I have installed docker and nvidia-docker v1 regarding the nvidia-docker guide on github. Guide says that, after installation, usage should be like this:
nvidia-docker run --rm nvidia/cuda nvidia-smi
what is that command doing? is it installing the latest version of cuda and nvidia driver? Because my OS has nvidia driver installed already. Also, because I will use DIGITS for the jetson/redtail project, I need cuda 8.0. By the way my system has cuda 9.0. Could you please explain this issue?
thanks for the explanation. I will dig into these topics. But I have an another question before going. If I uninstall Cuda 9.0 that my system has and reinstall an older version e.g cuda 8.0, will nvidia-docker install all related stuff into container, such as cuda 8.0. I am trying to say that does nvidia-docker pull what I have installed on my host system? In my case, nvidia-driver and cuda-x.y
I am new to docker. For a starter, which base-image I should be using for deploying a precompiled cuda binary (cuda 8/9 with cudart statically linked)? nvidia/driver or nvidia/cuda-x.x ? or just ubuntu?
I suppose if one wants to dynamically build the binary from source code, one would need nvidia/cuda because the needs for nvcc. However, for deploying a precompiled executable with static cudart, I am wondering if I should just need the driver? or nothing and rely on the driver installed on the host?
I found that nvidia/cuda-9.0 base image is 170MB, nvidia/driver:396.37-ubuntu16.04 is 750MB. Is it true that if I derive my image from nvidia/driver, a user may run my program without needing a driver locally? what the recommended container setup for deploying a cuda application?