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import torch as th



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import syft as sy



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sy.create_sandbox(globals(), False, False)









    



Setting up Sandbox...
Done!

Step 1: Action Events



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alices_params = th.tensor([1,2,3,4]).send(alice)
bobs_params = th.tensor([1,2,3,4]).send(bob)



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input_data = th.tensor([1,2,3,4])



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# these ways of forming promises are identical
alice_data_promise = sy.promise(alice, id=input_data.id)
bob_data_promise = input_data.promise(bob)



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alices.promise_queue = {} 
# key = tensor ID
# value = list of function which required this tensor ID

# whenever alices.recv_obj is called, after it serializes the object, it checks the keys in the queue to see if there are any outstanding commands



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alices_result_promise = alices_params * alice_data_promise



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bobs_result_promise = bobs_params * bobs_data_promise



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new_averaged_model = (alices_result_promise.get() + bobs_result_promise.get())/2



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# ASYNC using sockets

# input_data.send(bob)
# input_data.send(alice)

alice_data_promise.fulfill(input_data)
bobs_data_promise.fulfill(input_data)



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print(new_averaged_model.get())



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Scratch



In [5]:

    
class Promise():
    
    def __init__(self, id):
        
        self.operations = list()
        self.trigger_id = id
        
    def fulfill(self, tensor):
        ""
        
    
    def __add__(self, other_promise):
        return



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x = Promise(10)



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y = x + x



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y



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