Python内核在带有tensorflow 2的Jupyter笔记本上死亡

Question

我根据these的说明，使用conda在mac上安装了tensorflow 2：

conda create -n tf2 tensorflow

然后我安装了ipykernel，将这个新环境添加到我的jupyter笔记本内核中，如下所示：

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看起来效果不错，我可以在我的jupyter笔记本内核上看到我的tf环境。在

然后我试着运行简单的MNIST example来检查是否一切都正常，当我执行这行代码时：

model.fit(x_train, y_train, epochs=5)

我的jupyter笔记本的内核在没有更多信息的情况下死掉了。在

我通过python mnist在我的终端上执行了相同的代码_测试.py而且还通过ipython（一个命令接一个命令），我没有任何问题，这让我假设我的tensorflow 2正确安装在我的conda环境中。在

你知道安装过程中出了什么问题吗？在

版本：

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

这里我把完整的脚本和执行的标准输出放在这里：

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 1

在尝试了不同的操作之后，我使用以下命令在调试模式下运行jupyter notebook：

jupyter notebook  debug

然后，在我的笔记本上执行命令后，我收到错误消息：

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/.

在this讨论之后，在虚拟环境中安装nomkl对我很有用。在

^{pr2}$

Answer 2

尝试conda install nomkl。即使你面对这个问题，检查你的anaconda/lib folder，运行ll lib*omp*，你看到一些旧的libiomp5.dylib文件吗？把它取下来。在

Answer 3

我不能确切地猜出你的问题，但看起来它与一些版本冲突有关。执行以下操作（这是我所做的，对我有效）：

1. conda create -n tf2 python=3.7 ipython ipykernel
2. conda activate tf2
3. conda install -c anaconda tensorflow
4. python -m ipykernel install user name=tf2
5. 再次运行模型，看看它是否有效。在