title: Could not load library cudnn_cnn_infer64_8.dll. Error code 126
body: <pre><code>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!
  </code></pre>
  <p>I keep getting this error when I try to use TensorFlow with GPU, I've installed CUDA, cuDNN, and all the drivers multiple times according to the instructions. But nothing seems to work.
  If I use notebook then TensorFlow uses the CPU, with VS code notebook extension i can use the gpu but it stops the session at 1st epoch, when I tried to run it as a normal python file. the above error occurred.</p>
  <p>Complete terminal output:</p>
  <pre><code>Found 14630 validated image filenames belonging to 3 classes.
  Found 1500 validated image filenames belonging to 3 classes.
  2021-11-08 11:03:58.000354: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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.
  2021-11-08 11:03:58.603592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2775 MB memory:  -&gt; device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1        
  Epoch 1/10
  2021-11-08 11:04:07.306011: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8300
  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!
  E:\MyWorkSpace\animal_detect&gt;
  </code></pre>
  <p>The code snippet:</p>
  <pre><code>import tensorflow as tf 
  from tensorflow.keras.preprocessing.image import ImageDataGenerator 
  from tensorflow.keras import layers 
  from tensorflow.keras import Model 
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from tensorflow.keras.applications.vgg16 import VGG16
  import pandas as pd
  import numpy as np
  
  train_df = pd.read_csv('train.csv')
  test_df = pd.read_csv('test.csv')
  train_gen = ImageDataGenerator(rescale = 1./255.,rotation_range = 40, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)
  test_gen = ImageDataGenerator( rescale = 1.0/255. )
  train_set = train_gen.flow_from_dataframe(train_df,x_col='loc',y_col='label',batch_size=20,target_size=(224,224))
  test_set = train_gen.flow_from_dataframe(test_df,x_col='loc',y_col='label',batch_size=20,target_size=(224,224))
  base_model = VGG16(input_shape = (224, 224, 3),
  include_top = False,
  weights = 'imagenet')
  for layer in base_model.layers:
      layer.trainable = False
  x = layers.Flatten()(base_model.output)
  x = layers.Dense(512, activation='relu')(x)
  x = layers.Dropout(0.5)(x)
  x = layers.Dense(3, activation='sigmoid')(x)
  
  model = tf.keras.models.Model(base_model.input, x)
  
  model.compile(optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.0001), loss = 'categorical_crossentropy',metrics = ['acc'])
  vgghist = model.fit(train_set, validation_data = test_set, steps_per_epoch = 100, epochs = 10)
  </code></pre>
  <p>the same code has been used for Jupyter-notebook, VS code notebook extension and as a normal python file</p>
  <p>Device specifications:</p>
  <p>processor: Intel i5
  gpu: Nvidia Geforce 1050ti</p>
  <p>Cuda version: 11.5
  cuDNN version: 8.3</p>