Useful or not, from you.
tensorflow tensorflow:Layer will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria (using with GRU layer and dropout)

When trying to follow along F. Chollet's "Deep Learning with Python" listing 6.40 I encounter this warning: WARNING:tensorflow:Layer gru_4 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU after executing the following code:

input_tensor = layers.Input((None, float_data.shape[-1]))
kmodel = layers.GRU(32, dropout=0.2, recurrent_dropout=0.2)(input_tensor)
output_tensor = layers.Dense(1)(kmodel)
model = models.Model(input_tensor, output_tensor)

My imports are:

import os

import numpy as np
import matplotlib.pyplot as plt

from typing import Tuple

from tensorflow.keras import models, layers
from tensorflow.keras.optimizers import RMSprop

Note that if I don't use dropout and recurrent_dropout in the GRU layer everything works fine and fast. In the case I do use dropout like in the code above, it still works but with very slow performance.

System information: Python 3.7.7 tensorflow-gpu 2.2.0 GPU: Cuda compilation tools, release 10.1, V10.1.243 on GeForce RTX 2080 Ti 11016MiB OS: Ubuntu 18.04.4 LTS

That's a useful answer
Without any help

@amahendrakar, The link you have provided just reconfirms that the problem is with the recurrent_dropout argument. The requirement is to set it to 0, i.e., not using it. I think this should be implemented in the TF backend since it is an important option that highly affects performance (training time).