I am trying to have this repo on docker: https://github.com/facebookresearch/detectron2/tree/main/docker
but when I want to docker compose it, I receive this error:
ERROR: Package 'detectron2' requires a different Python: 3.6.9 not in '>=3.7'
The default version of the python I am using is 3.10 but I don’t know why through docker it’s trying to run it on python 3.6.9.
Is there a way for me to change it to a higher version of python while running the following dockerfile?
FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
# use an older system (18.04) to avoid opencv incompatibility (issue#3524)
ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update && apt-get install -y
python3-opencv ca-certificates python3-dev git wget sudo ninja-build
RUN ln -sv /usr/bin/python3 /usr/bin/python
# create a non-root user
ARG USER_ID=1000
RUN useradd -m --no-log-init --system --uid ${USER_ID} appuser -g sudo
RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER appuser
WORKDIR /home/appuser
ENV PATH="/home/appuser/.local/bin:${PATH}"
RUN wget https://bootstrap.pypa.io/pip/3.6/get-pip.py &&
python3 get-pip.py --user &&
rm get-pip.py
# install dependencies
# See https://pytorch.org/ for other options if you use a different version of CUDA
RUN pip install --user tensorboard cmake # cmake from apt-get is too old
RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
# install detectron2
RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo
# set FORCE_CUDA because during `docker build` cuda is not accessible
ENV FORCE_CUDA="1"
# This will by default build detectron2 for all common cuda architectures and take a lot more time,
# because inside `docker build`, there is no way to tell which architecture will be used.
ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
RUN pip install --user -e detectron2_repo
# Set a fixed model cache directory.
ENV FVCORE_CACHE="/tmp"
WORKDIR /home/appuser/detectron2_repo
# run detectron2 under user "appuser":
# wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
# python3 demo/demo.py
#--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml
#--input input.jpg --output outputs/
#--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
2
Answers
You can use pyenv: https://github.com/pyenv/pyenv
Just google
docker pyenv container, will give you some entries like: https://gist.github.com/jprjr/7667947If you follow the gist you can see how it has been updated, very easy to update to latest python that pyenv support. anything since 2.2 to 3.11
Only drawback is that container becomes quite large because it holds all glibc development tools and libraries to compile cpython, but often it helps in case you need modules without wheels and need to compile because it is already there.
Below is a minimal Pyenv Dockerfile Just change the PYTHONVER or set a –build-arg to anything pythonversion pyenv support have (
pyenv install -l):and the pyenv config to be saved as .bash_profile in Dockerfile directory:
build with:
docker build -t pyenv:3.10.5 .Will build the image, but as said it is quite big:
very easy to test any python version only changing PYTHONVER
if I build with
docker build -t pyenv:3.12-dev --build-arg PYTHONVER=3.12.dev .or change the PYTHONVER in the Dockerfile:This is an open issue with facebookresearch/detectron2. The developers updated the base Python requirement from 3.6+ to 3.7+ with commit 5934a14 last week but didn’t modify the
Dockerfile.I’ve created a
Dockerfilebased on Nvidia CUDA’s CentOS8 image (rather than Ubuntu) that should work.Alternatively, this is untested as the following images don’t work on my machine (because I run
arm64) so I can’t debug…In the original
Dockerfile, changing yourFROMline to this might resolve it, but I haven’t verified this (and the image mentioned in the issue (pytorch/pytorch:1.10.0-cuda11.3-cudnn8-devel) might work as well.