[Feature]: Add github workflow based on self hosted runner (#1751)

* add win10 workflow

* update

* update and add dockerfile

* update script

* add build docker image script

* update

* support cuda10.2

* update docker file

* update build image tag

* add release docker file

* update release dockerfile

* fix ncnn python build

* test docker workflow

* remove docker build workflow

* fix comments

* add conda-incubator

* update build script and workflow

* fix comments

.circleci/scripts/linux/build.sh

@@ -2,8 +2,10 @@
 
 ARGS=("$@")
 
-cd mmdeploy
+SCRIPT_DIR=$(cd `dirname $0`; pwd)
-MMDEPLOY_DIR=$(pwd)
+MMDEPLOY_DIR=$SCRIPT_DIR/../../..
+
+cd $MMDEPLOY_DIR
 mkdir -p build && cd build
 cmake .. -DMMDEPLOY_BUILD_SDK=ON -DMMDEPLOY_BUILD_TEST=ON -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
          -DMMDEPLOY_BUILD_EXAMPLES=ON -DMMDEPLOY_BUILD_SDK_CSHARP_API=ON \

.circleci/scripts/linux/test_full_pipeline.sh

@@ -0,0 +1,104 @@
+#!/bin/sh
+
+set -e
+# print env
+#python3 tools/check_env.py
+backend=${1:-ort}
+device=${2:-cpu}
+current_dir=$(cd `dirname $0`; pwd)
+mmdeploy_dir=$current_dir/../../..
+cd $mmdeploy_dir
+
+work_dir=$mmdeploy_dir/work_dir
+mkdir -p $work_dir $mmdeploy_dir/data
+
+model_cfg=$work_dir/resnet18_8xb32_in1k.py
+checkpoint=$work_dir/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+sdk_cfg=configs/mmcls/classification_sdk_dynamic.py
+input_img=tests/data/tiger.jpeg
+
+python3 -m mim download mmcls --config resnet18_8xb32_in1k --dest $work_dir
+
+if [ $backend == "ort" ]; then
+    deploy_cfg=configs/mmcls/classification_onnxruntime_dynamic.py
+    model=$work_dir/end2end.onnx
+elif [ $backend == "trt" ]; then
+    deploy_cfg=configs/mmcls/classification_tensorrt-fp16_dynamic-224x224-224x224.py
+    model=$work_dir/end2end.engine
+elif [ $backend == "ncnn" ]; then
+    deploy_cfg=configs/mmcls/classification_ncnn_static.py
+    model="$work_dir/end2end.param $work_dir/end2end.bin"
+else
+  echo "Unsupported Backend=$backend"
+  exit
+fi
+
+echo "------------------------------------------------------------------------------------------------------------"
+echo "deploy_cfg=$deploy_cfg"
+echo "model_cfg=$model_cfg"
+echo "checkpoint=$checkpoint"
+echo "device=$device"
+echo "------------------------------------------------------------------------------------------------------------"
+
+python3 tools/deploy.py \
+  $deploy_cfg \
+  $model_cfg \
+  $checkpoint \
+  $input_img \
+  --device $device \
+  --work-dir $work_dir \
+  --dump-info
+
+if [ $backend == "trt" ]; then
+    echo "Running onnx2tensorrt"
+    python3 tools/onnx2tensorrt.py \
+    $deploy_cfg \
+    $work_dir/end2end.onnx \
+    $work_dir/temp
+fi
+
+# prepare dataset
+wget -P data/ https://github.com/open-mmlab/mmdeploy/files/9401216/imagenet-val100.zip
+unzip data/imagenet-val100.zip -d data/
+
+echo "Running test with $backend"
+
+python3 tools/test.py \
+  $deploy_cfg \
+  $model_cfg \
+  --model $model \
+  --device $device \
+  --log2file $work_dir/test_ort.log \
+  --speed-test \
+  --log-interval 50 \
+  --warmup 20 \
+  --batch-size 8
+
+echo "Running test with sdk"
+
+# change topk for test
+sed -i 's/"topk": 5/"topk": 1000/g' work_dir/pipeline.json
+
+python3 tools/test.py \
+  $sdk_cfg \
+  $model_cfg \
+  --model $work_dir \
+  --device $device \
+  --log2file $work_dir/test_sdk.log \
+  --speed-test \
+  --log-interval 50 \
+  --warmup 20 \
+  --batch-size 8
+
+# test profiler
+echo "Profile sdk model"
+python3 tools/profiler.py \
+  $sdk_cfg \
+  $model_cfg \
+  ./data \
+  --model $work_dir \
+  --device $device \
+  --batch-size 8 \
+  --shape 224x224
+
+echo "All done"

.circleci/scripts/windows/test_full_pipeline.ps1

@@ -0,0 +1,110 @@
+<#
+.SYNOPSIS
+A helper script to test tools of MMDeploy Converter on windows.
+
+.Description
+    -Backend: support ort, trt
+    -Device: support cpu, cuda, cuda:0
+
+.EXAMPLE
+PS> .circleci/scripts/linux/test_full_pipeline.sh -Backend ort -Device cpu
+#>
+
+param(
+    [Parameter(Mandatory = $true)]
+    [string] $Backend,
+    [string] $Device
+)
+
+$MMDeploy_DIR="$PSScriptRoot\..\..\.."
+Set-Location $MMDeploy_DIR
+
+$work_dir="work_dir"
+New-Item -Path $work_dir, .\data -ItemType Directory -Force
+$model_cfg="$work_dir\resnet18_8xb32_in1k.py"
+$checkpoint="$work_dir\resnet18_8xb32_in1k_20210831-fbbb1da6.pth"
+$sdk_cfg="configs\mmcls\classification_sdk_dynamic.py"
+$input_img="tests\data\tiger.jpeg"
+
+python -m mim download mmcls --config resnet18_8xb32_in1k --dest $work_dir
+
+if ($Backend -eq "ort") {
+    $deploy_cfg="configs\mmcls\classification_onnxruntime_dynamic.py"
+    $model="$work_dir\end2end.onnx"
+} elseif ($Backend -eq "trt") {
+    $deploy_cfg="configs\mmcls\classification_tensorrt-fp16_dynamic-224x224-224x224.py"
+    $model="$work_dir\end2end.engine"
+} else {
+    Write-Host "Unsupported Backend=$Backend"
+    Exit
+}
+
+Write-Host "--------------------------------------"
+Write-Host "deploy_cfg=$deploy_cfg"
+Write-Host "$model_cfg=$model_cfg"
+Write-Host "$checkpoint=$checkpoint"
+Write-Host "device=$device"
+Write-Host "--------------------------------------"
+
+python tools\deploy.py `
+  $deploy_cfg `
+  $model_cfg `
+  $checkpoint `
+  $input_img `
+  --device $device `
+  --work-dir $work_dir `
+  --dump-info
+
+# prepare dataset
+Invoke-WebRequest -Uri https://github.com/open-mmlab/mmdeploy/files/9401216/imagenet-val100.zip -OutFile $pwd\data\imagenet-val100.zip
+Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$pwd\data\imagenet-val100.zip", "$pwd\data\")
+
+Write-Host "Running test with ort"
+python tools\test.py `
+  $deploy_cfg `
+  $model_cfg `
+  --model $model `
+  --device $device `
+  --log2file $work_dir\test_ort.log `
+  --speed-test `
+  --log-interval 50 `
+  --warmup 20 `
+  --batch-size 8
+
+
+Write-Host "Prepare dataset"
+# change topk for test
+$src_topk='"topk": 5'
+$dst_topk='"topk": 1000'
+$src_pipeline_file="$work_dir\pipeline.json"
+$tmp_pipeline_file="$work_dir\pipeline_tmp.json"
+Move-Item $src_pipeline_file $tmp_pipeline_file -force
+(Get-Content -Path $tmp_pipeline_file) -replace $src_topk, $dst_topk | Add-Content -Path $src_pipeline_file
+
+Write-Host "test sdk model"
+
+python tools\test.py `
+  $sdk_cfg `
+  $model_cfg `
+  --model $work_dir `
+  --device $device `
+  --log2file $work_dir\test_sdk.log `
+  --speed-test `
+  --log-interval 50 `
+  --warmup 20 `
+  --batch-size 8`
+
+# test profiler
+Write-Host "Profile sdk model"
+python tools\profiler.py `
+  $sdk_cfg `
+  $model_cfg `
+  .\data `
+  --model $work_dir `
+  --device $device `
+  --batch-size 8 `
+  --shape 224x224
+
+# remove temp data
+Remove-Item -Path "$work_dir" -Force -Recurse
+Write-Host "All done"

.github/scripts/test_mmcls_full_pipeline.sh

@@ -1,67 +0,0 @@
-#!/bin/sh
-
-set -e
-# print env
-python tools/check_env.py
-
-deploy_cfg=configs/mmcls/classification_onnxruntime_dynamic.py
-device=cpu
-python -m mim download mmcls --config resnet18_8xb32_in1k --dest ../
-model_cfg=../resnet18_8xb32_in1k.py
-checkpoint=../resnet18_8xb32_in1k_20210831-fbbb1da6.pth
-sdk_cfg=configs/mmcls/classification_sdk_dynamic.py
-input_img=tests/data/tiger.jpeg
-work_dir=work_dir
-
-echo "------------------------------------------------------------------------------------------------------------"
-echo "deploy_cfg=$deploy_cfg"
-echo "model_cfg=$model_cfg"
-echo "checkpoint=$checkpoint"
-echo "device=$device"
-echo "------------------------------------------------------------------------------------------------------------"
-
-mkdir -p $work_dir
-
-python tools/deploy.py \
-  $deploy_cfg \
-  $model_cfg \
-  $checkpoint \
-  $input_img \
-  --device $device \
-  --work-dir $work_dir \
-  --dump-info
-
-# prepare dataset
-wget -P data/ https://github.com/open-mmlab/mmdeploy/files/9401216/imagenet-val100.zip
-unzip data/imagenet-val100.zip -d data/
-
-echo "Running test with ort"
-
-python tools/test.py \
-  $deploy_cfg \
-  $model_cfg \
-  --model $work_dir/end2end.onnx \
-  --device $device \
-  --device $device \
-  --log2file $work_dir/test_ort.log \
-  --speed-test \
-  --log-interval 50 \
-  --warmup 20 \
-  --batch-size 32
-
-echo "Running test with sdk"
-
-# change topk for test
-sed -i 's/"topk": 5/"topk": 1000/g' work_dir/pipeline.json
-
-python tools/test.py \
-  $sdk_cfg \
-  $model_cfg \
-  --model $work_dir \
-  --device $device \
-  --device $device \
-  --log2file $work_dir/test_sdk.log \
-  --speed-test \
-  --log-interval 50 \
-  --warmup 20 \
-  --batch-size 1

.github/workflows/backend-ort.yml

@@ -43,4 +43,4 @@ jobs:
           export MMDEPLOY_DIR=$(pwd)
           export ONNXRUNTIME_DIR=$MMDEPLOY_DIR/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1
           export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$MMDEPLOY_DIR/build/install/lib:$LD_LIBRARY_PATH
-          bash .github/scripts/test_mmcls_full_pipeline.sh
+          bash .circleci/scripts/linux/test_full_pipeline.sh ort cpu

.github/workflows/build.yml

@@ -218,3 +218,106 @@ jobs:
           env_vars: OS,PYTHON,CPLUS
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  build_cuda113_linux:
+    runs-on: [self-hosted, linux-3090]
+    container:
+      image: openmmlab/mmdeploy:ubuntu20.04-cuda11.3
+      options: "--gpus=all --ipc=host"
+      #volumes:
+      #  - /data2/checkpoints:/__w/mmdeploy/mmdeploy_checkpoints
+      #  - /data2/benchmark:/__w/mmdeploy/data
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        with:
+          submodules: recursive
+      - name: Install dependencies
+        run: |
+          apt update && apt install unzip
+          python3 -V
+          python3 -m pip install opencv-python==4.5.4.60 opencv-python-headless==4.5.4.60 opencv-contrib-python==4.5.4.60
+          python3 -m pip install openmim numpy pycuda clip
+          python3 -m pip install -r requirements.txt
+          python3 -m mim install $(cat requirements/codebases.txt | grep mmcls)
+          python3 -m pip list
+      - name: Build SDK
+        run: |
+          export Torch_DIR=$(python3 -c "import torch;print(torch.utils.cmake_prefix_path + '/Torch')")
+          bash .circleci/scripts/linux/build.sh "cpu;cuda" "ort;trt;ncnn;torchscript" \
+              -Dpplcv_DIR=${pplcv_DIR} \
+              -DTENSORRT_DIR=${TENSORRT_DIR} \
+              -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} \
+              -Dncnn_DIR=${ncnn_DIR} \
+              -DTorch_DIR=${Torch_DIR}
+          ls build/lib
+      - name: Install converter
+        run: |
+          rm -rf .eggs && python3 -m pip install -e .
+          export LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${LD_LIBRARY_PATH}"
+          python3 tools/check_env.py
+      - name: Test TensorRT pipeline
+        run: |
+          export LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${LD_LIBRARY_PATH}"
+          export LD_LIBRARY_PATH="/root/workspace/mmdeploy/mmdeploy/lib:${LD_LIBRARY_PATH}"
+          bash .circleci/scripts/linux/test_full_pipeline.sh trt cuda
+
+  build_cuda113_windows:
+    runs-on: [self-hosted, win10-3080]
+    env:
+      BASE_ENV: cuda11.3-cudnn8.2-py3.8-torch1.10
+    defaults:
+      run:
+        shell: powershell
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        with:
+          submodules: 'recursive'
+      - name: Setup Python Environment
+        run: |
+          echo "============================== Info =============================="
+          echo "env:path= $env:path"
+          echo "============================== Info =============================="
+          conda info
+          conda info -e
+          conda create -p $pwd\tmp_env --clone $env:BASE_ENV -y
+          conda activate $pwd\tmp_env
+          python -V
+          python -m pip install openmim
+          python -m pip install -r requirements.txt -r requirements/backends.txt
+          python -m mim install "mmcls>=1.0.0rc2"
+          python -m pip list
+      - name: Build mmdeploy
+        run: |
+          conda activate $pwd\tmp_env
+          python -V
+          mkdir build
+          cd build
+          cmake ..  -A x64 -T v142 `
+            -DMMDEPLOY_BUILD_TEST=ON `
+            -DMMDEPLOY_BUILD_SDK_CSHARP_API=ON `
+            -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON `
+            -DMMDEPLOY_BUILD_SDK=ON `
+            -DMMDEPLOY_TARGET_DEVICES='cuda' `
+            -DMMDEPLOY_TARGET_BACKENDS='ort;trt' `
+            -DMMDEPLOY_CODEBASES='all' `
+            -Dpplcv_DIR="$env:PPLCV_DIR\pplcv-build\install\lib\cmake\ppl" `
+            -DOpenCV_DIR="$env:OPENCV_DIR\build\x64\vc15\lib" `
+            -DTENSORRT_DIR="$env:TENSORRT_DIR" `
+            -DONNXRUNTIME_DIR="$env:ONNXRUNTIME_DIR" `
+            -DMMDEPLOY_BUILD_EXAMPLES=ON `
+            -DCUDNN_DIR="$env:CUDNN_DIR"
+          cmake --build . --config Release -- /m
+          cmake --install . --config Release
+          ls $pwd\bin\Release
+      - name: Install mmdeploy converter
+        run: |
+          conda activate $pwd\tmp_env
+          python -m pip install -e .
+          python .\tools\check_env.py
+      - name: Test trt full pipeline
+        run: |
+          conda activate $pwd\tmp_env
+          $env:path = "$pwd\build\bin\Release;" + $env:path
+          .circleci\scripts\windows\test_full_pipeline.ps1 -Backend trt -Device cuda

docker/Base/Dockerfile

@@ -0,0 +1,110 @@
+ARG CUDA_INT=113
+
+FROM nvidia/cuda:11.3.0-cudnn8-devel-ubuntu20.04 AS cuda-113
+FROM nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04 AS cuda-102
+
+FROM cuda-${CUDA_INT} AS final
+
+ARG TORCH_VERSION=1.10.0
+ARG TORCHVISION_VERSION=0.11.0
+
+# important dependencies
+ARG OPENCV_VERSION==4.5.4.60
+ARG PPLCV_VERSION=0.7.0
+
+# backends
+ARG ONNXRUNTIME_VERSION=1.8.1
+ARG PPLNN_VERSION=0.8.1
+ARG NCNN_VERSION=20221128
+ARG TENSORRT_VERSION=8.2.3.0
+# tensorrt tar file url
+ARG TENSORRT_URL
+
+USER root
+WORKDIR /root/workspace
+
+ENV DEBIAN_FRONTEND=nointeractive
+ENV FORCE_CUDA="1"
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        apt-utils \
+        ca-certificates \
+        gcc-7 \
+        g++-7 \
+        git \
+        vim \
+        wget \
+        libopencv-dev \
+        libprotobuf-dev protobuf-compiler \
+        unzip \
+        python3-dev python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+
+# install jdk, onnxruntime, openvino, and other python packages
+RUN wget https://download.java.net/java/GA/jdk18/43f95e8614114aeaa8e8a5fcf20a682d/36/GPL/openjdk-18_linux-x64_bin.tar.gz &&\
+    tar xvf openjdk-18_linux-x64_bin.tar.gz && rm -rf openjdk-18_linux-x64_bin.tar.gz && \
+    wget https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz &&\
+    tar -xzvf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz && rm onnxruntime-*.tgz &&\
+    wget https://github.com/Kitware/CMake/releases/download/v3.25.2/cmake-3.25.2-linux-x86_64.tar.gz &&\
+    tar -xzvf cmake-3.25.2-linux-x86_64.tar.gz && rm cmake-*.tar.gz && mv cmake-* cmake &&\
+    export CUDA_INT=$(echo $CUDA_VERSION | awk '{split($0, a, "."); print a[1]a[2]}') &&\
+    python3 -m pip install --no-cache-dir --upgrade pip setuptools wheel &&\
+    python3 -m pip install --no-cache-dir onnxruntime-gpu==${ONNXRUNTIME_VERSION} &&\
+    python3 -m pip install --no-cache-dir openvino openvino-dev[onnx] &&\
+    python3 -m pip install --no-cache-dir opencv-python==${OPENCV_VERSION} opencv-python-headless==${OPENCV_VERSION} opencv-contrib-python==${OPENCV_VERSION} &&\
+    python3 -m pip install --no-cache-dir torch==${TORCH_VERSION}+cu${CUDA_INT} torchvision==${TORCHVISION_VERSION}+cu${CUDA_INT} -f https://download.pytorch.org/whl/torch_stable.html
+
+# create env
+ENV JAVA_HOME=/root/workspace/jdk-18
+ENV PATH=$JAVA_HOME/bin:/root/workspace/cmake/bin:$PATH
+ENV ONNXRUNTIME_VERSION=${ONNXRUNTIME_VERSION}
+ENV ONNXRUNTIME_DIR=/root/workspace/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}
+ENV LD_LIBRARY_PATH=${ONNXRUNTIME_DIR}/lib:$LD_LIBRARY_PATH
+
+### install ppl.nn
+RUN git clone --depth 1 --branch v${PPLNN_VERSION} --recursive https://github.com/openppl-public/ppl.nn.git &&\
+    cd ppl.nn &&\
+    export PYTHON_INCLUDE_DIR=$(python3 -c 'import sysconfig;print(sysconfig.get_paths()["include"])') &&\
+    ./build.sh -DPPLNN_USE_X86_64=ON -DPPLNN_USE_CUDA=ON -DPPLNN_ENABLE_PYTHON_API=ON -DPYTHON3_INCLUDE_DIRS=${PYTHON_INCLUDE_DIR} &&\
+    cd ./python/package && \
+    ./build.sh && \
+    cd - && mv pplnn-build/install ./ && rm -rf pplnn-build &&\
+    cd /tmp/pyppl-package/dist && \
+    python3 -m pip install pyppl*.whl && rm *.whl
+
+ENV pplnn_DIR=/root/workspace/ppl.nn/install/lib/cmake/ppl
+ENV PYTHONPATH=/root/workspace/ppl.nn/install/lib:$PYTHONPATH
+
+
+### build ncnn
+RUN git clone --depth 1 --branch ${NCNN_VERSION} --recursive https://github.com/Tencent/ncnn.git &&\
+    python3 -m pip install pybind11 &&\
+    mkdir ncnn/build && cd ncnn/build &&\
+    cmake -DNCNN_VULKAN=OFF -DNCNN_PYTHON=ON -DNCNN_BUILD_TOOLS=OFF -DCMAKE_INSTALL_PREFIX=../install .. &&\
+    make -j $(nproc) && make install &&\
+    cd .. && python3 -m pip install . && rm -rf ./build
+
+ENV ncnn_DIR=/root/workspace/ncnn/install/lib/cmake/ncnn
+
+### install ppl.cv
+RUN git clone --depth 1 --branch v${PPLCV_VERSION} https://github.com/openppl-public/ppl.cv.git  &&\
+    cd ppl.cv &&\
+    ./build.sh cuda && mv cuda-build/install ./ && rm -rf cuda-build
+
+ENV pplcv_DIR=/root/workspace/ppl.cv/install/lib/cmake/ppl
+
+### install tensorrt
+RUN wget -c $TENSORRT_URL && \
+    tar -zxvf /root/workspace/TensorRT-${TENSORRT_VERSION}*.tar.gz -C /root/workspace &&\
+    rm -rf TensorRT-${TENSORRT_VERSION}*.tar.gz &&\
+    ln -sf /root/workspace/TensorRT-${TENSORRT_VERSION} /root/workspace/TensorRT &&\
+    cd /root/workspace/TensorRT && rm -rf data doc samples uff &&\
+    export PY_VERSION=$(python3 -V | awk '{print $2}' | awk '{split($0, a, "."); print a[1]a[2]}') &&\
+    python3 -m pip install ./python/tensorrt-*-cp${PY_VERSION}-none-linux_x86_64.whl
+
+ENV TENSORRT_DIR=/root/workspace/TensorRT
+ENV LD_LIBRARY_PATH=$TENSORRT_DIR/lib:$LD_LIBRARY_PATH
+ENV PATH=$TENSORRT_DIR/bin:$PATH

docker/Release/Dockerfile

@@ -0,0 +1,25 @@
+FROM openmmlab/mmdeploy:ubuntu20.04-cuda11.3
+
+ARG MMDEPLOY_VERSION
+
+ENV BACKUP_LD_LIBRARY_PATH=$LD_LIBRARY_PATH
+ENV LD_LIBRARY_PATH=/usr/local/cuda/compat:$LD_LIBRARY_PATH
+
+# build mmdeploy
+RUN git clone --recursive -b $MMDEPLOY_VERSION --depth 1 https://github.com/open-mmlab/mmdeploy.git &&\
+    export Torch_DIR=$(python3 -c "import torch;print(torch.utils.cmake_prefix_path + '/Torch')") &&\
+    bash mmdeploy/.circleci/scripts/linux/build.sh "cpu;cuda" "ort;trt;ncnn;torchscript" \
+        -Dpplcv_DIR=${pplcv_DIR} \
+        -DTENSORRT_DIR=${TENSORRT_DIR} \
+        -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} \
+        -Dncnn_DIR=${ncnn_DIR} \
+        -DTorch_DIR=${Torch_DIR} &&\
+    cd mmdeploy &&\
+    python3 -m pip install -U openmim pycuda &&\
+    python3 -m pip install -r requirements.txt &&\
+    python3 -m pip install -e .
+
+ENV MMDeploy_DIR="/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
+ENV PATH="/root/workspace/mmdeploy/build/bin:${PATH}"
+ENV PYTHONPATH="/root/workspace/mmdeploy/build/lib:${PYTHONPATH}"

tools/scripts/build_base_image.sh

@@ -0,0 +1,37 @@
+#!/bin/sh
+
+set -e
+
+ip=${1}
+port=${2:8585}
+
+date_today=`date +'%Y%m%d'`
+
+# create http server
+nohup python3 -m http.server --directory /data2/shared/nvidia $port > tmp.log 2>&1
+
+export TENSORRT_URL=http://$ip:$port/TensorRT-8.2.3.0.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz
+export TENSORRT_VERSION=8.2.3.0
+export CUDA_INT=113
+export TAG=ubuntu20.04-cuda11.3
+
+# build docker image
+docker build ./docker/Base/ -t openmmlab/mmdeploy:$TAG \
+    --build-arg CUDA_INT=$CUDA_INT \
+    --build-arg TENSORRT_URL=${TENSORRT_URL} \
+    --build-arg TENSORRT_VERSION=${TENSORRT_VERSION}
+
+docker tag openmmlab/mmdeploy:$TAG openmmlab/mmdeploy:${TAG}-${date_today}
+
+# test docker image
+docker run --gpus=all -itd \
+  -v /data2/benchmark:/root/workspace/openmmlab-data \
+  -v /data2/checkpoints:/root/workspace/mmdeploy_checkpoints \
+  -v ~/mmdeploy:/root/workspace/mmdeploy \
+  openmmlab/mmdeploy:$TAG
+
+
+# push to docker hub
+docker login
+docker push openmmlab/mmdeploy:$TAG
+docker push openmmlab/mmdeploy:$TAG-${date_today}