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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. 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 container images 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 stick to 20.09 unless you target a very specific host.''' I will soon provide a table with driver versions for all hosts once they are upgraded and moved to the new cluster. As a general rule, everything which is made for Cuda 11.0 and driver version >= 450 should work fine on the Cluster.
You can again switch to a shell in the container and verify GPU capabilities:
