title: UnknownError: Failed to get convolution algorithm when running LSTM using Tensorflow-gpu
body: <p>I'm trying to run a RNN using tensorflow-gpu since with CPU it took me around 13 minutes to treat 1 epoch (8000 pictues in train/ 2000 in test). However, I tried several times the installation of tensorflow-gpu on my Windows 10 but could not manage to run it successfully. I have the following configuration:</p>
  
  <p>GPU 1: NVIDIA GeForce GTX 1650
  GPU 0: Intel(R) UHG Graphics 630
  CPU: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz</p>
  
  <p>I have the following installed in my virtualenv Tensorflow in pip: <a href="https://i.stack.imgur.com/l23v8.jpg" rel="nofollow noreferrer">pip packages</a></p>
  
  <p>I followed the following tutorials:</p>
  
  <ul>
  <li><a href="https://www.tensorflow.org/install/gpu" rel="nofollow noreferrer">https://www.tensorflow.org/install/gpu</a></li>
  <li><a href="https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781" rel="nofollow noreferrer">https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781</a></li>
  <li><a href="https://www.pugetsystems.com/labs/hpc/The-Best-Way-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-1187/" rel="nofollow noreferrer">https://www.pugetsystems.com/labs/hpc/The-Best-Way-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-1187/</a></li>
  </ul>
  
  <p>Also since this is a popular issue, i tried different solutions suggested in other topics like:</p>
  
  <ul>
  <li>compatibility between tensorflow-gpu / CUDA / Cudnn (i tried with different combinations CUDA 9.0/ ..)</li>
  <li>I downgraded my tensoflow-gpu to 1.8.0 but did not worked</li>
  </ul>
  
  <p>Currently, i have tensorflow-gpu 2.00, CUDA 10.0, Cudnn 7.4.1.5, this seems compatible as what is defined (<a href="https://www.tensorflow.org/install/source_windows" rel="nofollow noreferrer">https://www.tensorflow.org/install/source_windows</a>)
  Visual studio is installed and the latest drivers fir my Intel Card.</p>
  
  <p>But i also found that my GeForce GTX 1650 was not mentioned in the NVIDIA gpu enabled for CUDA (<a href="https://developer.nvidia.com/cuda-gpus" rel="nofollow noreferrer">https://developer.nvidia.com/cuda-gpus</a>) don't know whether this could be the issue?</p>
  
  <p>The errormessage i get is: UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]</p>
  
  <pre><code>from tensorflow.keras import backend
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Conv2D
  from tensorflow.keras.layers import MaxPooling2D
  from tensorflow.keras.layers import Flatten
  from tensorflow.keras.layers import Dense
  
  classifier = Sequential()
  
  classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
  
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  classifier.add(Flatten())
  
  classifier.add(Dense(units = 128, activation = 'relu'))
  classifier.add(Dense(units = 1, activation = 'sigmoid'))
  
  classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  
  train_datagen = ImageDataGenerator(rescale = 1./255,
                                     shear_range = 0.2,
                                     zoom_range = 0.2,
                                     horizontal_flip = True)
  
  test_datagen = ImageDataGenerator(rescale = 1./255)
  
  training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                   target_size = (64, 64),
                                                   batch_size = 32,
                                                   class_mode = 'binary')
  
  test_set = test_datagen.flow_from_directory('dataset/test_set',
                                              target_size = (64, 64),
                                              batch_size = 32,
                                              class_mode = 'binary')
  
  classifier.fit_generator(training_set,
                           steps_per_epoch = 8000,
                           epochs = 25,
                           validation_data = test_set,
                           validation_steps = 2000)
  </code></pre>