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Dockerfile nvidia
FROM nvcr.io/nvidia/pytorch:24.06-py3 RUN pip install vllm openai sse_starlette -i https://pypi.tuna.tsinghua.edu.cn/simple RUN pip install peft transformers datasets accelerate deepspeed tensorboard \ fire packaging ninja openai gradio -i https://pypi.tuna.tsinghua.edu.cn/simple 处理nvidia-smi执行后结果显示很慢的问题，安装fabric-manager
version=535.54.03 yum -y install yum-utils nvidia-docker2 yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo yum install -y nvidia-fabric-manager-${version}-1 nvidia-fabric-manager-devel-${version}-1 安装cuda 安装nvidia-docker2 安装nvidia驱动
cuda：https://developer.nvidia.com/cuda-toolkit-archive
nvidid： https://download.nvidia.com/
亦庄
FROM nvcr.io/nvidia/pytorch:23.10-py3 RUN pip install --upgrade pip && \ pip install --no-cache-dir vllm==0.4.3 openai sse_starlette spacy torch typer torch-tensorrt torchdata torchtext torchvision weasel --upgrade --upgrade-strategy=only-if-needed -i https://pypi.tuna.tsinghua.edu.cn/simple 