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This might lead to slightly more code duplication, but a much more flexible code base and much easier to understand.\r\n* Introduce the concept of  `Deployment` and `Runtime`. `Deployment` describe the behavior while packaging and deploying the code while `Runtime` describe the behavior on the remote target (i.e. a TFJob on Kubernetes). Currently `Runtime` is pretty simple, but will be useful once we re-introduce hyperparameter tuning and Population based training\r\n* Each provider (native, kubeflow) now can export several decorators (e.g. `Training` and `DistributedTraining`) instead of always working with `Train`. Issue with the previous approach is that `Train` ended up having tons of parameters which may or may not apply to your particular training, it was confusing.\r\n* Remove dependency on metaparticle (fixes #8) and allow each step of the build/deployment to easily modify the generated k8s objects. 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