sedna部署及Joint Inference样例演示

sedna部署及Joint Inference样例演示

SeDna部署及Joint Inference样例演示

1、 简介

KubeEdge SIG AI致力于解决AI在边缘落地过程中的上述挑战，提升边缘AI的性能和效率。结合前期将边云协同机制运用在AI场景的探索，AI SIG成员联合发起了Sedna子项目，将最佳实践经验固化到该项目中。

Sedna基于KubeEdge提供的边云协同能力，实现AI的跨边云协同训练和协同推理能力，支持业界主流的AI框架，包括TensorFlow/Pytorch/PaddlePaddle/MindSpore等，支持现有AI类应用无缝下沉到边缘，快速实现跨边云的增量学习，联邦学习，协同推理等能力，最终达到降低成本、提升模型性能、保护数据隐私等效果。

2、master节点查看主节点名称

kubectl get node -o wide

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SEDNA_GM_NODE设为主节点名称

SEDNA_GM_NODE=master
curl .sh | SEDNA_GM_NODE=$SEDNA_GM_NODE SEDNA_ACTION=create bash -

若网络不良，下载到本地后执行

export SEDNA_ROOT=/opt/sedna
SEDNA_GM_NODE=master
curl .sh | SEDNA_GM_NODE=$SEDNA_GM_NODE SEDNA_ACTION=create bash -# Check the GM status:
kubectl get deploy -n sedna gm
# Check the LC status:
kubectl get ds lc -n sedna
# Check the pod status:
kubectl get pod -n sedna

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3、edge安装kubectl

1.下载安装Kubectl

curl -LO .17.0/bin/linux/amd64/kubectl

2.使kubectl二进制可执行文件

chmod +x ./kubectl

3.将二进制文件移到PATH中

sudo mv ./kubectl /usr/local/bin/kubectl

4.测试安装版本

kubectl version --client

【The connection to the server localhost:8080 was refused - did you specify the right host or port?】

将master节点下/etc/f复制到edge节点，配置环境变量

vim /etc/profile
export KUBECONFIG=/etc/f【修改地址】
source /etc/profile

4、准备镜像

注释掉其他实验Dockerfile，本案例master保留big,edge保留little

./home/edge/sedna/examples/build_image.sh

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5、创建服务

Create Big Model Resource Object for Cloud

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind:  Model
metadata:name: helmet-detection-inference-big-modelnamespace: default
spec:url: "/data/big-model/yolov3_darknet.pb"format: "pb"
EOF

Create Little Model Resource Object for Edge

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:name: helmet-detection-inference-little-modelnamespace: default
spec:url: "/data/little-model/yolov3_resnet18.pb"format: "pb"
EOF

边缘端：

mkdir -p /joint_inference/output

Create JointInferenceService

[注]修改节点名字，以及镜像版本

CLOUD_NODE=“cloud-node-name”
EDGE_NODE=“edge-node-name”

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: JointInferenceService
metadata:name: helmet-detection-inference-examplenamespace: default
spec:edgeWorker:model:name: "helmet-detection-inference-little-model"hardExampleMining:name: "IBT"parameters:- key: "threshold_img"value: "0.9"- key: "threshold_box"value: "0.9"template:spec:nodeName: $EDGE_NODEcontainers:- image: kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.4.0imagePullPolicy: IfNotPresentname:  little-modelenv:  # user defined environments- name: input_shapevalue: "416,736"- name: "video_url"value: "rtsp://localhost/video"- name: "all_examples_inference_output"value: "/data/output"- name: "hard_example_cloud_inference_output"value: "/data/hard_example_cloud_inference_output"- name: "hard_example_edge_inference_output"value: "/data/hard_example_edge_inference_output"resources:  # user defined resourcesrequests:memory: 64Mcpu: 100mlimits:memory: 2GivolumeMounts:- name: outputdirmountPath: /data/volumes:   # user defined volumes- name: outputdirhostPath:# user must create the directory in hostpath: /joint_inference/outputtype: DirectorycloudWorker:model:name: "helmet-detection-inference-big-model"template:spec:nodeName: $CLOUD_NODEcontainers:- image: kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.4.0name:  big-modelimagePullPolicy: IfNotPresentenv:  # user defined environments- name: "input_shape"value: "544,544"resources:  # user defined resourcesrequests:memory: 2Gi
EOF

Check Joint Inference Status

kubectl get jointinferenceservices.sedna.io

Mock Video Stream for Inference in Edge Side

wget .1.0/EasyDarwin-linux-8.1.
tar -zxvf EasyDarwin-linux-8.1.
cd EasyDarwin-linux-8.1.0-1901141151
./start.shmkdir -p /data/video
cd /data/video
wget .
tar -zxvf ffmpeg -re -i /data/video/video.mp4 -vcodec libx264 -f rtsp rtsp://localhost/video

结果位于/joint_inference/output文件夹下。