Dealing with Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR in Tensorflow v2.x

environment

version

• Tensorflow: 2.1.0
• Ubuntu: 18.04
• python: 3.6.8
• CUDA: 10.0.130
• cuDNN: 7.6.5

Cause

GPU memory allocation issue in Tensorflow. I think that

Coping

If you are limiting memory allocation with the following code

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  # Restrict TensorFlow to only use the first GPU
  try:
    tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
  except RuntimeError as e:
    # Visible devices must be set before GPUs have been initialized
    print(e)

It was cured by changing to ↓ below.

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)

Details (Tensorflow Official Doc)

other

--Command execution: Error handling [Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR] --TF1 series: What to do if you get an error like Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR in TensorFlow