事先安装过 cuda 11.8  cudnn 8.6 和 TensorRT 

安装miniconda

下载源代码：

$ git clone --recursive https://github.com/togethercomputer/OpenChatKit.git

安装miniconda： 

$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

$ sh ./Miniconda3-latest-Linux-x86_64.sh

$ /home/mklguy/miniconda3/bin/conda init

mklguy@mklguy--PC:~/ex/OpenChatKit$ /home/mklguy/miniconda3/bin/conda env create -f environment.yml

在V-P-N的挟持下，创建成功：

在~/.bashrc中会出现这么一段，导致每次进入系统后，会先进入conda环境：

# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/home/mklguy/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; theneval "$__conda_setup"
elseif [ -f "/home/mklguy/miniconda3/etc/profile.d/conda.sh" ]; then. "/home/mklguy/miniconda3/etc/profile.d/conda.sh"elseexport PATH="/home/mklguy/miniconda3/bin:$PATH"fi
fi
unset __conda_setup
# <<< conda initialize <<<

备份文件后删掉这段试试。

$ source .bashrc

也可以使用 pip3再安装整个系统可用的 pytorch：

$ pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118

用经典案例做一个测试：

from __future__ import print_functionimport torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
import argparseclass Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(1, 32, 3, 1)self.conv2 = nn.Conv2d(32, 64, 3, 1)self.dropout1 = nn.Dropout(0.25)self.dropout2 = nn.Dropout(0.5)self.fc1 = nn.Linear(9216, 128)self.fc2 = nn.Linear(128, 10)def forward(self, x):x = self.conv1(x)x = F.relu(x)x = self.conv2(x)x = F.relu(x)x = F.max_pool2d(x, 2)x = self.dropout1(x)x = torch.flatten(x, 1)x = self.fc1(x)x = F.relu(x)x = self.dropout2(x)x = self.fc2(x)output = F.log_softmax(x, dim=1)return outputdef train(args, model, device, train_loader, optimizer, epoch):model.train()for batch_idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)optimizer.zero_grad()output = model(data)loss = F.nll_loss(output, target)loss.backward()optimizer.step()if batch_idx % args.log_interval == 0:print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))if args.dry_run:breakdef test(model, device, test_loader):model.eval()test_loss = 0correct = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch losspred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probabilitycorrect += pred.eq(target.view_as(pred)).sum().item()test_loss /= len(test_loader.dataset)print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(test_loader.dataset),100. * correct / len(test_loader.dataset)))def main():# Training settingsparser = argparse.ArgumentParser(description='PyTorch MNIST Example')parser.add_argument('--batch-size', type=int, default=64, metavar='N',help='input batch size for training (default: 64)')parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',help='input batch size for testing (default: 1000)')parser.add_argument('--epochs', type=int, default=14, metavar='N',help='number of epochs to train (default: 14)')parser.add_argument('--lr', type=float, default=1.0, metavar='LR',help='learning rate (default: 1.0)')parser.add_argument('--gamma', type=float, default=0.7, metavar='M',help='Learning rate step gamma (default: 0.7)')parser.add_argument('--no-cuda', action='store_true', default=False,help='disables CUDA training')parser.add_argument('--dry-run', action='store_true', default=False,help='quickly check a single pass')parser.add_argument('--seed', type=int, default=1, metavar='S',help='random seed (default: 1)')parser.add_argument('--log-interval', type=int, default=10, metavar='N',help='how many batches to wait before logging training status')parser.add_argument('--save-model', action='store_true', default=False,help='For Saving the current Model')args = parser.parse_args()use_cuda = not args.no_cuda and torch.cuda.is_available()torch.manual_seed(args.seed)device = torch.device("cuda" if use_cuda else "cpu")train_kwargs = {'batch_size': args.batch_size}test_kwargs = {'batch_size': args.test_batch_size}if use_cuda:cuda_kwargs = {'num_workers': 1,'pin_memory': True,'shuffle': True}train_kwargs.update(cuda_kwargs)test_kwargs.update(cuda_kwargs)transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])dataset1 = datasets.MNIST('../data', train=True, download=True,transform=transform)dataset2 = datasets.MNIST('../data', train=False,transform=transform)train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)model = Net().to(device)optimizer = optim.Adadelta(model.parameters(), lr=args.lr)scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)for epoch in range(1, args.epochs + 1):train(args, model, device, train_loader, optimizer, epoch)test(model, device, test_loader)scheduler.step()if args.save_model:torch.save(model.state_dict(), "mnist_cnn.pt")if __name__ == '__main__':main()

$ python3 hello_mnist.py

99%

安装git 的lfs，

$ git lfs install

遇到git版本低的问题，最终如下解决：

$ sudo apt-get autoremove git##:: /bin/sh: msgfmt: command not found
$ sudo apt-get install gettext##:: /bin/sh: 1: asciidoc: not found
$ sudo apt-get install asciidoc##:: /bin/sh: 1: docbook2x-texi: not found
$ sudo apt-get install docbook2X$ sudo apt-get install   texinfo perl openjade dh-autoreconf autoconf libcurl4-gnutls-dev libexpat1-dev gettext zlib1g-dev libssl-dev asciidoc xmlto docbook2x$ wget https://www.kernel.org/pub/software/scm/git/git-2.40.0.tar.gz$ make prefix=/usr all doc info ;# as yourself# make prefix=/usr install install-doc install-html install-info ;# as root$ wget https://github.com/git-lfs/git-lfs/releases/download/v3.3.0/git-lfs-linux-amd64-v3.3.0.tar.gz
$ sudo ./install.sh##:: OK了

安装 huggingface的 transformers：

$ pip3 install transformers

下载参数数据并测试：

from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-NeoXT-Chat-Base-20B')
pipe(''': Hello!\n:''')

上面第二句的执行是这样的：第一波至少下载接近50GB的参数：