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Complex Machine Learning workflows are supported through terminal/SSH logins and a full Linux/Ubuntu CUDA development suite. Users may install additional library packages (e.g. conda/pip, CRAN) as needed, or can opt to replace the default environment entirely by launching their own custom Docker containers. High High-speed cluster-local storage houses workspaces and common training corpora (e.g. CIFAR, ImageNet).
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