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→Running actual workloads on the cluster
See [https://www.nvidia.com/en-us/gpu-cloud/containers/ the catalog of containers by nVidia] for more options for base images (e.g. [https://ngc.nvidia.com/catalog/containers/nvidia:pytorch PyTorch]), or Google around for containers of your favourite application. '''Make sure you only run containers from trusted sources!'''
'''Please note (very important): The versions 20.09 of the deep learning frameworks on nvcr.io work on all hosts in the cluster. While there are newer images available, they require drivers >= 455, which are not available for all machines yet. So please For guaranteed compability, you must stick to 20.09 unless , but you can target a specific hostwith newer drivers.'''
At the bottom of the GPU cluster status page, there is the nvidia-smi output for each node, where you can check individual driver and CUDA version. You can also switch to a shell in the container and verify GPU capabilities:
