Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Hi **@Tanishq-JM** ,
Sorry for the dealy, Confirm that TensorFlow-GPU is installed correctly by running a simple GPU-enabled TensorFlow script. You can use the following code snippet to check if TensorFlow can detect and use your GPU:
```
import tensorflow as tf
print("Num GPUs Available:", len(tf.config.experimental.list_physical_devices('GPU')))
```
If this script doesn't detect your GPU, there may be underlying issues with your TensorFlow installation or GPU setup.

Here i am providing the [documentation](https://www.tensorflow.org/install/source_windows) how to install. Please go through it once.

Here i am providing updated [gist](https://colab.sandbox.google.com/gist/Venkat6871/5a7a97e09655679f39c3565ec176fd4a/63702_2-16-1-nightly.ipynb) with some corrections for your reference. Please check this too.

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. Hi @Venkat6871 
Thank you for your time , i am still facing same issue and now i think i would go for the google colab instead it would me more easy for me to maintain and run it .
Thank you for your time and kind support 😃.  Hi **@Tanishq-JM** ,
- Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved.

Thank you! Hello I am still facing this issue 
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.10.0

### Custom code

No

### OS platform and distribution

windows 10

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.2 / 11.2(8.1 I think)

### GPU model and memory

Nvidia T2000 4GB

### Current behavior?

# Problem : 

I expect this problem to be resolved soon. I am unable to use my GPU for training, as it remains at 0% usage. 
When I try another version, it only uses my CPU, which takes days and causes my computer (laptop) to overheat.

### Error

I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Please make sure cudnn_cnn_infer64_8.dll is in your library path!

![image](https://github.com/tensorflow/tensorflow/assets/138287162/c607e26d-0196-42d2-ac60-181873a76608)

# Output 

![image](https://github.com/tensorflow/tensorflow/assets/138287162/fdbe8ac9-43f2-4b38-bc7b-8e3f4c051cbc)


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import subprocess
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

def get_gpu_info():
try:
result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)
return result.stdout.decode('utf-8')
except FileNotFoundError:
return "nvidia-smi not found. Make sure NVIDIA drivers are installed."

from keras import layers, models, datasets
import time

(train_images, train_labels), (_, _) = datasets.mnist.load_data()
train_images = train_images.reshape(-1, 28, 28, 1).astype('float32') / 255.0
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

start_time = time.time()
end_time = start_time + 30

while time.time() < end_time:
gpu_info = get_gpu_info()
print(gpu_info)

model.fit(train_images, train_labels, epochs=1, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(train_images, train_labels, verbose=0)
print(f"Loss: {loss}, Accuracy: {accuracy}")
```


### Relevant log output

```shell
(ML) PS D:\python> & d:/python/ML/Scripts/python.exe d:/python/test.py
2024-03-14 11:50:53.845372: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-14 11:50:55.378833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 12036372220610172862
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 2232156160
locality {
bus_id: 1
links {
}
}
incarnation: 1063671899388078998
physical_device_desc: "device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5"
xla_global_id: 416903419
]
2024-03-14 11:50:55.686954: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000,
pci bus id: 0000:01:00.0, compute capability: 7.5
Thu Mar 14 11:50:55 2024

2024-03-14 11:50:57.159136: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Please make sure cudnn_cnn_infer64_8.dll is in your library path!
(ML) PS D:\python>
```
