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This document serves as a place for brainstorming ideas for Model & Dataset CRD design. The general goal is to design reusable CRDs that can be shared by various higher level machine learning tasks and frameworks.

Goals

• What does do the CRDs CRD controllers do? Define the exact responsibilities of model & dataset CRDs and controllers.
• How will the higher level tasks, i.e. federated learning, model serving etc, utilize the services provided by model & dataset CRDs. 

Non-goals

• The truly format of the AI dataset, such as imagenet, coco or tf-record etc.

• The truly format of the AI model, such as ckpt, saved_model of tensorflow etc.

• The truly operations of the AI dataset, such as shuffle, crop etc.

• The truly operations of the AI model, such as train, inference etc.

Use Cases

• Users can create the dataset resource, by providing the dataset url, format and the nodeName which owns the dataset.
• Users can create the model resource by providing the model url and format.
• Users can show the information of dataset/model.
• Users can delete the dataset/model.Cloud edge communication mechanism for both controllers and specific task workers.

Use Cases

Model serving

Upon creating a model CRD object, model controller

Design Details