record_shapes=True,profile_memory=True,with_stack=True) as prof:for step, batch_data in enumerate(train_loader):prof.step() # Need to call this at each step to notify profiler of steps' boundary.if step >= 1 + 1
item()) y_true_train += list(y.cpu().data.numpy()) y_pred_train += list(pred.cpu().data.numpy()) total_loss_train += loss.item() train_acc = rmse(y_true_train, y_pred_train) train_loss = total_loss_train/len(train_dl) # len train_dl = 704. the calc is number of ...
is_available()print(f'Train on gpu: {train_on_gpu}')# Number of gpusif train_on_gpu: gpu_count = cuda.device_count() print(f'{gpu_count} gpus detected.') if gpu_count > 1: multi_gpu = True else: multi_gpu = Falseif train_on_gpu: model = model.to('cuda')if multi_gpu:...
在本例中,一个episode中包含许多次更新,在任务未终止前,count()会一直计数,当达到终止状态时,done为True,next_state为None。 将环境转移放入经验回放池,同时更新状态 # Store the transition in memory memory.push(state, action, next_state, reward) # Move to the next state state = next_state 更...
n_gpu=torch.cuda.device_count()torch.distributed.init_process_group("nccl",world_size=n_gpus,rank=args.local_rank) 1.2.2.2.2 第二步 torch.cuda.set_device(args.local_rank)该语句作用相当于CUDA_VISIBLE_DEVICES环境变量 1.2.2.2.3 第三步 ...
torch.nonzero(tensor) # index of non-zero elementstorch.nonzero(tensor==0) # index of zero elementstorch.nonzero(tensor).size(0) # number of non-zero elementstorch.nonzero(tensor == 0).size(0) # number of zero elements 判断两个张量相等 ...
val_set.append(j)#设置每个batch大小batch_size = 128nw= min([os.cpu_count(), batch_sizeifbatch_size > 1else0, 8])#number of workersprint('Using {} dataloader workers every process'.format(nw))#创建x、y的张量 训练集、测试集x =torch.tensor(np.array(train_set)) ...
parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') args = parser.parse_args() torch.manual_seed(args.seed) WORLD_SIZE = torch.cuda.device_count() mp.spawn(fsdp_main, args=(WORLD_SIZE, args), nprocs=WORLD_SIZE, join=True) ...
train_sampler, batch_size, drop_last=True) nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers if rank == 0: print('Using {} dataloader workers every process'.format(nw)) #每个进程(process)中会使用几个线程(workers)来加载数据 ...
model-analyzer profile -m /triton_repository/ \ --profile-models resnet50_trt \ --run-config-search-max-concurrency 2 \ --run-config-search-max-instance-count 2 \ --run-config-search-preferred-batch-size-disable true 执行以下命令,生成分析报告。