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Learn how to leverage NVIDIA cuPyNumeric, a multi-node, multi-GPU NumPy-compatible library, to scale Python and NumPy programs across multiple GPUs and nodes without writing complex parallel computing code like MPI. Discover how to set up and use 2 DGX Spark systems as a desktop cluster to accelerate cuPyNumeric computations locally, making high-performance computing accessible to domain scientists and researchers who want to scale their existing Python workflows beyond single-GPU limitations.
