no model named 'torch.distributed'

你好！关于你提到的'torch.distributed'模型，我了解到这是PyTorch中用于分布式训练的模块，用于多台机器之间的数据并行和模型并行。这个模块可以用于训练大规模的深度学习模型。使用'torch.distributed'模块，你可以在多个GPU或多台机器上并行地训练模型，以加快训练速度和提高性能。如果你在使用中遇到了问题，可以告诉我具体的错误信息，我会尽力帮助你解决。

相关问题

No module named torch.distributed.run

As an AI language model, I am not able to diagnose the specific error of your code. However, the error message "No module named torch.distributed.run" suggests that the module "torch.distributed.run" is not installed in your Python environment or it is not properly imported in your code.

To resolve this issue, you can try to install the missing module by running the following command in your terminal or command prompt:

pip install torch.distributed

If the installation is successful, you can import the module in your code using the following statement:

import torch.distributed.run

Make sure that you have the latest version of PyTorch installed in your system as well.

pytorch ddp mp.spawn 启动多机多卡训练

PyTorch支持使用分布式数据并行（DDP）和多进程训练（mp）来实现多机多卡训练。以下是使用PyTorch进行多机多卡训练的步骤：

1. 安装必要的软件和库。您需要安装PyTorch和Horovod（或其他分布式训练库），以便能够使用DDP和mp。

2. 编写代码。您需要编写一个PyTorch脚本，该脚本使用DDP和mp启动多机多卡训练。您可以使用以下代码片段来启动多机多卡训练：

import torch
import horovod.torch as hvd
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data.distributed import DistributedSampler
from torchvision import datasets, transforms

# Initialize Horovod
hvd.init()

# DistributedSampler to split the dataset among the workers
train_sampler = DistributedSampler(
    datasets.MNIST(
        './data',
        train=True,
        download=True,
        transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ])
    ),
    num_replicas=hvd.size(),
    rank=hvd.rank()
)

# DataLoader to load the dataset
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        './data',
        train=True,
        download=True,
        transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ])
    ),
    batch_size=64,
    sampler=train_sampler
)

# Setup the model
model = ...
model = DistributedDataParallel(model)

# Define the optimizer
optimizer = ...

# An optimizer is instantiated for each worker with a copy of the model and its gradients
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())

# Train the model
for epoch in range(10):
    train(epoch, model, train_loader, optimizer, hvd.rank(), hvd.size())

3. 启动多机多卡训练。您需要在多台计算机上启动多个进程来运行PyTorch脚本。以下是一个简单的启动脚本示例：

mpirun -np 4 \
    -H server1:2,server2:2 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    python train.py

上述命令将在两台计算机上启动4个进程（每台计算机上有两个进程），并将它们连接起来进行多机多卡训练。请注意，您需要将“server1”和“server2”替换为您的计算机名称或IP地址。

4. 监视训练进度。您可以使用TensorBoard等工具来监视训练进度，并在训练期间进行实时分析。