Comments (2)
kashif
commented on July 30, 2024
@invisibleroads for that you might want to uncomment the CMakeList.txt line and add the compute capability of your card. As far as I remember for 610M it is 2.0 or 2.1?
list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_20,code=sm_21)from cuda-convnet.
invisibleroads
commented on July 30, 2024
Aha, I confirm that using the following configuration in CMakeLists.txt results in convnet.py running successfully on GeForce 610M. Thank you so much. You know the answers!
list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_20,code=sm_20)
#list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_30,code=sm_30)
#list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_32,code=sm_32)
#list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_35,code=sm_35)
#list(APPEND CUDA_NVCC_FLAGS -gencode arch=compute_50,code=sm_50)
from cuda-convnet.
Related Issues (14)
- Where to put CONVNET_RANDOM_SEED ? HOT 1
- osx HOT 24
- Weight array Access HOT 1
- Import Error When run convnet.py HOT 15
- Can it be compiled without GPU support? HOT 1
- [noccn] Make batch File wont work on 128 and 256 images HOT 1
- error on osx HOT 2
- Import error convnet
- various syntax errors while trying to make
- Error rate reached : logprob: 0.066761, 0.002564 is it ok or smth went wrong ?
- Why should we double the number of outputs when using dropout? HOT 1
- error when I tried this code in linux... HOT 7
- Result
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from cuda-convnet.