Tensorflow seems to be using system memory not GPU, and the Program stops after global_variable_inititializer()

Question

I just got a new GTX 1070 Founders Addition for my desktop, and I am trying to run tensorflow on this new GPU. I am using tensorflow.device() to run tensorflow on my GPU, bu

Answer 1

Try compare time (GPU vs CPU) with this simple example:

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
    model = tf.keras.models.Sequential([
      tf.keras.layers.Flatten(input_shape=(28, 28)),
      tf.keras.layers.Dense(512, activation=tf.nn.relu),
      tf.keras.layers.Dropout(0.2),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model    

epoch = 3

print('GPU:')
with tf.device('/gpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)

print('\nCPU:')
with tf.device('/cpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)