It is a base environment for torch with GPU support (including 3090Ti!) that can be used for working with AI models. This image requires nvidia-driver-525 and nvidia-docker2 installed on host. It needs 30GB on disk!
Dockerfile
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
ENV PYTHONUNBUFFERED=1
# SYSTEM
RUN apt-get update --yes --quiet && DEBIAN_FRONTEND=noninteractive apt-get install --yes --quiet --no-install-recommends \
software-properties-common \
build-essential apt-utils \
wget curl vim git ca-certificates kmod \
nvidia-driver-525 \
&& rm -rf /var/lib/apt/lists/*
# PYTHON 3.10
RUN add-apt-repository --yes ppa:deadsnakes/ppa && apt-get update --yes --quiet
RUN DEBIAN_FRONTEND=noninteractive apt-get install --yes --quiet --no-install-recommends \
python3.10 \
python3.10-dev \
python3.10-distutils \
python3.10-lib2to3 \
python3.10-gdbm \
python3.10-tk \
pip
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 999 \
&& update-alternatives --config python3 && ln -s /usr/bin/python3 /usr/bin/python
RUN pip install --upgrade pip
# ANACONDA
RUN wget -O /tmp/anaconda.sh https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh \
&& bash /tmp/anaconda.sh -b -p /anaconda \
&& eval "$(/anaconda/bin/conda shell.bash hook)" \
&& conda init \
&& conda update -n base -c defaults conda \
&& conda create --name env \
&& conda activate env \
&& conda install -y pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
Build
$ docker build -t nvidia-cuda .
Run
$ docker run --gpus all -it nvidia-cuda
Test
Run inside container:
$ conda activate env
$ python
>>> include torch
>>> torch.cuda.is_available()
Top comments (2)
My host machine has driver version 525.147.05, and the version I installed inside the container is nvidia-driver-515. Why is the nvidia-smi command inside the container still showing the host machine's driver version?
how large is this image? the offical torch-cuda image seems to be around 18G