persist elephas model

.gitignore

@@ -68,4 +68,5 @@ examples/*.csv
 venv/
 
 train.csv
-test.csv
+test.csv
+test.h5

elephas/spark_model.py

@@ -2,7 +2,10 @@
 from __future__ import print_function
 
 import pyspark
+import h5py
+import json
 from keras.optimizers import serialize as serialize_optimizer
+from keras.models import load_model
 
 from .utils import lp_to_simple_rdd
 from .utils import model_to_dict
@@ -77,12 +80,25 @@ def get_train_config(epochs, batch_size, verbose, validation_split):
                 'validation_split': validation_split}
 
     def get_config(self):
-        return {'model': self.master_network.get_config(),
-                'optimizer': self.optimizer.get_config(),
+        return {'parameter_server_mode': self.parameter_server_mode,
+                'elephas_optimizer': self.optimizer.get_config(),
                 'mode': self.mode,
                 'frequency': self.frequency,
                 'num_workers': self.num_workers}
 
+    def save(self, file_name):
+        model = self.master_network
+        model.save(file_name)
+        f = h5py.File(file_name, mode='a')
+
+        f.attrs['distributed_config'] = json.dumps({
+            'class_name': self.__class__.__name__,
+            'config': self.get_config()
+        }).encode('utf8')
+
+        f.flush()
+        f.close()
+
     @property
     def master_network(self):
         return self._master_network
@@ -168,6 +184,19 @@ def _fit(self, rdd, epochs, batch_size, verbose, validation_split):
             self.stop_server()
 
 
+def load_spark_model(file_name):
+    model = load_model("test.h5")
+    f = h5py.File(file_name, mode='r')
+
+    elephas_conf = json.loads(f.attrs.get('distributed_config'))
+    class_name = elephas_conf.get('class_name')
+    config = elephas_conf.get('config')
+    if class_name == "SparkModel":
+        return SparkModel(model=model, **config)
+    elif class_name == "SparkMLlibModel":
+        return SparkMLlibModel(model=model, **config)
+
+
 class SparkMLlibModel(SparkModel):
 
     def __init__(self, model, mode='asynchronous', frequency='epoch', parameter_server_mode='http',

tests/test_mllib_model.py

@@ -4,7 +4,7 @@
 from keras.optimizers import RMSprop
 from keras.utils import np_utils
 
-from elephas.spark_model import SparkMLlibModel
+from elephas.spark_model import SparkMLlibModel, load_spark_model
 from elephas.utils.rdd_utils import to_labeled_point
 
 import pytest
@@ -46,6 +46,12 @@
 model.compile(rms, 'categorical_crossentropy', ['acc'])
 
 
+def test_serialization():
+    spark_model = SparkMLlibModel(model, frequency='epoch', mode='synchronous', num_workers=2)
+    spark_model.save("test.h5")
+    recov = load_spark_model("test.h5")
+
+
 def test_mllib_model(spark_context):
     # Build RDD from numpy features and labels
     lp_rdd = to_labeled_point(spark_context, x_train, y_train, categorical=True)

tests/test_spark_model.py

@@ -5,12 +5,10 @@
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers.core import Dense, Dropout, Activation
-from keras.optimizers import SGD
 from keras.utils import np_utils
 
-from elephas.spark_model import SparkModel
+from elephas.spark_model import SparkModel, load_spark_model
 from elephas.utils.rdd_utils import to_simple_rdd
-from elephas import optimizers as elephas_optimizers
 
 
 # Define basic parameters
@@ -51,6 +49,12 @@
 model.compile(optimizer="sgd", loss="categorical_crossentropy", metrics=["acc"])
 
 
+def test_serialization():
+    spark_model = SparkModel(model, frequency='epoch', mode='synchronous', num_workers=2)
+    spark_model.save("test.h5")
+    recov = load_spark_model("test.h5")
+
+
 def test_spark_model_end_to_end(spark_context):
     rdd = to_simple_rdd(spark_context, x_train, y_train)