NVidia AI Workstation fixing the container config when the container build fails

I broke my AI Workstation container image configuration so that I couldn't get an image built. This meant I couldn't edit my repository to edit back. Turns I could use the AI Workstation Environments tab to clean up the configuration to build a new image.
NVidia AI Workstation provides managed containerized environments for CUDA and other Jupyter Notebook and Tensor Lab workloads. AI Workstation creates a container image for each project based on some base image and is augmented via custom configuration through files like requirements.txt. Sometimes you can blow up the container build process by putting bad stuff in the environmental configuration files. The Workstation GUI lets you edit environmental properties that it then pushes back into the configuration files. You can iterate on that until you have a buildable container image.

[@sadekulIslam-q6y]

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