Make sure you finish the graph creation before creating the other threads.
Calling finalize() on the graph may help you with that.
def __init__(self, model_path):
self.cnn_model = load_model(model_path)
self.session = K.get_session()
self.graph = tf.get_default_graph()
self.graph.finalize()
Update 1: finalize() will make your graph read-only so it can be safely used in multiple threads. As a side effect, it will help you find unintentional behavior and sometimes memory leaks as it will throw an exception when you try to modify the graph.
Imagine that you have a thread that does for instance one hot encoding of your inputs. (bad example:)
def preprocessing(self, data):
one_hot_data = tf.one_hot(data, depth=self.num_classes)
return self.session.run(one_hot_data)
If you print the amount of objects in the graph you will notice that it will increase over time
# amount of nodes in tf graph
print(len(list(tf.get_default_graph().as_graph_def().node)))
But if you define the graph first that won't be the case (slightly better code):
def preprocessing(self, data):
# run pre-created operation with self.input as placeholder
return self.session.run(self.one_hot_data, feed_dict={self.input: data})
Update 2: According to this thread you need to call model._make_predict_function() on a keras model before doing multithreading.
Keras builds the GPU function the first time you call predict(). That
way, if you never call predict, you save some time and resources.
However, the first time you call predict is slightly slower than every
other time.
The updated code:
def __init__(self, model_path):
self.cnn_model = load_model(model_path)
self.cnn_model._make_predict_function() # have to initialize before threading
self.session = K.get_session()
self.graph = tf.get_default_graph()
self.graph.finalize() # make graph read-only
Update 3: I did a proof of concept of a warming up, because _make_predict_function() doesn't seems to work as expected.
First I created a dummy model:
import tensorflow as tf
from keras.layers import *
from keras.models import *
model = Sequential()
model.add(Dense(256, input_shape=(2,)))
model.add(Dense(1, activation='softmax'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.save("dummymodel")
Then in another script I loaded that model and made it run on multiple threads
import tensorflow as tf
from keras import backend as K
from keras.models import load_model
import threading as t
import numpy as np
K.clear_session()
class CNN:
def __init__(self, model_path):
self.cnn_model = load_model(model_path)
self.cnn_model.predict(np.array([[0,0]])) # warmup
self.session = K.get_session()
self.graph = tf.get_default_graph()
self.graph.finalize() # finalize
def preproccesing(self, data):
# dummy
return data
def query_cnn(self, data):
X = self.preproccesing(data)
with self.session.as_default():
with self.graph.as_default():
prediction = self.cnn_model.predict(X)
print(prediction)
return prediction
cnn = CNN("dummymodel")
th = t.Thread(target=cnn.query_cnn, kwargs={"data": np.random.random((500, 2))})
th2 = t.Thread(target=cnn.query_cnn, kwargs={"data": np.random.random((500, 2))})
th3 = t.Thread(target=cnn.query_cnn, kwargs={"data": np.random.random((500, 2))})
th4 = t.Thread(target=cnn.query_cnn, kwargs={"data": np.random.random((500, 2))})
th5 = t.Thread(target=cnn.query_cnn, kwargs={"data": np.random.random((500, 2))})
th.start()
th2.start()
th3.start()
th4.start()
th5.start()
th2.join()
th.join()
th3.join()
th5.join()
th4.join()
Commenting the lines for the warmingup and finalize I was able to reproduce your first issue
