[Solved]: Deep Learning of Darknet on GPU and error “CUDA Error: invalid device ordinal darknet: ./src/cuda.c:36: check_error: Assertion `0′ failed.”
When after compilation darknet with GPU=1 option, You received error during execution darknet command like below:
CUDA Error: invalid device ordinal
darknet: ./src/cuda.c:36: check_error: Assertion `0′ failed.
Abandon (core dumped)
that means that You compiled sources with invalid arch attribute.
For instance existing configuration:
for RTX 2080 TI Dual is incorrect and destination valid arch parameter is:
In makefile You should place arch parameter according valid architecture related to Your GPU specification. Below is listed valid parameters:
Sample flags for generation on CUDA 7 for maximum compatibility:
–arch= –gencode=arch=compute_20,code=sm_20 \
–gencode=arch=compute_30,code=sm_30 \
–gencode=arch=compute_50,code=sm_50 \
–gencode=arch=compute_52,code=sm_52 \
–gencode=arch=compute_52,code=compute_52
Sample flags for generation on CUDA 8 for maximum compatibility:
–arch= –gencode=arch=compute_20,code=sm_20 \
–gencode=arch=compute_30,code=sm_30 \
–gencode=arch=compute_50,code=sm_50 \
–gencode=arch=compute_52,code=sm_52 \
–gencode=arch=compute_60,code=sm_60 \
–gencode=arch=compute_61,code=sm_61 \
–gencode=arch=compute_61,code=compute_61
Sample flags for generation on CUDA 9 for maximum compatibility with Volta cards:
–arch= –gencode=arch=compute_50,code=sm_50 \
–gencode=arch=compute_52,code=sm_52 \
–gencode=arch=compute_60,code=sm_60 \
–gencode=arch=compute_61,code=sm_61 \
–gencode=arch=compute_70,code=sm_70 \
–gencode=arch=compute_70,code=compute_70
Sample flags for generation on CUDA 10 for maximum compatibility with Turing cards:
–arch= –gencode=arch=compute_50,code=sm_50 \
–gencode=arch=compute_52,code=sm_52 \
–gencode=arch=compute_60,code=sm_60 \
–gencode=arch=compute_61,code=sm_61 \
–gencode=arch=compute_70,code=sm_70 \
–gencode=arch=compute_75,code=sm_75 \
–gencode=arch=compute_75,code=compute_75
