Manual indexing with multidimensional cupy ndarray in user defined kernels
In the cupy docs on user defined kernels (https://docs.cupy.dev/en/stable/user_guide/kernel.html), there is a section defining certain variables that are predefined, like _ind.size() and i for things like manual indexing in element-wise kernels.
AttributeError: module ‘cupy’ has no attribute ‘ctypeslib’
Trying to import from numpy.ctypeslib import ndpointer but from cupy.
AttributeError: module ‘cupy’ has no attribute ‘ctypeslib’
Trying to import from numpy.ctypeslib import ndpointer but from cupy.
Why does the compute sanitizer not detect leaks in CuPy kernels?
kernel = r””” extern “C” __global__ void entry0() { if (threadIdx.x == 0) malloc(16); return ; } “”” import cupy as cp raw_module = cp.RawModule(code=kernel, backend=’nvcc’, enable_cooperative_groups=True) def main(): v1 = raw_module.get_function(f”entry0″) v1((2,),(32,),(),shared_mem=0) if __name__ == ‘__main__’: print(main()) PS C:Spiral’s ML Library> compute-sanitizer –leak-check full python .backend_teststest2.py ========= COMPUTE-SANITIZER None ========= LEAK SUMMARY: 0 bytes […]
PerformanceWarning: Jitify is performing a one-time only warm-up to populate the persistent cache
I’m trying to run a custom NER spaCy model using GPU. When training the pipeline with python3 -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./train.spacy --gpu-id 0, it gives a performance warning,
When compiling Cuda modules with Cupy, how do I get the diagnostic results from the `-ptxas=v` option?
I am not sure if this even the right question to ask, but when I compile the Cuda Visual Studio example project, there is an --ptxas=v option to turn on diagnostics which prints out the local memory memory being used and such. And I think the option is responsible for instructing the nvcc compiler to output the info.