I use the nvcr.io/nvidia/deepstream:5.1-21.02-base image.
**• Hardware Platform (Jetson / GPU)**GPU
• DeepStream Version 5.1
• JetPack Version (valid for Jetson only)
• TensorRT Version 7
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs)
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
I cannot run nvvc, since this is all what is in /usr/local/cuda folder:
root@f657071a0fc9:/usr/local/cuda# ls
compat include lib64 targets
Seems to be installed in the nvcr.io/nvidia/deepstream:5.1-21.02-devel image but is 13 GB which makes it completely unmanageable…
Is there a fast way of installing nvcc on base image? I cannot really understand why is not installed by default on base
base docker (contains only the runtime libraries and GStreamer plugins. Can be used as a base to build custom dockers for DeepStream applications)
one easy way was you can copy nvcc into one directory and mount that directory into docker environments, then copy nvcc to cuda bin directory if your system os same as os docker using.
but it’s not sufficient just copy nvcc for building cuda sample or deepstream sample, it have many dependencies, i just copy one bandwidth sample from cuda samples to build, need to install make, g++, also need copy cuda libraries and header files one by one.
Here is the files copied for bandwidth build,
/usr/local/cuda-11.1/targets/x86_64-linux/include/*
/usr/local/cuda-11.1/lib64/libcudadevrt.a
/usr/local/cuda-11.1/lib64/libcudart_static.a
/usr/local/cuda-11.1/bin/crt/
/usr/local/cuda-11.1/bin/fatbinary
/usr/local/cuda-11.1/bin/nvcc*
/usr/local/cuda-11.1/bin/nvlink
you may met other build errors for other samples. it’s your decision to install cuda toolkit entirely or copy one by one for saving disk space. or you can start with devel docker.