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

* 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
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RunningLeon 2023-03-21 11:44:11 +08:00 committed by GitHub
parent 754c08012d
commit 2fcc8dc19d
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9 changed files with 494 additions and 70 deletions

6
.circleci/scripts/linux/build.sh
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@ -2,8 +2,10 @@
ARGS=("$@")
cd mmdeploy
MMDEPLOY_DIR=$(pwd)
SCRIPT_DIR=$(cd `dirname $0`; pwd)
MMDEPLOY_DIR=$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 \

104
.circleci/scripts/linux/test_full_pipeline.sh 100644
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#!/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"

110
.circleci/scripts/windows/test_full_pipeline.ps1 100644
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<#
.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"

67
.github/scripts/test_mmcls_full_pipeline.sh vendored
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@ -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

2
.github/workflows/backend-ort.yml vendored
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@ -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

103
.github/workflows/build.yml vendored
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@ -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

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docker/Base/Dockerfile 100644
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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

25
docker/Release/Dockerfile 100644
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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}"

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tools/scripts/build_base_image.sh 100644
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@ -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}