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Tensorflow sucks

nicodjimenez.github.io

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Re: Tensorflow sucks

#31

Post by gormanc »

throw847333 wrote: post #28

A few more for your selection, - Bloated build system that is near impossible to get working - who even uses maven ?! Pytorch/Caffe are super-simple to build in comparison; with Chainer, it's even simple: all you need is pip install (even on exotic ARM devices). - The benefits of all that static analysis simply aren't there. In addition, PyTorch has a jit-compiler which one can argue lets one have their cake and eat…

I love that PyTorch kind of went all-in with anaconda. Building it is so much easier than TF! I'm a recent convert but it's dang good.

Re: Tensorflow sucks

#32

Post by 0xbear »

throw847333 wrote: post #28

A few more for your selection, - Bloated build system that is near impossible to get working - who even uses maven ?! Pytorch/Caffe are super-simple to build in comparison; with Chainer, it's even simple: all you need is pip install (even on exotic ARM devices). - The benefits of all that static analysis simply aren't there. In addition, PyTorch has a jit-compiler which one can argue lets one have their cake and eat…

TF doesn’t use Maven. Also, its build system lets you build it for a lot of different platforms in a uniform fashion, and speeds up the development cycle due to fast incremental builds and built in incremental, parallel, multi-language test support.

Re: Tensorflow sucks

#33

Post by Marat_Dukhan »

ffriend wrote: post #30
Marat_Dukhan wrote: post #26

Earlier quoted context omitted.

Have you looked at ONNX? It is a neural network exchange format that, in particular, lets you deploy PyTorch models in production via Caffe2. Here is a tutorial: http://pytorch.org/docs/master/onnx.html Disclaimer: I work on Caffe2 team (not on ONNX, though)

If I want to export my own computational graph to ONNX, what is the first place I should look at? Do you know about any documentation or reference implementation of the format?

Reference specification and validator are hosted in https://github.com/onnx/onnx

Re: Tensorflow sucks

#34

Post by gormanc »

Hell, being able to effortlessly switch between PyTorch and Numpy/SciPy/sklearn/skimage has been so helpful for the project I'm working on. That and I have tensors in later layers whose shapes depend on the training of the previous layers.

Re: Tensorflow sucks

#35

Post by throw65554 »

0xbear wrote: post #32
throw847333 wrote: post #28

A few more for your selection, - Bloated build system that is near impossible to get working - who even uses maven ?! Pytorch/Caffe are super-simple to build in comparison; with Chainer, it's even simple: all you need is pip install (even on exotic ARM devices). - The benefits of all that static analysis simply aren't there. In addition, PyTorch has a jit-compiler which one can argue lets one have their cake and eat…

TF doesn’t use Maven. Also, its build system lets you build it for a lot of different platforms in a uniform fashion, and speeds up the development cycle due to fast incremental builds and built in incremental, parallel, multi-language test support.

Very well, Bazel then. I'm sure it's all the rage in the cube-farms, but that doesn't necessarily make one's life easier.

Re: Tensorflow sucks

#36

Post by throw65554 »

amelius wrote: post #2

Personally, I hate it that all these libraries are so much geared towards neural networks. Why can't we just have compute networks that can be used for anything, from computational linear algebra to deep learning? > Let’s be honest, when you have about half a dozen open source high-level libraries out there built on top of your already high-level library to make your library usable, you know something has gone terrib…

PyTorch and Chainer take this approach.

CuPy is even designed to be drop-in compatible with Numpy; I don't think there is much support for LAPACK routines at this point though.

Doing MATLAB-esque work with tensorflow would require some special tolerance for pain, or would require one to be a masochist.

Re: Tensorflow sucks

#37

Post by homerowilson »

nnfy wrote: post #10
amelius wrote: post #2

Personally, I hate it that all these libraries are so much geared towards neural networks. Why can't we just have compute networks that can be used for anything, from computational linear algebra to deep learning? > Let’s be honest, when you have about half a dozen open source high-level libraries out there built on top of your already high-level library to make your library usable, you know something has gone terrib…

Have you looked at the APIs for any of these libraries? I think there is a perception that these are all for NN only because NN is the hot topic that everyone is jumping on noe; but tf for example is a general matrix manipulation library with a bunch of amazing, extra NN stuff shipped alongside it.

This is a good observation! Tensorflow is a practical and flexible dataflow implementation. See for instance Greta, https://github.com/gavinsimpson/greta -- a framework for Bayesian stats built on tensorflow.

Re: Tensorflow sucks

#38

Post by Arcsech »

j2kun wrote: post #20
fujipadam wrote: post #16

Don't flame me but how does tensorflow compare with Azure machine learning platform. For me it provides a great platform for practitioners

I think these are not comparable. Tensorflow is a software library, Azure is a compute environment which allows one to run, among many other libraries, tensorflow implementations of ML models.

I think GP is referring to Azure Machine Learning Studio[1], which does seem like it might be comparable to TF. That said, I don't know enough about either to answer their question.

1: https://studio.azureml.net

Re: Tensorflow sucks

#39

Post by agibsonccc »

edanm wrote: post #27
boulos wrote: post #19

Earlier quoted context omitted.

Don't take this the wrong way, but 20M images per month is only about 20 qps or so (20M / 3600 / 730 => 7.6 qps, but that's unrealistically even). That's pretty manageable even on a single box, depending on the network and the size of the box. When people say they're concerned about a system in production, they often mean when needing to roll it out to many machines due to higher scalability requirements. Disclosure:…

That's a fair point, but just as a counterpoint, for plenty of people, 20M per month is a lot of traffic, and it's good and valid to hear about experiences of people putting PyTorch into production with this scale.

"Production" tends to be an overloaded term. Arguments like this tend to lead to "My production is bigger than your production!" which is frankly silly.

You could mean large scale, real time, "small batch job with online lookups from a CRUD database",..

Let's just admit it's use case specific and move on.

Re: Tensorflow sucks

#40

Post by agibsonccc »

Marat_Dukhan wrote: post #26
0xbear wrote: post #6

Earlier quoted context omitted.

In my experience (computer vision, deep learning) PyTorch is substantially faster as well, especially in data augmentation where it’s not just a thin layer over cudnn. That said, you’re right. There’s no way I’d deploy it to production.

Have you looked at ONNX? It is a neural network exchange format that, in particular, lets you deploy PyTorch models in production via Caffe2. Here is a tutorial: http://pytorch.org/docs/master/onnx.html Disclaimer: I work on Caffe2 team (not on ONNX, though)

We are taking a pretty hard look at ONNX right now. It doesn't support a lot of ops yet. I would add that it's very early for supporting anything "real" yet.
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