I am using keras 2.0.8 with tensorflow 1.3.0 backend.
I am loading a model in the class init and then use it to predict multithreaded.
import tensorflow as tf from keras import backend as K from keras.models import load_model class CNN: def __init__(self, model_path): self.cnn_model = load_model(model_path) self.session = K.get_session() self.graph = tf.get_default_graph() def query_cnn(self, data): X = self.preproccesing(data) with self.session.as_default(): with self.graph.as_default(): return self.cnn_model.predict(X) I initialize the CNN once and the query_cnn method happens from multiple threads.
The exception i get in my log is:
File "/home/*/Similarity/CNN.py", line 43, in query_cnn return self.cnn_model.predict(X) File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 913, in predict return self.model.predict(x, batch_size=batch_size, verbose=verbose) File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1713, in predict verbose=verbose, steps=steps) File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1269, in _predict_loop batch_outs = f(ins_batch) File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2273, in __call__ **self.session_kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 895, in run run_metadata_ptr) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1124, in _run feed_dict_tensor, options, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1321, in _do_run options, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1340, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.NotFoundError: PruneForTargets: Some target nodes not found: group_deps The code works fine most of the times, its probably some problem with the multithreading.
How can i fix it?
1 Answers
Answers 1
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
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