docker for CUDA 10

🚀 Feature

Hi, Apparently GTX 2080s have trouble with CUDA9, which is what the current Dockerfile installs. Would it be possible to have a version of the Dockerfile for CUDA10? I’ve tried it myself for several hours by starting FROM pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-runtime instead of ubuntu and commenting out the CUDA 9.0-specific steps, but haven’t managed to make it work.

In case it helps, the following Dockerfile

FROM pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-runtime
# PyTorch (Geometric) installation
# RUN rm /etc/apt/sources.list.d/cuda.list && \
#     rm /etc/apt/sources.list.d/nvidia-ml.list 

RUN apt-get update &&  apt-get install -y \
    curl \
    ca-certificates \
    vim \
    sudo \
    git \
    bzip2 \
    libx11-6 \
 && rm -rf /var/lib/apt/lists/*

# Create a working directory.
RUN mkdir /app
WORKDIR /app

# Create a non-root user and switch to it.
RUN adduser --disabled-password --gecos '' --shell /bin/bash user \
 && chown -R user:user /app
RUN echo "user ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-user
USER user

# All users can use /home/user as their home directory.
ENV HOME=/home/user
RUN chmod 777 /home/user

# Install Miniconda.
RUN curl -so ~/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh \
 && chmod +x ~/miniconda.sh \
 && ~/miniconda.sh -b -p ~/miniconda \
 && rm ~/miniconda.sh
ENV PATH=/home/user/miniconda/bin:$PATH
ENV CONDA_AUTO_UPDATE_CONDA=false

# Create a Python 3.6 environment.
RUN /home/user/miniconda/bin/conda install conda-build \
 && /home/user/miniconda/bin/conda create -y --name py36 python=3.6.5 \
 && /home/user/miniconda/bin/conda clean -ya
ENV CONDA_DEFAULT_ENV=py36
ENV CONDA_PREFIX=/home/user/miniconda/envs/$CONDA_DEFAULT_ENV
ENV PATH=$CONDA_PREFIX/bin:$PATH

# CUDA 9.0-specific steps.
RUN conda install -c pytorch pytorch
RUN conda install -c fragcolor cuda10.0 && conda clean -ya
# RUN conda install -y -c pytorch \
#     cuda90=1.0 \
#     magma-cuda90=2.4.0 \
#     "pytorch=1.1.0=py3.6_cuda9.0.176_cudnn7.5.1_0" \
#     torchvision=0.2.1 \
#  && conda clean -ya

# Install HDF5 Python bindings.
RUN conda install -y h5py=2.8.0 \
 && conda clean -ya
RUN pip install h5py-cache==1.0

# Install TorchNet, a high-level framework for PyTorch.
# RUN pip install torchnet==0.0.4

# Install Requests, a Python library for making HTTP requests.
RUN conda install -y requests=2.19.1 \
 && conda clean -ya

# Install Graphviz.
# RUN conda install -y graphviz=2.38.0 \
#  && conda clean -ya
# RUN pip install graphviz==0.8.4

# Install OpenCV3 Python bindings.
RUN sudo apt-get update && sudo apt-get install -y --no-install-recommends \
    libgtk2.0-0 \
    libcanberra-gtk-module \
 && sudo rm -rf /var/lib/apt/lists/*
RUN conda install -y -c menpo opencv3=3.1.0 \
 && conda clean -ya

# Install PyTorch Geometric.
RUN CPATH=/usr/local/cuda/include:$CPATH \
 && LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
 && DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH

RUN pip install --verbose --no-cache-dir torch-scatter \
 && pip install --verbose --no-cache-dir torch-sparse \
 && pip install --verbose --no-cache-dir torch-cluster \
 && pip install --verbose --no-cache-dir torch-spline-conv \
 && pip install torch-geometric

# Set the default command to python3.
CMD ["python3"]
#######
# Mine #
#######
RUN pip --no-cache-dir install -U tensorboardX \
h5py \
matplotlib \
ipdb \
scipy \
tqdm

COPY *.py /code/

allows the docker to be created and import torch works, but

import torch_geometric

leads to

ModuleNotFoundError: No module named 'torch_scatter.scatter_cuda'

[JsBlueCat]

CUDA9

ARG CUDA=“10.0” ARG CUDNN=“7”

FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu16.04

RUN echo ‘debconf debconf/frontend select Noninteractive’ | debconf-set-selections

install basics

RUN apt-get update -y
&& apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++
&& apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev

Install Miniconda

RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
&& chmod +x /miniconda.sh
&& /miniconda.sh -b -p /miniconda
&& rm /miniconda.sh

ENV PATH=/miniconda/bin:$PATH

Create a Python 3.6 environment

RUN /miniconda/bin/conda install -y conda-build
&& /miniconda/bin/conda create -y --name py36 python=3.6.8
&& /miniconda/bin/conda clean -ya

ENV CONDA_DEFAULT_ENV=py36 ENV CONDA_PREFIX=/miniconda/envs/$CONDA_DEFAULT_ENV ENV PATH=$CONDA_PREFIX/bin:$PATH ENV CONDA_AUTO_UPDATE_CONDA=false

RUN conda install -y ipython RUN pip install ninja yacs cython numpy matplotlib opencv-python tqdm pyyaml tensorboardX

install pytorch

RUN conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
&& conda clean -ya

set cuda path

ENV PATH=/usr/local/cuda/bin:$PATH ENV CPATH=/usr/local/cuda/include:$CPATH ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH ENV DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH

RUN pip install --verbose --no-cache-dir torch-scatter RUN pip install --verbose --no-cache-dir torch-sparse RUN pip install --verbose --no-cache-dir torch-cluster RUN pip install --verbose --no-cache-dir torch-spline-conv RUN pip install torch-geometric WORKDIR /meshvertex

i’m use this docker and run

sudo docker run --rm -it --runtime=nvidia --device=/dev/video0 --shm-size 16G -e DISPLAY=unix$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /home/cery/workspace/MeshVertexNet/:/meshvertex  jsbluecat/pytorch:meshvertex bash

may be this help

[FerranAlet]

Took me a while, but I think I managed to do it. Will use the docker for a bit to check everything is ok and then do a pull request. Only thing is that I had to comment out the Install Graphviz portion, but that also happened for the CUDA9 version and to a friend of mine trying to use the original Dockerfile. Unless we figure out there’s something wrong with it, I’ll keep it in the CUDA10 version for consistency