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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>
57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
## Use Docker Image
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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.
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### Build docker image
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For CPU users, we can build the docker image with the latest MMDeploy through:
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```
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cd mmdeploy
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docker build docker/CPU/ -t mmdeploy:master-cpu
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```
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For GPU users, we can build the docker image with the latest MMDeploy through:
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```
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cd mmdeploy
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docker build docker/GPU/ -t mmdeploy:master-gpu
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```
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For installing MMDeploy with a specific version, we can append `--build-arg VERSION=${VERSION}` to build command. GPU for example:
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```
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cd mmdeploy
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docker build docker/GPU/ -t mmdeploy:0.1.0 --build-arg VERSION=0.1.0
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```
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For installing libs with the aliyun source, we can append `--build-arg USE_SRC_INSIDE=${USE_SRC_INSIDE}` to build command.
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```
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# GPU for example
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cd mmdeploy
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docker build docker/GPU/ -t mmdeploy:inside --build-arg USE_SRC_INSIDE=true
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# CPU for example
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cd mmdeploy
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docker build docker/CPU/ -t mmdeploy:inside --build-arg USE_SRC_INSIDE=true
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```
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### Run docker container
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After building the docker image succeed, we can use `docker run` to launch the docker service. GPU docker image for example:
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```
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docker run --gpus all -it -p 8080:8081 mmdeploy:master-gpu
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```
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### FAQs
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1. CUDA error: the provided PTX was compiled with an unsupported toolchain:
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As described [here](https://forums.developer.nvidia.com/t/cuda-error-the-provided-ptx-was-compiled-with-an-unsupported-toolchain/185754), update the GPU driver to the latest one for your GPU.
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2. docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].
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```
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# Add the package repositories
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
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sudo systemctl restart docker
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```
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