The simplest micro benchmark for cupy CUDA containerized in NVIDIA AI Workbench

NVIDIA AI Workbench runs inside a containerized environment, and I wanted an environment check that verifies the container has access and that Docker/podman, the NVIDIA driver, and Workbench are all on compatible versions.  

Containerization has no effect on performance. The CUDA code is pretty much a direct pass-through to the card.

Environment

  • NVIDIA AI Workbench
  • One local GPU
  • Windows 11 
  • Docker Desktop
  • Program running in a container built by the Workbench based on the PyTorch/Cuda image

Program

I access the containerized environment via a Jupyter Notebook visible to the browser on the Windows machine. This is a snapshot of the Jupyter Notebook.


It found a problem

The program demonstrated that there was a container adapter (or something) mismatch that recently happened.  cupy returned that it had access to the GPU. It turns out that it really did not, and that a Docker Desktop upgrade was needed to fix something driver-related.  The error message was

"CUDARuntimeError: cudaErrorSymbolNotFound: named symbol not found"

Revision History
Created 2024/07

Corrected NVIDIA capitalization 2025/08

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