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Tensorflow v1.2 released

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Re: Tensorflow v1.2 released

#21

Post by matt4077 »

claudiug wrote: post #12

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

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.

Re: Tensorflow v1.2 released

#22

Post by matt4077 »

davidf18 wrote: post #10

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?

Are 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.

Re: Tensorflow v1.2 released

#23

Post by jasperkoops »

claudiug wrote: post #12

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

Scikit 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.

Re: Tensorflow v1.2 released

#24

Post by Houshalter »

jswny wrote: post #8
jonbaer wrote: post #3

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

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.

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

Re: Tensorflow v1.2 released

#25

Post by jbmorgado »

freefrancisco wrote: post #7

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.

I think because now they have AMD GPUs for some years.

I have a MacBook Pro from 2013 and I think it was the one before the last to have a Nvidia GPU.

Also, it's always an headache for me to get CUDA to work with Python/R libraries anytime I update the system and I have yet to find a straightforward guide that tells me exactly the steps to take... they all fail at some place.

Re: Tensorflow v1.2 released

#26

Post by swang »

matt4077 wrote: post #19
tempay wrote: post #14

Earlier quoted context omitted.

> 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)…

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 oth…

My late 2013 MBP has a Nvidia 750M

Re: Tensorflow v1.2 released

#27

Post by [deleted] »

apetresc wrote: post #5
jonbaer wrote: post #3

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

To 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…

[deleted]

Re: Tensorflow v1.2 released

#28

Post by RockyMcNuts »

claudiug wrote: post #12

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

TensorFlow 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.

Re: Tensorflow v1.2 released

#29

Post by dbecker »

Houshalter wrote: post #24
jswny wrote: post #8

Earlier quoted context omitted.

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.

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

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.

Re: Tensorflow v1.2 released

#30

Post by probably_wrong »

jboggan wrote: post #6

"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 r…

I 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

[2] https://github.com/tensorflow/tensorflow/issues/8191

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