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* checkout qq group qrcode * update the cover image * update build doc * reorganize chapters * update readme * remove index of build on different platforms in readthedocs * update benchmark * update get started document in Chinese based on the prebuild package * update get_started * re-style benchmark * update get_started in zh_cn * update get_started in english * update get_started in english * update get_started in english * update get_started doc * update according to reviewer comments * update linker ci * fix(.github/scripts/check_doc_linker.py): skip code block * specify PYTHONPATH * update get_started * update diagram * rename some documents * fix according to reviewer comments Co-authored-by: tpoisonooo <khj.application@aliyun.com>
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Use Docker Image
We provide two dockerfiles for CPU and GPU respectively. For CPU users, we install MMDeploy with ONNXRuntime, ncnn and OpenVINO backends. For GPU users, we install MMDeploy with TensorRT backend. Besides, users can install mmdeploy with different versions when building the docker image.
Build docker image
For CPU users, we can build the docker image with the latest MMDeploy through:
cd mmdeploy
docker build docker/CPU/ -t mmdeploy:master-cpu
For GPU users, we can build the docker image with the latest MMDeploy through:
cd mmdeploy
docker build docker/GPU/ -t mmdeploy:master-gpu
For installing MMDeploy with a specific version, we can append --build-arg VERSION=${VERSION} to build command. GPU for example:
cd mmdeploy
docker build docker/GPU/ -t mmdeploy:0.1.0 --build-arg VERSION=0.1.0
For installing libs with the aliyun source, we can append --build-arg USE_SRC_INSIDE=${USE_SRC_INSIDE} to build command.
# GPU for example
cd mmdeploy
docker build docker/GPU/ -t mmdeploy:inside --build-arg USE_SRC_INSIDE=true
# CPU for example
cd mmdeploy
docker build docker/CPU/ -t mmdeploy:inside --build-arg USE_SRC_INSIDE=true
Run docker container
After building the docker image succeed, we can use docker run to launch the docker service. GPU docker image for example:
docker run --gpus all -it -p 8080:8081 mmdeploy:master-gpu
FAQs
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CUDA error: the provided PTX was compiled with an unsupported toolchain:
As described here, update the GPU driver to the latest one for your GPU.
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docker: Error response from daemon: could not select device driver "" with capabilities: gpu.
# Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker