How to run multiple tensorflow codes in one gpu?

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by larissa , in category: Third Party Scripts , 15 days ago

How to run multiple tensorflow codes in one gpu?

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1 answer

Member

by jasen , 14 days ago

@larissa 

To run multiple TensorFlow codes in one GPU, you can use the tf.device() context manager in TensorFlow to allocate specific operations to a specific GPU. Here is a simple example to demonstrate how to run two TensorFlow codes on the same GPU:

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import tensorflow as tf

# Create a TensorFlow session
with tf.device('/gpu:0'):
    # Define and run the first TensorFlow code
    a = tf.constant([1.0, 2.0, 3.0])
    b = tf.constant([4.0, 5.0, 6.0])
    c = a + b

    with tf.Session() as sess:
        result1 = sess.run(c)
        print("Result 1:", result1)

# Create a TensorFlow session
with tf.device('/gpu:0'):
    # Define and run the second TensorFlow code
    x = tf.constant([7.0, 8.0, 9.0])
    y = tf.constant([10.0, 11.0, 12.0])
    z = x * y

    with tf.Session() as sess:
        result2 = sess.run(z)
        print("Result 2:", result2)


In this example, we allocate both operations to the same GPU ('/gpu:0') using the tf.device() context manager. You can additionally specify which GPU you want to use by changing the device string to '/gpu:1', etc., if you have multiple GPUs in your system. Remember that the code running on the GPU will share resources with other codes, and the performance may be impacted if the GPU is overloaded.