title: Jupyter Notebook - Kernel dies during training - tensorflow-gpu 2.0, Python 3.6.8
body: <p>Since I am kind of new in this field I tried following the official tutorial from tensorflow for predicting time series. <a href="https://www.tensorflow.org/tutorials/structured_data/time_series" rel="nofollow noreferrer">https://www.tensorflow.org/tutorials/structured_data/time_series</a></p>
  
  <p>Following problem occurs:
  -When training a multivariate model, after 2 or 3 epochs the kernel dies and restarts.</p>
  
  <p>However this doesn't happen with a simpler univariate model, which has only one LSTM layer (not really sure if this makes a difference).</p>
  
  <p>Second however, this problem just happened today. Yesterday the training of the multivariate model was possible and error-free.</p>
  
  <p>As can be seen in the tutorial in the link below the model looks like this:</p>
  
  <pre><code>multi_step_model = tf.keras.models.Sequential()
  multi_step_model.add(tf.keras.layers.LSTM(32,return_sequences=True,input_shape=x_train_multi.shape[-2:]))
  
  multi_step_model.add(tf.keras.layers.LSTM(16, activation='relu'))
  
  multi_step_model.add(tf.keras.layers.Dense(72))
  
  multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae')
  </code></pre>
  
  <p>And the kernel dies after executing the following cell (usually after 2 or 3 epochs).</p>
  
  <pre><code>multi_step_history = multi_step_model.fit(train_data_multi, epochs=10,
                                            steps_per_epoch=300,
                                            validation_data=val_data_multi,
                                            validation_steps=50)
  </code></pre>
  
  <p>I have uninstalled and reinstalled tf, restarted my laptop, but nothing seems to work.</p>
  
  <p>Any ideas?</p>
  
  <p>OS: Windows 10
  Surface Book 1</p>