Provide GPU version of lifelong cityscapes example. #414
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| If you want GPU to accelerate training or inference in Sedna, you can follow the steps below to enable GPU: | ||
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| > 1. Follow the instructions in [nvidia-device-plugin](https://github.com/NVIDIA/k8s-device-plugin#quick-start) to make nvidia-docker to be docker runtime. | ||
| > 2. Set config `devicePluginEnabled` to `true` and restart edgecore in the gpu edge node. |
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How to restart edge core? Show a link to the guidance will be better.
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OK, link of KubeEdge docs has been added.
Signed-off-by: Jie Pu <[email protected]>
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\lgtm |
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| > 1. Follow the instructions in [nvidia-device-plugin](https://github.com/NVIDIA/k8s-device-plugin#quick-start) to make nvidia-docker to be docker runtime. | ||
| > 2. Set config `devicePluginEnabled` to `true` and restart edgecore in the gpu edge node. Please refer to this [doc](https://kubeedge.io/docs/setup/config#configuration-edge-side-kubeedge-worker-node) for configuring edgecore. |
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How should the edge container be configured when the runtime is containerd？
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Currently not tested on containerd for edge. And if it is supported by Nvidia for kubernetes, it will support for kubeedge and also Sedna.
What type of PR is this?
kind/documentation
What this PR does / why we need it:
Provide GPU version of lifelong cityscapes example.