cupy.random.random(size=(x.shape,1)). I would appreciate any advice. Thanks in advance.
x.sizework for you?
If you are installing CuPy and cuTENSOR from conda-forge, we notice an binary incompatibility issue. For the time being, please limit the cuTENSOR version as follows:
conda install -c conda-forge cupy cutensor=1.2 ...We are working on a proper fix and will announce here once it is done.
@/all This is now fixed.
conda install -c conda-forge cupy cutensor ... will work just fine (with cuTENSOR 1.2). The support of cuTENSOR 1.3 in conda-forge is ongoing.
cupyx.scipy.sparse.linalg.*enhancements, and more!
:tada: Released CuPy v9.4.0 & v10.0.0b2!
This release includes support for NVIDIA's CUDA Python (docs, repo), AMD ROCm 4.3, and many more distributions in
cupy.random.Generator thanks to GSoC student @povinsahu1909!
Read to the release notes for the full changes:
pip install cupy-cuda*** -f https://github.com/cupy/cupy/releases/tag/v10.0.0b2(see cupy/cupy#5671 for more info.)
ndarrays of sizes
M, D, Dand
N, D, D, what would you recommend for efficiently computing
cp.matmul(A[i], B[j])for all
j. I have already thought of looping through and expanding
A[i]and multiplying by
B. Any input would be welcome ! Thanks !