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[Python Debug]Kernel Crash While Running Neural Network with Keras|Jupyter Notebook运行Keras服务器宕机原因...

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发布于 2019/03/30 12:31
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最近做Machine Learning作业,要在Jupyter Notebook上用Keras搭建Neural Network。结果连最简单的一层神经网络都运行不了,更奇怪的是我先用iris数据集跑了一遍并没有任何问题,但是用老师给的fashion mnist一运行服务器就提示挂掉重启。更更奇怪的是同样的code在同学的电脑上跑也是一点问题都没有,让我一度以为是我的macbook年代久远配置太低什么的,差点要买新电脑了>_<

今天上课经ML老师几番调试,竟然完美解决了,不愧是CMU大神!(这里给Prof强烈打call,虽然他看不懂中文><)因为刚学python没多久,还很不熟悉,经过这次又学会好多新技能✌️

出问题的完整code如下,就是用Keras实现logistic regression,是一个简单的一层网络,但是每次运行到最后一行server就挂掉,然后重启kernel。

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA, FastICA
from sklearn.linear_model import LogisticRegression
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D
from keras.utils import to_categorical
from keras.datasets import fashion_mnist

(x3_train, y_train), (x3_test, y_test) = fashion_mnist.load_data()
n_classes = np.max(y_train) + 1

# Vectorize image arrays, since most methods expect this format
x_train = x3_train.reshape(x3_train.shape[0], np.prod(x3_train.shape[1:]))
x_test = x3_test.reshape(x3_test.shape[0], np.prod(x3_test.shape[1:]))

# Binary vector representation of targets (for one-hot or multinomial output networks)
y3_train = to_categorical(y_train)
y3_test = to_categorical(y_test)

from sklearn import preprocessing
scaler = preprocessing.StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)     
x_test_scaled = scaler.fit_transform(x_test) 

n_output = y3_train.shape[1]
n_input = x_train_scaled.shape[1]

nn_lr = Sequential() 
nn_lr.add(Dense(units=n_output, input_dim= n_input, activation = 'softmax'))
nn_lr.compile(optimizer = 'sgd', loss = 'categorical_crossentropy', metrics = ['accuracy'])

由于Jupyter Notebook只是一直重启kernel,并没有任何错误提示,所以让人无从下手。但是经老师提示原来启动Jupyter Notebook时自动打开的terminal上会记录运行的信息(小白第一次发现。。),包括了kerter中止及重启的详细过程及原因:

[I 22:11:54.603 NotebookApp] Kernel interrupted: 7e7f6646-97b0-4ec7-951c-1dce783f60c4

[I 22:13:49.160 NotebookApp] Saving file at /Documents/[Rutgers]Study/2019Spring/MACHINE LEARNING W APPLCTN LARGE DATASET/hw/Untitled1.ipynb

2019-03-28 22:13:49.829246: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

2019-03-28 22:13:49.829534: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.

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/.

[I 22:13:51.049 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports

kernel c1114f5a-3829-432f-a26a-c2db6c330352 restarted

还有另外一个方法,把代码copy到ipython中,也可以得到类似的信息,所以最后定位的错误是:

OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized.

谷歌了一下,github上有一个很详细的讨论帖,但是楼主是运行XGBoost时遇到了这个问题,让我联想到寒假安装XGBoost确实经过了很曲折的过程,可能不小心把某个文件重复下载到了不同路径,于是程序加载package时出现了冲突。帖子里提供了几种可能的原因及解决方法:

1. 卸载clang-omp

brew uninstall libiomp clang-omp

as long as u got gcc v5 from brew it come with openmp

follow steps in:
https://github.com/dmlc/xgboost/tree/master/python-package

尝试了卸载xgboost再安装,然后卸载clang-omp,得到错误提示

No such keg: /usr/local/Cellar/libiomp

pip uninstall xbgoost
pip install xgboost
brew uninstall libiomp clang-omp

 

2. 直接在jupyter notebook里运行:

# DANGER! DANGER!
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

老师说这行命令可以让系统忽略package冲突的问题,自行选择一个package使用。试了一下这个方法确实有效,但这是非常危险的做法,极度不推荐

 

3. 找到重复的libiomp5.dylib文件,删除其中一个

在Finder中确实找到了两个文件,分别在~/⁨anaconda3⁩/lib⁩和~/anaconda3⁩/⁨lib⁩/⁨python3.6⁩/⁨site-packages⁩/⁨_solib_darwin⁩/⁨_U@mkl_Udarwin_S_S_Cmkl_Ulibs_Udarwin___Uexternal_Smkl_Udarwin_Slib⁩ (????)可是不太确定应该删除哪一个,感觉这种做法也蛮危险的,删错了整个跑不起来了。

 

4. OpenMP冲突

Hint: This means that multiple copies of the OpenMP runtime have been linked into the program 

根据提示信息里的Hint,搜了下TensorFlow OpenMP。OpenMP是一个多线程并行编程的平台,TensorFlow似乎有自己的并行计算架构,并用不上OpenMP(see https://github.com/tensorflow/tensorflow/issues/12434

 

5. 卸载nomkl

I had the same error on my Mac with a python program using numpy, keras, and matplotlib. I solved it with 'conda install nomkl'.

这是最后有效的做法!nomkl全称是Math Kernel Library (MKL) Optimization,是Interl开发的用来加速数学运算的模块,通过conda安装package可以自动使用mkl,更详细的信息可以看这个Anaconda的官方文档

To opt out, run conda install nomkl and then use conda install to install packages that would normally include MKL or depend on packages that include MKL, such as scipynumpy, and pandas.

可能是numpy之类的package更新时出现了一些冲突,安装nomkl之后竟然神奇地解决了,后来又尝试把MKL卸载了,程序依然正常运行。。卸载命令如下:

conda remove mkl mkl-service

 

总结:

1. 老师好厉害呀,三下五除二就把问题解决了><

2. 经大神提醒,运行python之前创建一个虚拟环境可以很好避免package冲突之类的问题,具体方法:https://www.jianshu.com/p/d8e7135dca40

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