我们首先确保随机种子每个地方都是一样的。 torch.manualSeed(1234) -- choose a dimension N =5-- create a random NxN matrix A = torch.rand(N, N) -- make it symmetric positive A = A*A:t() -- make it definite A:add(0.001, torch.eye(N)) -- add a linear term b = torch.rand(...
我们首先确保随机种子每个地方都是一样的。 torch.manualSeed(1234) --choose a dimension N=5--create a random NxN matrix A=torch.rand(N,N)--make it symmetric positive A=A*A:t()--make it definite A:add(0.001,torch.eye(N))--add a linear term b=torch.rand(N)--create the quadratic ...
manualSeed(1234) -- make sure the random seed is the same for everyone N = 5 -- choose a dimension A = torch.rand(N, N) -- create a random NxN matrix A = A*A:t() -- make it symmetric positive A:add(0.001, torch.eye(N)) -- make it definite b = torch.rand(N) -- ...
manual_seed(1234) tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100) device = local_rank * 2 + (iter + local_rank) % 2 tensor = tensor.cuda(device).type(dtype) multiplied = tensor * size hvd.allreduce_(tensor, average=False) max_difference = tensor.sub(multiplied)....
importtorchfromtorch.profilerimportprofile,record_function,ProfilerActivitydtype=torch.int32N=1000000000torch.manual_seed(1234)torch.cuda.manual_seed(1234)# 设置当前设备的随机种子t=torch.randint(0,N,(N,),dtype=dtype,device="cuda")deffunc():output,inverse_indices=torch.unique(t,return_inverse=True...
manual_seed(1234) input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() target = torch.randint(low=1, high=C, size=(N,S), dtype=torch.long) input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) target_lengths = torch.randint(low=S_min, ...
iter +=1torch.manual_seed(1234) tensor = torch.FloatTensor(*([17] * dim)).random_(-100,100) device =local_rank*2+ (iter +local_rank) %2tensor = tensor.cuda(device).type(dtype) multiplied = tensor * size hvd.allreduce_(tensor, average=False) ...
torch.manual_seed(seed) if cuda_deterministic: # slower, more reproducible cudnn.deterministic = True cudnn.benchmark = False else: # faster, less reproducible cudnn.deterministic = False cudnn.benchmark = True def main(): random_seed = 1234 ...
torch$manual_seed(1234) #> <torch._C.Generator> x <- torch$randn(10L, 480L, 640L, 3L) x[1:3, 1:2, 1:3, 1:2] #> tensor([[[-0.0883, 0.3420], #> [ 1.0051, -0.1117], #> [-0.0982, -0.3511]], #> #> [[-0.1465, 0.3960], #> [-1.6878, 0.5720], #> [ 0.9426, 2....
manual_seed(42) train(train_file, test_file, batch_size, epochs, gpu_mode, num_workers, retrain_model, retrain_model_path, gru_layers, hidden_size, learning_rate, weight_decay, model_dir, stats_dir, train_mode, total_callers, rank, device_ids[rank]) cleanup() ...