AI/ML

GPU in Tensorflow 2

If the error is Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR, chances are GPU is not configured properly

If the error is could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED, chances are GPU running out of memory

Set Memory Growth

This code will set set_memory_growth to true.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

Limit GPU Memory

This code will limit the1st GPU’s memory usage up to 3072 MB. The index of gpus and memory_limit can be changed as per requirement.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        tf.config.experimental.set_virtual_device_configuration(
            gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=3072)])
    except RuntimeError as e:
        print(e)

References

The official documentation can be found at:

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