardware lock-in is nasty business, and with tools like HIP it shouldn't be too difficult.
- Capt-RogerOver 9y agoWhile 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
- mk321 9y agoWhat about Java?