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facebookresearch / detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
https://detectron2.readthedocs.io/en/latest/
Apache License 2.0
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Docker can not be built due to latest NVIDIA key rotation #4204

Open pantelis opened 2 years ago

pantelis commented 2 years ago

If you do not know the root cause of the problem, please post according to this template:

Instructions To Reproduce the Issue:

cd docker/
# Build:
docker build --build-arg USER_ID=$UID -t detectron2:v0 .
W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease' is not signed.

Expected behavior:

Docker should build successfully, however docker build fails due to the key rotation by NVIDIA as described in

https://forums.developer.nvidia.com/t/notice-cuda-linux-repository-key-rotation/212771

Docker can be built successfully if the following two lines are added in the Dockerfile after the the FROM command. 

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-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub

Environment:

sys.platform            linux
Python                  3.6.9 (default, Mar 15 2022, 13:55:28) [GCC 8.4.0]
numpy                   1.19.5
detectron2              0.6 @/workspaces/detectron2/detectron2
detectron2._C           not built correctly: No module named 'detectron2._C'
Compiler ($CXX)         c++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CUDA compiler           Build cuda_11.1.TC455_06.29190527_0
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.10.0+cu111 @/home/appuser/.local/lib/python3.6/site-packages/torch
PyTorch debug build     False
GPU available           Yes
GPU 0                   NVIDIA RTX A4000 (arch=8.6)
Driver version          510.60.02
CUDA_HOME               /usr/local/cuda
TORCH_CUDA_ARCH_LIST    Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing;Ampere
Pillow                  8.4.0
torchvision             0.11.1+cu111 @/home/appuser/.local/lib/python3.6/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore                  0.1.5
iopath                  0.1.9
cv2                     3.2.0
----------------------  --------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
AbhishekBose commented 2 years ago

This seems to work for me

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 rm /etc/apt/sources.list.d/cuda.list
RUN rm /etc/apt/sources.list.d/nvidia-ml.list
RUN apt-key del 7fa2af80
RUN apt-get update && apt-get install -y --no-install-recommends wget

RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
RUN dpkg -i cuda-keyring_1.0-1_all.deb
Skorkmaz88 commented 2 years ago

Worked for me @AbhishekBose thanks This is the working Dockerfile for me after making the change:

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 rm /etc/apt/sources.list.d/cuda.list
RUN rm /etc/apt/sources.list.d/nvidia-ml.list
RUN apt-key del 7fa2af80
RUN apt-get update && apt-get install -y --no-install-recommends wget

RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
RUN dpkg -i cuda-keyring_1.0-1_all.deb

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