Hello,
Is it to export customized container image supporting CUDA by adding tools such as vim, ssh, … based on official docker image, l4t-base r32.5.0 ?
I exported image and remove container, then import the image again.
The result of testing CUDA is failed. I didn’t record the details of log, sorry.
Terminal reports that no CUDA-supporting device is detected.
My testing about building flow of the customized image :
l4t-base r32.5.0 → install software in container ( Customized part. Do nothing currently. ) → export docker image → remove container → import image → run container → simple cuda sample testing → fail
It seems that this flow doesn’t work …
If it is possible, how should I do ?
Thank you in advance !!
Hi @LeoLiao, did you run the container with --runtime nvidia
? That is necessary to use CUDA inside of it.
Also, you may find adding your customizations in a Dockerfile that uses l4t-base as the base image is easier to manage. That’s what I do in these jetson-containers: GitHub - dusty-nv/jetson-containers: Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
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Hi @dusty_nv ,
I run the flow again today.
The details of log showed " Error: Only 0 Devices available. 1 requested. Existing" after I ran the cuda samples copied from the host to the container.
Finally, result of running samples is failed until I add "–gpus ‘“device=0”’ " and “–runtime nvidia” in the docker command.
Thanks for your reply.
Thanks @LeoLiao, good to know you were able to get it working. I haven’t had to use the --gpus
argument on Jetson before - does --gpus all
also work for you?
Hi @dusty_nv,
"--gpus all " also works !!
Thanks for your reply !!