dockerで機械学習(35) with anaconda(35)「scikit-learn Cookbook」 2nd Edition By Trent Hauck, Julian Avila

1.すぐに利用したい方へ(as soon as)

「scikit-learn Cookbook」 2nd Edition By Trent Hauck, Julian Avila

docker

dockerを導入し、Windows, Macではdockerを起動しておいてください。
Windowsでは、BiosでIntel Virtualizationをenableにしないとdockerが起動しない場合があります。
また、セキュリティの警告などが出ることがあります。

docker run

$  docker run -it -p 8888:8888 kaizenjapan/anaconda-indra /bin/bash

以下のshell sessionでは
(base) root@f19e2f06eabb:/#は入力促進記号(comman prompt)です。実際には数字の部分が違うかもしれません。この行の#の右側を入力してください。
それ以外の行は出力です。出力にエラー、違いがあれば、コメント欄などでご連絡くださると幸いです。
それぞれの章のフォルダに移動します。

dockerの中と、dockerを起動したOSのシェルとが表示が似ている場合には、どちらで捜査しているか間違えることがあります。dockerの入力促進記号(comman prompt)に気をつけてください。

ファイル共有または複写

dockerとdockerを起動したOSでは、ファイル共有をするか、ファイル複写するかして、生成したファイルをブラウザ等表示させてください。参考文献欄にやり方のURLを記載しています。

複写の場合は、dockerを起動したOS側コマンドを実行しました。お使いのdockerの番号で置き換えてください。複写したファイルをブラウザで表示し内容確認しました。

setup.py

(base) root@a221771835f7:/scikit-learn# python setup.py
Partial import of sklearn during the build process.
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
   or: setup.py --help [cmd1 cmd2 ...]
   or: setup.py --help-commands
   or: setup.py cmd --help

error: no commands supplied

(base) root@a221771835f7:/scikit-learn# python setup.py --help
Partial import of sklearn during the build process.
Common commands: (see '--help-commands' for more)

  setup.py build      will build the package underneath 'build/'
  setup.py install    will install the package

Global options:
  --verbose (-v)      run verbosely (default)
  --quiet (-q)        run quietly (turns verbosity off)
  --dry-run (-n)      don't actually do anything
  --help (-h)         show detailed help message
  --no-user-cfg       ignore pydistutils.cfg in your home directory
  --command-packages  list of packages that provide distutils commands

Information display options (just display information, ignore any commands)
  --help-commands     list all available commands
  --name              print package name
  --version (-V)      print package version
  --fullname          print <package name>-<version>
  --author            print the author's name
  --author-email      print the author's email address
  --maintainer        print the maintainer's name
  --maintainer-email  print the maintainer's email address
  --contact           print the maintainer's name if known, else the author's
  --contact-email     print the maintainer's email address if known, else the
                      author's
  --url               print the URL for this package
  --license           print the license of the package
  --licence           alias for --license
  --description       print the package description
  --long-description  print the long package description
  --platforms         print the list of platforms
  --classifiers       print the list of classifiers
  --keywords          print the list of keywords
  --provides          print the list of packages/modules provided
  --requires          print the list of packages/modules required
  --obsoletes         print the list of packages/modules made obsolete

usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
   or: setup.py --help [cmd1 cmd2 ...]
   or: setup.py --help-commands
   or: setup.py cmd --help

sklearn/base.py

(base) root@a221771835f7:/scikit-learn/sklearn# python base.py
Traceback (most recent call last):
  File "base.py", line 11, in <module>
    from .externals import six
ModuleNotFoundError: No module named '__main__.externals'; '__main__' is not a package

sklearn/setup.py

(base) root@a221771835f7:/scikit-learn/sklearn# python setup.py
non-existing path in '__check_build': '_check_build.c'
Appending sklearn.__check_build configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.__check_build')
Appending sklearn._build_utils configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn._build_utils')
Appending sklearn.svm.tests configuration to sklearn.svm
Ignoring attempt to set 'name' (from 'sklearn.svm' to 'sklearn.svm.tests')
non-existing path in 'svm': 'libsvm.c'
non-existing path in 'svm': 'liblinear.c'
non-existing path in 'svm': 'libsvm_sparse.c'
Appending sklearn.svm configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.svm')
non-existing path in 'datasets': '_svmlight_format.c'
Appending sklearn.datasets configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.datasets')
Appending sklearn.datasets/tests configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.datasets/tests')
non-existing path in 'feature_extraction': '_hashing.c'
Appending sklearn.feature_extraction configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.feature_extraction')
Appending sklearn.feature_extraction/tests configuration to sklearn
Ignoring attempt to set 'name' (from 'sklearn' to 'sklearn.feature_extraction/tests')
non-existing path in 'cluster': '_dbscan_inner.cpp'
non-existing path in 'cluster': '_hierarchical.cpp'
non-existing path in 'cluster': '_k_means.c'
Appending sklearn.cluster configuration to sklearn
...

examples/feature_stacker.py

(base) root@a221771835f7:/scikit-learn/examples# python feature_stacker.py 
Fitting 3 folds for each of 18 candidates, totalling 54 fits
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9019607843137255, total=   0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9215686274509803, total=   0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9607843137254902, total=   0.0s
[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9215686274509803, total=   0.0s
[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9791666666666666, total=   0.0s
[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=0.9019607843137255, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9019607843137255, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10 
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[Parallel(n_jobs=1)]: Done  54 out of  54 | elapsed:    0.4s finished
Pipeline(memory=None,
     steps=[('features', FeatureUnion(n_jobs=1,
       transformer_list=[('pca', PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)), ('univ_select', SelectKBest(k=2, score_func=<function f_classif at 0x7f08e0594268>))],
       transformer...,
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False))])

##examples/missing_values.py

(base) root@a221771835f7:/scikit-learn/examples# python missing_values.py 
/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
Score with the entire dataset = 0.56
Traceback (most recent call last):
  File "missing_values.py", line 49, in <module>
    dtype=np.bool),
TypeError: 'numpy.float64' object cannot be interpreted as an integer

##plot_cv_predict.py

(base) root@a221771835f7:/scikit-learn/examples# python plot_cv_predict.py 
/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py:448: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  % get_backend())

４行追加
import matplotlib as mpl
mpl.use('Agg')
fig = plt.figure()
fig.savefig('img.png')

１行plt.show()を注釈に。

##benchmark/bench_20newsgroups.py

(base) root@a221771835f7:/scikit-learn/benchmarks# python bench_20newsgroups.py 
/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
usage: bench_20newsgroups.py [-h] -e
                             {dummy,random_forest,extra_trees,logistic_regression,naive_bayes,adaboost}
                             [{dummy,random_forest,extra_trees,logistic_regression,naive_bayes,adaboost} ...]
bench_20newsgroups.py: error: the following arguments are required: -e/--estimators

-e dummyを引数に。

(base) root@a221771835f7:/scikit-learn/benchmarks# python bench_20newsgroups.py -e dummy
/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
Downloading 20news dataset. This may take a few minutes.
Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)

#2. dockerを自力で構築する方へ

ここから下は、上記のpullしていただいたdockerをどういう方針で、どういう手順で作ったかを記録します。
上記のdockerを利用する上での参考資料です。本の続きを実行する上では必要ありません。
自力でdocker/anacondaを構築する場合の手順になります。
dockerfileを作る方法ではありません。ごめんなさい。
##docker

ubuntu, debianなどのLinuxを、linux, windows, mac osから共通に利用できる仕組み。
利用するOSの設定を変更せずに利用できるのがよい。
同じ仕様で、大量の人が利用することができる。
ソフトウェアの開発元が公式に対応しているものと、利用者が便利に仕立てたものの両方が利用可能である。今回は、公式に配布しているものを、自分で仕立てて、他の人にも利用できるようにする。
##python

DeepLearningの実習をPhthonで行って来た。
pythonを使う理由は、多くの機械学習の仕組みがpythonで利用できることと、Rなどの統計解析の仕組みもpythonから容易に利用できることがある。
###anaconda

pythonには、2と３という版の違いと、配布方法の違いなどがある。
Anacondaでpython3をこの１年半利用してきた。
Anacondaを利用した理由は、統計解析のライブラリと、JupyterNotebookが初めから入っているからである。
##docker公式配布

ubuntu, debianなどのOSの公式配布,gcc, anacondaなどの言語の公式配布などがある。
これらを利用し、docker-hubに登録することにより、公式配布の質の確認と、変更権を含む幅広い情報の共有ができる。dockerが公式配布するものではなく、それぞれのソフト提供者の公式配布という意味。
###docker pull

docker公式配布の利用は、URLからpullすることで実現する。
###docker Anaconda

anacondaが公式配布しているものを利用。

$  docker pull kaizenjapan/anaconda-keras
Using default tag: latest
latest: Pulling from continuumio/anaconda3
Digest: sha256:e07b9ca98ac1eeb1179dbf0e0bbcebd87701f8654878d6d8ce164d71746964d1
Status: Image is up to date for continuumio/anaconda3:latest

$ docker run -it -p 8888:8888 continuumio/anaconda3 /bin/bash

実際にはkeras, tensorflow を利用していた他のpushをpull

##apt-get

(base) root@d8857ae56e69:/# apt-get update

(base) root@d8857ae56e69:/# apt-get install -y procps

(base) root@d8857ae56e69:/# apt-get install -y vim

(base) root@d8857ae56e69:/# apt-get install -y apt-utils

(base) root@d8857ae56e69:/# apt-get install sudo

##ソース git

(base) root@f19e2f06eabb:/# git clone https://github.com/tshauck/scikit-learn

conda

(base) root@f19e2f06eabb:/d# conda update --prefix /opt/conda anaconda

pip

(base) root@f19e2f06eabb:/d# pip install --upgrade pip

#docker hubへの登録

$ docker ps
CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                    NAMES
caef766a99ff        continuumio/anaconda3   "/usr/bin/tini -- /b…"   10 hours ago        Up 10 hours         0.0.0.0:8888->8888/tcp   sleepy_bassi

$ docker commit caef766a99ff kaizenjapan/anaconda-indra

$ docker push kaizenjapan/anaconda-indra 

参考資料(reference)

なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

dockerで機械学習(1) with anaconda(1)「ゼロから作るDeep Learning - Pythonで学ぶディープラーニングの理論と実装」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/a7e94ef6dca128d035ab

dockerで機械学習(2)with anaconda(2)「ゼロから作るDeep Learning2自然言語処理編」斎藤 康毅 著
https://qiita.com/kaizen_nagoya/items/3b80dfc76933cea522c6

dockerで機械学習(3)with anaconda(3)「直感Deep Learning」Antonio Gulli、Sujit Pal　第1章,第2章
https://qiita.com/kaizen_nagoya/items/483ae708c71c88419c32

dockerで機械学習(71) 環境構築(1) docker どっかーら、どーやってもエラーばっかり。
https://qiita.com/kaizen_nagoya/items/690d806a4760d9b9e040

dockerで機械学習(72) 環境構築(2) Docker for Windows
https://qiita.com/kaizen_nagoya/items/c4daa5cf52e9f0c2c002

dockerで機械学習(73) 環境構築(3) docker/linux/macos bash スクリプト, ms-dos batchファイル
https://qiita.com/kaizen_nagoya/items/3f7b39110b7f303a5558

dockerで機械学習(74) 環境構築(4) R 難関いくつ？
https://qiita.com/kaizen_nagoya/items/5fb44773bc38574bcf1c

dockerで機械学習(75)環境構築(5)docker関連ファイルの管理
https://qiita.com/kaizen_nagoya/items/4f03df9a42c923087b5d

OpenCVをPythonで動かそうとしてlibGL.soが無いって言われたけど解決した。
https://qiita.com/toshitanian/items/5da24c0c0bd473d514c8

サーバサイドにおけるmatplotlibによる作図Tips
https://qiita.com/TomokIshii/items/3a26ee4453f535a69e9e

Dockerでホストとコンテナ間でのファイルコピー
https://qiita.com/gologo13/items/7e4e404af80377b48fd5

Docker for Mac でファイル共有を利用する
https://qiita.com/seijimomoto/items/1992d68de8baa7e29bb5

「名古屋のIoTは名古屋のOSで」Dockerをどっかーらどうやって使えばいいんでしょう。TOPPERS/FMP on RaspberryPi with Macintosh編 ５つの関門
https://qiita.com/kaizen_nagoya/items/9c46c6da8ceb64d2d7af

64bitCPUへの道 and/or 64歳の決意
https://qiita.com/kaizen_nagoya/items/cfb5ffa24ded23ab3f60

ゼロから作るDeepLearning２自然言語処理編　読書会の進め方（例）
https://qiita.com/kaizen_nagoya/items/025eb3f701b36209302e

Ubuntu 16.04 LTS で NVIDIA Docker を使ってみる
https://blog.amedama.jp/entry/2017/04/03/235901

文書履歴(document history)

ver. 0.10 初稿 20181023
ver. 0.02 URL追記　20230308

最後までおよみいただきありがとうございました。

いいね　💚、フォローをお願いします。

Thank you very much for reading to the last sentence.

Please press the like icon 💚　and follow me for your happy life.