Hey Shen,
I am running a simple application using <torch.distributed.launch> on two machines each having 2 gpus. It throws me the above error.
CUDA 10.2
Pytorch 1.5.1
NCCL backend as
libnccl-devel-2.5.6-1+cuda10.2.x86_64
libnccl-2.5.6-1+cuda10.2.x86_64
libnccl-static-2.5.6-1+cuda10.2.x86_64
But torch.cuda.nccl.version() shows me 2.4.
Ran NCCL tests – they are working fine.
def train(args):
    current_env = os.environ.copy()
    dist.init_process_group(backend='nccl', init_method='env://')
    model = ConvNet()
    torch.cuda.set_device(args.local_rank)
    model.cuda(args.local_rank)
    batch_size = 256
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(args.local_rank)
    optimizer = torch.optim.SGD(model.parameters(), 1e-4)

    model = DDP(model, device_ids=[args.local_rank])
    # Data loading code
    train_dataset = torchvision.datasets.MNIST(root='./data',
                                               train=True,
                                               transform=transforms.ToTensor(),
                                               download=True)

    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset, num_replicas=int(current_env["WORLD_SIZE"]), rank=args.local_rank)

    train_loader = torch.utils.data.DataLoader(
    	dataset=train_dataset,
        batch_size=batch_size,
        shuffle=False,         
        num_workers=0,
        pin_memory=True,
        sampler=train_sampler)  

Plz let me know if you need any more info.
Thanks