title: How to install Tensorflow 2.0 install with Conda?
body: <p>I get this error when I try to run a test example.</p>
  
  <blockquote>
    <p>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.</p>
  </blockquote>
  
  <p>I have tried a recommended process for conda found <a href="https://www.pugetsystems.com/labs/hpc/How-to-Install-TensorFlow-with-GPU-Support-on-Windows-10-Without-Installing-CUDA-UPDATED-1419/" rel="nofollow noreferrer">here</a>. Create new environment install Tensorflow-GPU. Install Jupyter Notebook and test some code. I have tried changing versions of cudatoolkit and cudnn but I can't seem to figure out how to do this. The install Tensorflow-GPU puts Cudatoolkit 10.0.130 and cudnn 7.6.</p>
  
  <pre><code>import tensorflow as tf
  
  mnist = tf.keras.datasets.mnist
  
  (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
  
  train_images = train_images.reshape(60000, 28, 28, 1)
  
  test_images = test_images.reshape(10000, 28, 28, 1)
  
  train_images, test_images = train_images/255, test_images/255
  
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape (28,28,1)),
      tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
      tf.keras.layers.MaxPooling2D(2,2),
      tf.keras.layers.Dropout(0.25),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(128, activation='relu'),
      tf.keras.layers.Dropout(0.5),
      tf.keras.layers.Dense(10, activation='softmax')
  ])
  
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
  import time
  
  start_time=time.time()
  
  model.fit(train_images, train_labels, batch_size=128, epochs=15, verbose=1,
       validation_data=(test_images, test_labels))
  
  print('Training took {} seconds'.format(time.time()-start_time))
  </code></pre>