A data science team wants to ship a deep learning inference application to run on several different customer clusters. Each cluster has different host OS versions and locally installed frameworks, and the team is spending significant effort resolving mismatched library dependencies during every deployment. They want a repeatable way to package the application with all its CUDA, cuDNN, and framework dependencies so it runs consistently across environments. Which NVIDIA software approach best addresses this need?