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PyTorch 1.0 is out

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Re: PyTorch 1.0 is out

#21

Post by umvi »

usgroup wrote: post #18
htfy96 wrote: post #12

What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example

i picked up c++ for some gpu stuff with the arrayfire api... felt the same. firstly, modern c++ takes no time to learn if you come from java / c/ c# / etc. secondly, things like operator overloading and type inference make for pretty seamless apis. E.g. want to add matrices? auto C = A + B. A lot of things suck (closures, generator functions, first order functions all suck in c++), but oh my does it all run fast when…

I'm not sure how I feel about operator overloading. It starts out simple enough, but can lead to extremely confusing code.

Take this snippet of an API call[1] that parses ISO strings to chrono::time_point using date[2]:

    std::istringstream in(iso_string);
    in >> date::parse("%FT%TZ", tp);
At first glace my brain cannot comprehend the second statement. Why is there an input stream going into a function? Is that even valid C++? Why not just pass `in` as a function parameter and return time_point as the return value?? When you actually dig into the source, it's a namespace overloaded operator and it's extremely heavily templated to be generic. So now if I think of `>>` as simply a second function call using the result of `date::parse` it makes sense, but... I still don't understand all of the technical reasons behind that decision. I assume they're valid reasons though since Howard Hinnant is the c++ datetime expert.

[1] https://stackoverflow.com/a/33438989/2516916

[2] https://github.com/HowardHinnant/date

Re: PyTorch 1.0 is out

#22

Post by nerdponx »

perturbation wrote: post #16
htfy96 wrote: post #12

What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example

I'm hoping that this results in a nice, high-level API for https://github.com/fragcolor-xyz/nimtorch as well, which AFAIK has been wrapping the low-level Aten API. I've been keeping my eye on that project, and been really excited about it.

Likewise, I hope this leads to a nice wrapper API for Julia.

Re: PyTorch 1.0 is out

#24

Post by nafizh »

Really grateful to the FAIR team for Pytorch. I use deep learning for computational biology. Pytorch lets me focus on the problem rather than nitpicking with the framework (looking at you tensorflow) to make something work.

Re: PyTorch 1.0 is out

#26

Post by gnulinux »

wpietri wrote: post #19

In case anybody else was wondering, since this isn't in the fine article: "PyTorch is a Python package that provides two high-level features: * Tensor computation (like NumPy) with strong GPU acceleration * Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed."

ML noob hobbyist here. Would you use PyTorch for models not involving deep neural networks too or is it just good for that. Say if I use linear models (like least squares etc) or use custom algorithms (integer linear programming, optimization, or something else...) but need very fast linear algebra support is PyTorch a good lib? I'm a C kinda guy so I usually use blas, lapack etc or numpy+pandas+sklearn in python. Would PyTorch give a "complete" enough feel or would I just use it only for nn and use other libraries for other things?

Re: PyTorch 1.0 is out

#27

Post by make3 »

l3robot wrote: post #14

TL;DR - New JIT feature that lets you run your model without python. It now seems trivial to load a pytorch model in C++ - New distributed computation package. Major redesign. - C++ frontend - New torch hub feature to load models from github easily

I love PyTorch, but my experience with jits embedded in Python (eg. Numba) has been everything but simple, nevermind trivial. I'll really have to try it to believe it.

Re: PyTorch 1.0 is out

#29

Post by p1esk »

gnulinux wrote: post #26
wpietri wrote: post #19

In case anybody else was wondering, since this isn't in the fine article: "PyTorch is a Python package that provides two high-level features: * Tensor computation (like NumPy) with strong GPU acceleration * Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed."

ML noob hobbyist here. Would you use PyTorch for models not involving deep neural networks too or is it just good for that. Say if I use linear models (like least squares etc) or use custom algorithms (integer linear programming, optimization, or something else...) but need very fast linear algebra support is PyTorch a good lib? I'm a C kinda guy so I usually use blas, lapack etc or numpy+pandas+sklearn in python. Wo…

PyTorch uses CuBLAS [1] under the hood, among other libraries, so basic linear algebra ops should be fast.

You might also look at CuPy [2], especially if you like NumPy.

[1] https://developer.nvidia.com/cublas [2] https://cupy.chainer.org/

Re: PyTorch 1.0 is out

#30

Post by dangom »

openbasic wrote: post #23

What would be a good book and project to get started with this? Object recognition? Product recommendations?

I would suggest going through the Fast.AI course [1]. It's an excellent course to learn more about DL in general, and some of torch API. The downside is that the course material was produced when PyTorch was still at 0.3, so some of the API has changed since then.

[1] https://course.fast.ai/

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