Design choices of implementing an ensemble classifier

Ray Core
2

My final solution is this:

  • instead of using actors, use normal Python classes and Ray functions
  • each call of the Ray function creates a component classifier, fits it, and returns it
  • subsequent use of the classifiers (e.g., making predictions) depend on using the returned component classifiers.

A working MWE is below:

import ray
import time

class MyClass:
    def fit(self, value):
        time.sleep(1)
        print('done with fitting value {}'.format(value))
        return value

    def predict(self, value):
        time.sleep(1)
        print('done with prediction value {}'.format(value))
        return value

@ray.remote(num_cpus=2)
def _fit(value):
    obj = MyClass()
    obj.fit(value)
    return obj

ray.init(num_cpus=4)  # we request 4 cpus

obj_ids = [_fit.remote(v) for v in range(4)]

@ray.remote
def _predict(obj, value):
    return obj.predict(value)

res_ids = [_predict.remote(obj, value) for obj, value in zip(obj_ids, range(len(obj_ids)))]

ray.get(res_ids)

ray.shutdown()
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