...
For some algorithms, cross-edge synchronization is required. More details will be provided later.
Design Details
The model operator is supposed to run as a separate binary, fully decoupled from the KubeEdge platform code. It leverages the KubeEdge platform to schedule work on edge nodes. Here is the high level design diagram for the work flow:
There are generally two phases for machine learning model development, i.e. training and inference. Model behaviors are quite different depending on whether it is used for training or for inference. So we might as well define two different types of model CRDs:
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
apiVersion: ai.kubeedge.io/v1alpha1
kind: InferenceModel
metadata:
name: facialexpression
spec:
modelName: facialexpression
deployToLayer: edge
frameworkType: tensorflow
image:
nodeSelector:
kubernetes.io/hostname: iris-precision-5820
nodeName: iris-precision-5820
manifest:
- version: '3'
downloadURL: http://192.168.1.13/model_emotion_3.tar.gz
sha256sum: dec87e2f3c06e60e554acac0b2b80e394c616b0ecdf878fab7f04fd414a66eff
- version: '4'
downloadURL: http://192.168.1.13/model_emotion_4.tar.gz
sha256sum: 108a433a941411217e5d4bf9f43a262d0247a14c35ccbf677f63ba3b46ae6285
targetVersion: '4'
servingPort: 8080
replicas: 1 |
...