using multiple GPUs can significantly speed up the process. However, handling multiple GPUs properly requires understanding different parallelism techniques, automating GPU selection, and troubleshooting
首先，只有主 GPU 能进行损耗计算、反向推导和渐变步骤，其他 GPU 则会在 60 摄氏度以下冷却，等待下一组数据。 其次，在主 GPU 上聚合所有输出所需的额外内存通常会促使你减小批处理的大小。nn.DataParallel 将批处理均匀地分配到多个 GPU。假设你有 4 个 GPU，批处理总大小为 32；然后，每个 GPU 将获得包含...
https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255 https://medium.com/@theaccelerators/learn-pytorch-multi-gpu-properly-3eb976c030ee https://towardsdatascience.com/how-to-scale-training-on-multiple-gpus-dae1041f49d2 建议5:...
import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import torchvision.datasets as datasets # Check if GPU is available, and if not, use the CPU device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 加载CIFAR-10 CIFA...
此处的batch_size 应该是每个GPU的batch_size的总和 1.2.2 方式二:torch.nn.parallel.DistributedDataParallel(推荐) 1.2.2.1 多进程执行多卡训练,效率高 1.2.2.2 代码编写流程 1.2.2.2.1 第一步 n_gpu=torch.cuda.device_count()torch.distributed.init_process_group("nccl",world_size=n_gpus,rank=args.loca...
利用partition.to(device) 把partition放置到相关设备之上,这就是前文提到的,~torchgpipe.GPipe使用CUDA进行训练。用户不需要自己将模块移动到GPU,因为~torchgpipe.GPipe自动把每个分区移动到不同的设备上。 把这个partition加入到分区数组中 然后去下一个device看看 ...
🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. To use it, you don't need to change anything in your training code; you can set everything using justaccelerate config. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide yo...
defdo_inference(context, bindings, inputs, outputs, stream, batch_size=1):# Transfer data from CPU to the GPU.[cuda.memcpy_htod_async(inp.device, inp.host, stream)forinpininputs]# Run inference.context.execute_async(batch_size=batch_size, bind...
std::cout << "--datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)" << std::endl; ...
Here is an example of how to usetorch.cuda.synchronize()in PyTorch: importtorch# Create a tensor on the GPUx=torch.randn(10,device='cuda')# Perform some operations on the GPUy=x*2z=x+y# Synchronize CUDA operationstorch.cuda.synchronize() ...