No kernel identified while running tensorflow with GPU
@vaibhavAIJKV GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin.
Please refer to this [doc](https://www.tensorflow.org/install/source_windows) for further information.
Thank you! > @vaibhavAIJKV GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin. Please refer to this [doc](https://www.tensorflow.org/install/source_windows) for further information. Thank you!

Hi
As mentioned I am using TensorFlow-DirectML-Plugin, but let me go through the document to see if installing other dependencies resolve this issue @vaibhavAIJKV Sure, kindly let us know?
Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

Windows 10

### Mobile device

_No response_

### Python version

3.8.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda_12.4.r12.4/compiler.34097967_0

### GPU model and memory

Nvidia Geforce MX450

### Current behavior?

I am using windows 10 and using tensorflow with GPU using tensorflow-directml-plugin API. While running the code I am getting the following error:

> InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNNV2' used by {{node sequential/bidirectional/forward_cu_dnnlstm/CudnnRNNV2}} with these attrs: [seed=0, dropout=0, T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="lstm", seed2=0, is_training=true]
> Registered devices: [CPU, GPU]
> Registered kernels:
>   <no registered kernels>
> 
> 	 [[sequential/bidirectional/forward_cu_dnnlstm/CudnnRNNV2]] [Op:__inference_train_function_7434]

It's a simple model with configuration something like:
   
    regressor2.add(Bidirectional(LSTM(units = 128,  return_sequences = True)))
    regressor2.add(Flatten())    
    regressor2.add(Dense(units = 64,activation='relu'))
    regressor2.add(Dense(units = 1,activation='linear'))
    opt = keras.optimizers.Adam(learning_rate=0.001)
    regressor2.compile(optimizer = opt,  loss='mean_squared_error',metrics=["MeanSquaredError"])

### Standalone code to reproduce the issue

```shell
regressor2.add(Bidirectional(LSTM(units = 128,  return_sequences = True)))
    regressor2.add(Flatten())    
    regressor2.add(Dense(units = 64,activation='relu'))
    regressor2.add(Dense(units = 1,activation='linear'))
    opt = keras.optimizers.Adam(learning_rate=0.001)
    regressor2.compile(optimizer = opt,  loss='mean_squared_error',metrics=["MeanSquaredError"])
```


### Relevant log output

_No response_