Question: I installed tensorflow 2 on my mac using conda according these instructions: conda create -n tf2 tensorflow  Then I installed ipykernel to add this new environment to my jupyter notebook kernels as follows: conda activate tf2 conda install ipykernel python -m ipykernel install --user --name=tf2  That seemed to work well, I am able to see my tf2 environment on my jupyter notebook kernels. Then I tried to run the simple MNIST example to check if all was working properly and I when I execute this line of code: model.fit(x_train, y_train, epochs=5)  The kernel of my jupyter notebook dies without more information.  I executed the same code on my terminal via python mnist_test.py and also via ipython (command by command) and I don't have any issues, which let's me assume that my tensorflow 2 is correctly installed on my conda environment. Any ideas on what went wrong during the install? Versions: python==3.7.5 tensorboard==2.0.0 tensorflow==2.0.0 tensorflow-estimator==2.0.0 ipykernel==5.1.3 ipython==7.10.2 jupyter==1.0.0 jupyter-client==5.3.4 jupyter-console==5.2.0 jupyter-core==4.6.1  Here I put the complete script as well as the STDOUT of the execution: import tensorflow as tf import matplotlib.pyplot as plt import seaborn as sns mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 nn_model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) nn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) nn_model.fit(x_train, y_train, epochs=5) nn_model.evaluate(x_test, y_test, verbose=2)   (tf2) ➜ tensorflow2 python mnist_test.py 2020-01-03 10:46:10.854619: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. 2020-01-03 10:46:10.854860: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance. Train on 60000 samples Epoch 1/5 60000/60000 [==============================] - 6s 102us/sample - loss: 0.3018 - accuracy: 0.9140 Epoch 2/5 60000/60000 [==============================] - 6s 103us/sample - loss: 0.1437 - accuracy: 0.9571 Epoch 3/5 60000/60000 [==============================] - 6s 103us/sample - loss: 0.1054 - accuracy: 0.9679 Epoch 4/5 60000/60000 [==============================] - 6s 103us/sample - loss: 0.0868 - accuracy: 0.9729 Epoch 5/5 60000/60000 [==============================] - 6s 103us/sample - loss: 0.0739 - accuracy: 0.9772 10000/1 - 1s - loss: 0.0359 - accuracy: 0.9782 (tf2) ➜ tensorflow2 

Answer: After trying different things I run jupyter notebook on debug mode by using the command: jupyter notebook --debug  Then after executing the commands on my notebook I got the error message:  OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized. OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.   And following this discussion, installing nomkl on the virtual environment worked for me. conda install nomkl