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Preferred Networks | Tokyo or Remote in Japan | Full-time | https://www.preferred.jp/en Preferred Networks is an AI company based in Tokyo working across the stack, from AI chips a…

Preferred Networks | Tokyo or Remote in Japan | Full-time | https://www.preferred.jp/en Preferred Networks is an AI company based in Tokyo working across the stack, from AI chips a…

Preferred Networks | Tokyo or Remote in Japan | Full-time | https://www.preferred.jp/en Preferred Networks is an AI company based in Tokyo working across the stack, from AI chips a…

CuPy isn't semi-abandoned as well, obviously :)

As a maintainer of CuPy and also as a user of several GPU-powered Python libraries, I empathize with the frustrations and difficulties here. Indeed, one thing CuPy values is to mak…

NumPy has a mechanism to dispatch execution to CuPy: https://numpy.org/neps/nep-0018-array-function-protocol.html Just prepare the input on NumPy or CuPy, and then you can just fee…

cuDF is a CuPy-based library providing drop-in replacement for Pandas: https://rapids.ai/

Actually all CUDA Toolkit libs are already available through the conda-forge channel: https://anaconda.org/conda-forge/cuda-cudart , https://anaconda.org/conda-forge/libcublas , et…

CuPy tensors (or `ndarray`) provide the same semantics as NumPy. In-place operations are permitted.

For those interested in the NumPy/SciPy API coverage in CuPy, here is the comparison table: https://docs.cupy.dev/en/latest/reference/comparison.html

An excellent example of Array API usage can be found in scikit-learn. Estimators written in NumPy are now operable on various backends courtesy of Array API compatible libraries su…