feature enhancements tracking issue

[llhuii]

This issue is to track these known enhancements to be done:

• [x] add resource limits for worker
• [ ] add gpu support for worker
• [x] add pod template like support for worker spec
• [x] add model management with central storage support
• [x] add dataset management with central storage support
• [ ] add example code style checker
• [x] add descriptions for CRD fields
• [ ] abstract the worker controller into one, currently each feature controller has own similarity worker implementation
• [x] move the feature CR logic embedded in upstream/downstream to respective feature controller
• [ ] replace self-built websocket between gm and lc with KubeEdge message communication
• [ ] improve the state translation implementation of incremental learning
• [ ] make the python lib interface more clearer
• [ ] model serving should support hot loading & multiple models
• [ ] the basic TensorFlow images in the examples needs to be unified to one version
• [ ] the networking differences need to be considered when the LC is deployed on the cloud

[jaypume]

• [ ] make the python lib interface more clearer
• [ ] model serving should support hot loading & multiple models
• [ ] manually copying training script to edge node is not recommended
• [ ] the basic TensorFlow images in the examples needs to be unified to one version
• [ ] the networking differences need to be considered when the LC is deployed on the cloud

[llhuii]

• [ ] make the python lib interface more clearer
• [ ] model serving should support hot loading & multiple models
• [ ] manually copying training script to edge node is not recommended
• [ ] the basic TensorFlow images in the examples needs to be unified to one version
• [ ] the networking differences need to be considered when the LC is deployed on the cloud

Added

[kevin-wangzefeng]

To follow up for the feature enhancements, it's recommanded to create separated tracking issues for each items.