[Enhance]: update dockerfile (#1946)

docker/Dockerfile

-ARG PYTORCH="1.6.0"
-ARG CUDA="10.1"
-ARG CUDNN="7"
-ARG MMCV="1.5.0"
-ARG MMDET="2.19.0"
-ARG MMSEG="0.20.0"
+ARG PYTORCH="1.9.0"
+ARG CUDA="11.1"
+ARG CUDNN="8"
+
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
-ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
-ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
-ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"
+ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6+PTX" \
+    TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
+    CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
+    FORCE_CUDA="1"
+
+# Avoid Public GPG key error
+# https://github.com/NVIDIA/nvidia-docker/issues/1631
+RUN rm /etc/apt/sources.list.d/cuda.list \
+    && rm /etc/apt/sources.list.d/nvidia-ml.list \
+    && apt-key del 7fa2af80 \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
 
-# To fix GPG key error when running apt-get update
-RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
-RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+# (Optional, use Mirror to speed up downloads)
+# RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list && \
+#    pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
 
-RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+# Install the required packages
+RUN apt-get update \
+    && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
-# Install MMCV, MMDetection and MMSegmentation
-ARG PYTORCH
-ARG CUDA
-ARG MMCV
-ARG MMDET
-ARG MMSEG
-RUN ["/bin/bash", "-c", "pip install --no-cache-dir mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
-RUN pip install --no-cache-dir mmdet==${MMDET} mmsegmentation==${MMSEG}
+# Install MMEngine, MMCV and MMDetection
+RUN pip install openmim && \
+    mim install "mmengine" "mmcv>=2.0.0rc1" "mmdet>=3.0.0rc0"
 
 # Install MMDetection3D
-RUN conda clean --all
-COPY . /mmdetection3d
+RUN conda clean --all \
+    && git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x /mmdetection3d \
+    && cd /mmdetection \
+    && pip install --no-cache-dir -e .
+
 WORKDIR /mmdetection3d
-ENV FORCE_CUDA="1"
-RUN pip install -r requirements/build.txt
-RUN pip install --no-cache-dir -e .

docker/serve/Dockerfile

-ARG PYTORCH="1.6.0"
-ARG CUDA="10.1"
-ARG CUDNN="7"
+ARG PYTORCH="1.9.0"
+ARG CUDA="11.1"
+ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
-ARG MMCV="1.3.8"
-ARG MMSEGMENTATION="0.14.1"
-ARG MMDET="2.14.0"
-ARG MMDET3D="0.17.1"
+ARG MMCV="2.0.0rc4"
+ARG MMDET="3.0.0rc5"
+ARG MMDET3D="1.1.0rc3"
 
 ENV PYTHONUNBUFFERED TRUE
 
+# Avoid Public GPG key error
+# https://github.com/NVIDIA/nvidia-docker/issues/1631
+RUN rm /etc/apt/sources.list.d/cuda.list \
+    && rm /etc/apt/sources.list.d/nvidia-ml.list \
+    && apt-key del 7fa2af80 \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+# (Optional, use Mirror to speed up downloads)
+# RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list
+
+# Install the required packages
 RUN apt-get update && \
     DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
     ca-certificates \
@@ -19,8 +30,8 @@ RUN apt-get update && \
     ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
     && rm -rf /var/lib/apt/lists/*
 
-ENV PATH="/opt/conda/bin:$PATH"
-RUN export FORCE_CUDA=1
+ENV PATH="/opt/conda/bin:$PATH" \
+    FORCE_CUDA="1"
 
 # TORCHSEVER
 RUN pip install torchserve torch-model-archiver
@@ -28,11 +39,11 @@ RUN pip install torchserve torch-model-archiver
 # MMLAB
 ARG PYTORCH
 ARG CUDA
-RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
-RUN pip install mmdet==${MMDET}
-RUN pip install mmsegmentation==${MMSEGMENTATION}
-RUN pip install mmdet3d==${MMDET3D}
-
+RUN pip install openmim
+RUN mim install mmengine
+RUN mim install mmcv==${MMCV}
+RUN mim install mmdet==${MMDET}
+RUN mim install mmdet3d==${MMDET3D}
 
 RUN useradd -m model-server \
     && mkdir -p /home/model-server/tmp

docs/en/get_started.md

@@ -253,7 +253,7 @@ Within Jupyter, the exclamation mark `!` is used to call external executables an
 We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >= 19.03.
 
 ```shell
-# build an image with PyTorch 1.6, CUDA 10.1
+# build an image with PyTorch 1.9, CUDA 11.1
 # If you prefer other versions, just modified the Dockerfile
 docker build -t mmdetection3d docker/
 ```

docs/zh_cn/get_started.md

@@ -250,7 +250,7 @@ print(mmdet3d.__version__)
 我们提供了 [Dockerfile](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docker/Dockerfile) 来构建一个镜像。请确保您的 [docker 版本](https://docs.docker.com/engine/install/) >= 19.03。
 
 ```shell
-# 基于 PyTorch 1.6，CUDA 10.1 构建镜像
+# 基于 PyTorch 1.9，CUDA 11.1 构建镜像
 # 如果您想要其他版本，只需要修改 Dockerfile
 docker build -t mmdetection3d docker/
 ```