title: InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN'
body: <p>I followed the steps provided here : <a href="https://www.tensorflow.org/install/gpu" rel="noreferrer">https://www.tensorflow.org/install/gpu</a>
  to install tensorflow that works with my gpu, and use the cuDNN SDK
  but for some reasons I still can not use it, and am still getting this error:</p>
  
  <pre><code>tensorflow.python.framework.errors_impl.InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN' used by node sequential/cu_dnnlstm/CudnnRNN (defined at cudrnn.py:27) with these attrs: [input_mode="linear_input", T=DT_FLOAT, direction="unidirectional", rnn_mode="lstm", seed2=0, is_training=true, seed=0, dropout=0]
  Registered devices: [CPU, XLA_CPU, XLA_GPU]
  Registered kernels:
    device='GPU'; T in [DT_DOUBLE]
    device='GPU'; T in [DT_FLOAT]
    device='GPU'; T in [DT_HALF]
  </code></pre>
  
  <p>even though the GPU is registered but tensorflow claims that they can not find an OpKernel.</p>
  
  <p>I am using:</p>
  
  <p>tensoflow-gpu 1.14.0</p>
  
  <p>Cuda 10.0</p>
  
  <p>Ubuntu 18.04</p>
  
  <p>P.S: I can run other code using tensorflow and by checking nvidia-smi I can see that the GPUs are getting used actually.</p>
  
  <p>can anyone please help me knowing why I am getting this problem ?</p>
  
  <p>this is the code I am trying to run:</p>
  
  <pre><code>model = tf.keras.Sequential([
      tf.keras.layers.CuDNNLSTM(
          3,
          return_sequences=False),
      tf.keras.layers.Dense(1)
  ])
  
  model.compile(
      optimizer=tf.keras.optimizers.Adam(0.1, decay=0.003), 
      loss=tf.keras.losses.mse)
  
  model.fit(train_x, train_y, epochs=100, validation_split=0, shuffle=False)
  </code></pre>