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CuPy is also great – makes it trivial to port existing numerical code from NumPy/SciPy to CUDA, or to write code than can run either on CPU or on GPU. I recent
by setopt 2y ago
CuPy is also great – makes it trivial to port existing numerical code from NumPy/SciPy to CUDA, or to write code than can run either on CPU or on GPU.
I recently saw a 2-3 orders of magnitude speed-up of some physics code when I got a mid-range nVidia card and replaced a few NumPy and SciPy calls with CuPy.
- 6gvONxR4sf7o 2y agoDon’t forget JAX! It’s my preferred library for “i want to write numpy but want it to run on gpu/tpu with auto diff etc”
- westurner 2y agoFrom https://news.ycombinator.com/item?id=37686351 https://news.ycombinator.com/item?id=37686351 : >> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df50c19eb7aee33c17/sympy/utilities/lambdify.py#L182 https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... : >> """Convert a SymPy expression into a function that allows for fast numeric evaluation""" [e.g. the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPy, numexpr,] sympy#20516: "re-implementation of torch-lambdify" https://github.com/sympy/sympy/pull/20516 https://github.com/sympy/sympy/pull/20516