How to Save/Load Optimized GPy Regression Model

I'm trying to save my optimized Gaussian process model for use in a different script. My current line of thinking is to store the model information in a json file, utilizing GPy's built-in to_dict and from_dict functions. Something along the lines of:

import GPy
import numpy as np
import json

X = np.random.uniform(-3.,3.,(20,1))
Y = np.sin(X) + np.random.randn(20,1)*0.05
kernel = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.)

m = GPy.models.GPRegression(X, Y, kernel)

m.optimize(messages=True)
m.optimize_restarts(num_restarts = 10)

jt = json.dumps(m.to_dict(save_data=False), indent=4)
with open("j-test.json", 'w') as file:
    file.write(jt)

This step works with no issues, but I run into problems when I try to load the model information using :

with open("j-test.json", 'r') as file:
    d = json.load(file)  # d is a dictionary

m2 = GPy.models.GPClassification.from_dict(d, data=None)

which gives me an assertion error because "data is not None", which it is -- or at least I think so.

I'm really new to GPy and using jsons, so I'm really not sure where I've gone astray. I tried looking into the documentation, but the documentation is a bit vague and I couldn't find an example of its use. Is there a step/concept that I missed? Also, is this the best way to store and reload my model? Any help with this would be greatly appreciated! Thanks!

The module pickle is your friend here!

import pickle
with open('save.pkl', 'wb') as file:
    pickle.dump(m, file)

you can call it back in a future script with:

with open('save.pkl', 'rb') as file:
    loaded_model = pickle.load(file)