nvcr.io/nvidia/l4t-cuda have a performance benefits over
docker.io/nvidia/cuda on jetson? or just extra pre-installed libraries (cudnn or opencv)?
I can see that l4t-cuda does:
Dockerfile.l4t_r34 · master · nvidia / container-images / l4t-base · GitLab
RUN echo "/usr/lib/aarch64-linux-gnu/tegra" >> /etc/ld.so.conf.d/nvidia-tegra.conf && \
echo "/usr/lib/aarch64-linux-gnu/tegra-egl" >> /etc/ld.so.conf.d/nvidia-tegra.conf
Does it make any performance improvement or enable tegra specific features?
I can find
/use/lib/aarch64-linux/gnu/tegra also in
If I add ldconf path in the
docker.io/nvidia/cuda, can I have same benefits what like
docker.io/nvidia/cuda doesn’t support Jetson platform.
What supprt can I expect by using
docker.io/nvidia/cuda? performance improvement described in this document?
I want to know I can code and tset my CUDA program both on
docker.io/nvidia/cuda in my desktop computer and on
nvcr.io/nvidia/l4t-cuda in Jetson without code change.
Is there any compaitibility issue (I don’t concern minor performance issue at this stage)?
The program will be compiled and deployed on
nvcr.io/nvidia/l4t-cuda in Jetson at production stage.
This is a compatible issue with the package and underlying GPU driver.
docker.io/nvidia/cuda only support x86 and ARM CPU servers.
But the CUDA API is the same.
The same user-space app is expected to work on both containers.
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