Does have a performance benefits over 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/ && \
    echo "/usr/lib/aarch64-linux-gnu/tegra-egl" >> /etc/

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, can I have same benefits what like

Hi, doesn’t support Jetson platform.


What supprt can I expect by using over performance improvement described in this document?
I want to know I can code and tset my CUDA program both on in my desktop computer and on 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 in Jetson at production stage.


This is a compatible issue with the package and underlying GPU driver. 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.


1 Like

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.