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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?



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/


Now I'm wondering if anybody wrote a DL library on top of CuPy, and if such library could be competitive with PyTorch in terms of performance.


Yes, it is called Chainer and it's quite competitive.




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