The project and the problem
Researchers in the laboratory programme needed Python, notebooks, machine-learning models and graphics-processor workloads for confidential measurement data that had to remain on local infrastructure. A conventional shared server would create dependency conflicts, uncontrolled resource use and result files whose input dataset, code, runtime and model versions could not be reconstructed.
