The audience of PySyft largely consists of people who would like to train their model on private data that reside on other devices/locations.
Right now one has to manually spin-up Google Cloud Machines, load a PyGrid instance, queue and run training jobs, deposit the results to another long-running instance(Master) and teardown the created instances(Workers).
The project aims to implement functionality necessary to automatically spin-up Google cloud machines, load a PyGrid instance, run a training job, and tear down the instance upon completion (depositing the results into another long-running instance). The primary feature will be the ability to run a “hyperparameter sweep”.
The sweep function will kill the nodes(Workers) upon completion of training and depositing the results into another long-running instance(Master).