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CuPy – NumPy-compatible matrix library accelerated by CUDA

cupy.chainer.org

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Re: CuPy – NumPy-compatible matrix library accelerated by CUDA

#31

Post by jjoonathan »

elcritch wrote: post #30
jjoonathan wrote: post #28

Earlier quoted context omitted.

I've done a fair bit of Numba CUDA and I was so happy to throw everything out and switch to C++. NubaCUDA gave me lots of small problems and a few big ones. The poor support for debug/perf tools and poor integration with other high-level python CUDA code (FFTs in particular) sent me packing, but the number of small problems was excessive in comparison to the size of my code. I had 5 reduced bugs at the bottom of my n…

What’s the difference of NuMBA CUDA and Pytorch or similar? If you’re doing custom kernels you should take a look at the Julia library CuArray [1] and generic kernels [2]. I really like that I don’t have to dig into C++ and deal with all of the memory and kernel management. 1: https://github.com/JuliaGPU/CuArrays.jl 2: http://mikeinnes.github.io/2017/08/24/cudanative.html

My impression was that pytorch focused on linear algebra / deep learning. The reason I was playing with numbacuda in the first place was because part of my problem did not fit nicely into a (dense) linear algebra framework, so numbacuda's custom kernel support seemed attractive. Does pytorch have a good low-level kernel library? Or sparse linear algebra library?

I love Julia, but I haven't managed to convert anyone else on my team and I already spent my informal exploration budget for the GPU project on nubacuda, so JuliaGPU will have to wait for another time. I'll be sure to keep it in mind, though!

How is the CUDA debug/perf story with Julia? Does it play nice with the nvidia tooling?

Re: CuPy – NumPy-compatible matrix library accelerated by CUDA

#32

Post by elcritch »

jjoonathan wrote: post #31
elcritch wrote: post #30

Earlier quoted context omitted.

What’s the difference of NuMBA CUDA and Pytorch or similar? If you’re doing custom kernels you should take a look at the Julia library CuArray [1] and generic kernels [2]. I really like that I don’t have to dig into C++ and deal with all of the memory and kernel management. 1: https://github.com/JuliaGPU/CuArrays.jl 2: http://mikeinnes.github.io/2017/08/24/cudanative.html

My impression was that pytorch focused on linear algebra / deep learning. The reason I was playing with numbacuda in the first place was because part of my problem did not fit nicely into a (dense) linear algebra framework, so numbacuda's custom kernel support seemed attractive. Does pytorch have a good low-level kernel library? Or sparse linear algebra library? I love Julia, but I haven't managed to convert anyone e…

Ah, that makes sense. I've only dabbled a little with DNN's recently, but pytorh/tensorflow seemed very targeted toward deep learning. Generic tools seem more useful to me. What are you doing with fft's?

I haven't dug too deep with CudaNative / Cuarray to understand the state of Julia perf debugging. Though here's one post on the topic:

https://discourse.julialang.org/t/cudanative-is-awesome/1786...

In general It's been very pleasant experimenting with gpu programming in Julia. I couldn't quite grok tensorflow code, and it's cool to just declare a Julia array and send it the GPU.

Re: CuPy – NumPy-compatible matrix library accelerated by CUDA

#33

Post by Ragib_Zaman »

rahimnathwani wrote: post #25
brian_herman__ wrote: post #4

Cool I wish that other libraries like tensorflow could get cudnn installed automatically like this library!

pytorch does the same: https://news.ycombinator.com/item?id=15622763

Their website seems to imply different:

https://pytorch.org/get-started/locally/

E.g "To install PyTorch via Anaconda, and you are using CUDA 9.0, use the following conda command". If they are shipping with CUDA perhaps that should be phrased more like "and you want to use CUDA 9.0". And of course you do indeed need your own CUDA installation if you want to build PyTorch from source yourself.

Re: CuPy – NumPy-compatible matrix library accelerated by CUDA

#34

Post by oarfish »

rahimnathwani wrote: post #25
brian_herman__ wrote: post #4

Cool I wish that other libraries like tensorflow could get cudnn installed automatically like this library!

pytorch does the same: https://news.ycombinator.com/item?id=15622763

At least up until 2 months ago, you had to do it manually, i don't know if that changed.

Re: CuPy – NumPy-compatible matrix library accelerated by CUDA

#35

Post by rahimnathwani »

oarfish wrote: post #34
rahimnathwani wrote: post #25

Earlier quoted context omitted.

pytorch does the same: https://news.ycombinator.com/item?id=15622763

At least up until 2 months ago, you had to do it manually, i don't know if that changed.

When I installed pytorch, I had already installed CUDA and CUDNN (as I needed them for tensorflow) so I have not verified what smhx said.
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