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To perform distributed deep learning in PyTorch, you must first know the access address of the master process. By accessing this master process, PyTorch can form a group for distributed deep learning. This process typically involves specifying environment variables such as MASTER_ADDR and MASTER_PORT, which define the network location where the master node is running. All participating worker processes use this information to establish communication channels and synchronize with each other.