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