python package : https://github.com/mwburke/xgboost-python-deploy

import xgboost as xgb
import numpy as np
import pandas as pd
from xgb_deploy.fmap import generate_fmap_from_pandas
from xgb_deploy.model import ProdEstimator
from sklearn.model_selection import train_test_split
import json
import random

dim_float = 80
dim_int = 20
n = 50000 

df_float = pd.DataFrame(np.random.rand(n,dim_float))
df_float.columns = ['float_%s'%i for i in range(dim_float)]
df_int = pd.DataFrame(np.random.randint(0,10,size=(n,dim_int)))
df_int.columns = ['int_%s'%i for i in range(dim_int)]
feature_cols = list(df_float.columns)+list(df_int.columns)
df_data = pd.concat([df_float,df_int],axis=1)
df_data['label'] = np.random.randint(0,2,n)
print(df_data['label'].value_counts())
print(df_data.shape)
print(df_data.head(5))

generate_fmap_from_pandas(df_data, 'demo_fmap.txt')

X_train, X_test, y_train, y_test = train_test_split(df_data[feature_cols], df_data['label'], test_size=0.33)

dtrain = xgb.DMatrix(data=X_train, label=y_train)
dtest = xgb.DMatrix(data=X_test, label=y_test)

classification_params = {
    'base_score': 0.5,  # np.mean(y_train),
    'max_depth': 3,
    'eta': 0.1,
    'objective': 'binary:logistic',
    'eval_metric': 'auc',
    'silent': 1,
    'n_jobs ':-1
}

clf = xgb.XGBClassifier(**classification_params)
clf.fit(X_train, y_train,eval_set=[(X_train, y_train), (X_test, y_test)],eval_metric='logloss',verbose=True)
X_test['pred1'] = clf.predict_proba(X_test)[:,1]
model = clf._Booster

model.dump_model(fout='demo_xgb.json', fmap='demo_fmap.txt', dump_format='json')

with open('demo_xgb.json', 'r') as f:
    model_data = json.load(f)

estimator = ProdEstimator(model_data, pred_type='classification', base_score=classification_params['base_score'])
X_test['pred2'] = estimator.predict(X_test.to_dict(orient='records'))
X_test['diff'] = X_test['pred1'] - X_test['pred2']
print(X_test[['pred1','pred2','diff']].head(30))
print(X_test['diff'].sum())



      pred1     pred2          diff

33243 0.515672 0.515672 1.635301e-08

15742 0.478694 0.478694 3.468678e-08

24815 0.596091 0.596091 -5.536898e-09

33120 0.489696 0.489696 4.128085e-08

29388 0.472804 0.472804 -6.701184e-09

33662 0.478668 0.478668 1.495377e-08

15019 0.495415 0.495415 -1.104315e-09

7787 0.555280 0.555280 -1.022957e-08

39378 0.494439 0.494439 5.891659e-08

15317 0.481563 0.481563 1.630472e-08

31946 0.533403 0.533403 -2.231835e-08

16784 0.484454 0.484454 2.196223e-08

13511 0.529494 0.529494 -2.274838e-09

11304 0.492583 0.492583 -1.724794e-09

9583 0.501279 0.501279 -1.815183e-09

31448 0.517019 0.517019 -2.593171e-08

38030 0.482880 0.482880 -1.191063e-08

49734 0.479614 0.479614 -1.770112e-08

15682 0.479675 0.479675 4.876058e-09

30756 0.539753 0.539753 9.885628e-09

4829 0.507685 0.507685 2.341456e-08

49888 0.502952 0.502952 2.951946e-08

41311 0.500395 0.500395 1.270836e-08

22434 0.486226 0.486226 1.047917e-08

45807 0.531456 0.531457 -3.217818e-08

25009 0.490071 0.490071 2.752955e-08

3419 0.516763 0.516763 -2.142890e-09

18176 0.486686 0.486686 -5.403653e-09

18296 0.490275 0.490275 -3.624349e-08

314 0.496112 0.496112 -1.507733e-08

-0.05263647978160496