Dockerfile.cu118.torch2.0.1.py39

ARG CUDA_VERSION="11.8.0"
ARG CUDNN_VERSION="8"
ARG UBUNTU_VERSION="22.04"

# Base NVidia CUDA Ubuntu image
FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base

ENV HOME /root
WORKDIR $HOME
ENV PYTHON_VERSION=3.9.18
ENV PATH="/usr/local/cuda/bin:${PATH}"
#
# Install Python plus openssh, which is our minimum set of required packages.
# Install useful command line utility software
ARG APTPKGS="zsh wget tmux tldr nvtop vim neovim curl rsync net-tools less iputils-ping 7zip zip unzip"
RUN apt-get update -y && \
    apt-get install -y python3 python3-pip python3-venv && \
    apt-get install -y --no-install-recommends openssh-server openssh-client git git-lfs && \
    python3 -m pip install --upgrade pip && \
    apt-get install -y --no-install-recommends $APTPKGS && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# Install Miniconda for Python env management
ENV PATH="${HOME}/miniconda3/bin:${PATH}"
ENV BASEPATH="${PATH}"
RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
    && mkdir ${HOME}/.conda \
    && bash Miniconda3-latest-Linux-x86_64.sh -b -p ${HOME}/miniconda3 \
    && rm -f Miniconda3-latest-Linux-x86_64.sh

# Make base conda environment
ENV CONDA=pytorch
RUN conda create -n "${CONDA}" python="${PYTHON_VERSION}"
ENV PATH="${HOME}/miniconda3/envs/${CONDA}/bin:${BASEPATH}"

# Install pytorch
ARG PYTORCH="2.0.1"
ARG CUDA="118"
RUN pip3 install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu$CUDA

# Set up git to support LFS, and to store credentials; useful for Huggingface Hub
RUN git config --global credential.helper store && \
    git lfs install

#COPY --chmod=755 start-ssh-only.sh /start.sh

WORKDIR /workspace

CMD [ "tail -f /dev/null" ]