RPC communication python calls using Zerorpc (MNIST sklearn example)

client.py

import matplotlib.pyplot as plt
from execution import Execution

e = Execution()

#digits = datasets.load_digits()
digits = e.execute('datasets.load_digits')

n_samples = len(digits.images)

data = digits.images.reshape((n_samples, -1))

#classifier = svm.SVC(gamma=0.001)
classifier = e.execute('svm.SVC', args={"gamma":0.001})

#classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])
classifier = e.execute('fit', args=[data[:n_samples // 2], digits.target[:n_samples // 2]], var=classifier)

expected = digits.target[n_samples // 2:]
#predicted = classifier.predict(data[n_samples // 2:])
predicted = e.execute('predict', args=[data[n_samples // 2:]], var=classifier)

images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
for index, (image, prediction) in enumerate(images_and_predictions[:4]):
    plt.subplot(2, 4, index + 5)
    plt.axis('off')
    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    plt.title('Prediction: %i' % prediction)

plt.show()

execution.py

import pickle
import zerorpc

client = zerorpc.Client()
client.connect("tcp://127.0.0.1:4242")


class Execution():

    def execute(self, function_call, args=None, var=None):
        return pickle.loads(client.execute(function_call, pickle.dumps(args), pickle.dumps(var)))

server.py

import zerorpc
import pickle

#sklearn has to be imported
from sklearn import datasets, svm, metrics


def dec_func(function_name, args, var):
    if var:
        function_call = getattr(var, function_name)
        return pickle.dumps(function_call(*args))
    else:
        function_call = eval(function_name)
        if args:
            return pickle.dumps(function_call(**args))
        else:
            return pickle.dumps(function_call())

class Server():
    def execute(self, f_name, args=None, var=None):
        args = pickle.loads(args)
        var = pickle.loads(var)
        return dec_func(f_name, args, var)

s = zerorpc.Server(Server())
s.bind('tcp://127.0.0.1:4242')
s.run()

Description:

I used Zerorpc as RPC communication library for client to call functions which is installed on different node (server).
If you noticed sklearn library has not been installed on client machine while is being called. The calls on client being sent to server using execution API (execution.py) and data being sent back to client using RPC too.