1.すぐに利用したい方へ(as soon as)
「Deep Learning with Theano 」By Christopher Bourez
http://shop.oreilly.com/product/9781786465825.do
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 pull and run
$ docker pull kaizenjapan/anaconda-christopher
$ docker run -it -p 8888:8888 kaizenjapan/anaconda-christopher /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。
1-simple.py
(base) root@a221771835f7:/# cd Deep-Learning-with-Theano/
(base) root@a221771835f7:/Deep-Learning-with-Theano# ls
Chapter02 Chapter03 Chapter04 Chapter05 Chapter06 Chapter10 Chapter11 Chapter12 Chapter13 LICENSE README.md
(base) root@a221771835f7:/Deep-Learning-with-Theano# cd Chapter02
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# ls
1-simple.py 2-multi.py 3-cnn.py 4-plot.py 5-cnn-with-dropout.py 6-display-activation-functions.py README.md
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# python 1-simple.py
Using device cpu
Loading data
Traceback (most recent call last):
File "1-simple.py", line 13, in <module>
with gzip.open(data_dir + "mnist.pkl.gz", 'rb') as f:
File "/opt/conda/lib/python3.6/gzip.py", line 53, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/opt/conda/lib/python3.6/gzip.py", line 163, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: '/sharedfiles/mnist.pkl.gz'
(base) root@a221771835f7:/# mkdir sharedfiles
(base) root@a221771835f7:/# cd sharedfiles/
(base) root@a221771835f7:/sharedfiles# wget https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz
--2018-10-23 10:42:27-- https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz
Resolving github.com (github.com)... 192.30.255.113, 192.30.255.112
Connecting to github.com (github.com)|192.30.255.113|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/html]
Saving to: ‘mnist.pkl.gz’
mnist.pkl.gz [ <=> ] 42.39K 178KB/s in 0.2s
2018-10-23 10:42:28 (178 KB/s) - ‘mnist.pkl.gz’ saved [43403]
(base) root@a221771835f7:/# cd /Deep-Learning-with-Theano
(base) root@a221771835f7:/Deep-Learning-with-Theano# cd Chapter02
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# python 1-simple.py
Using device cpu
Loading data
Traceback (most recent call last):
File "1-simple.py", line 14, in <module>
train_set, valid_set, test_set = pickle.load(f)
File "/opt/conda/lib/python3.6/gzip.py", line 296, in peek
return self._buffer.peek(n)
File "/opt/conda/lib/python3.6/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/opt/conda/lib/python3.6/gzip.py", line 463, in read
if not self._read_gzip_header():
File "/opt/conda/lib/python3.6/gzip.py", line 411, in _read_gzip_header
raise OSError('Not a gzipped file (%r)' % magic)
OSError: Not a gzipped file (b'\n\n')
4-plot.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# python 4-plot.py
Traceback (most recent call last):
File "4-plot.py", line 5, in <module>
curves[0] = { 'data' : numpy.load("simple_valid_loss.npy"), 'name' : "simple"}
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py", line 384, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'simple_valid_loss.npy'
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# wget https://git.rcc.uchicago.edu/ivy2/DL_Theano/blob/d664855aa9be2761bb5e8bf346c19b1671bd9e1b/labs/8/simple_valid_loss.npy
--2018-10-23 10:48:22-- https://git.rcc.uchicago.edu/ivy2/DL_Theano/blob/d664855aa9be2761bb5e8bf346c19b1671bd9e1b/labs/8/simple_valid_loss.npy
Resolving git.rcc.uchicago.edu (git.rcc.uchicago.edu)... 128.135.112.102
Connecting to git.rcc.uchicago.edu (git.rcc.uchicago.edu)|128.135.112.102|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/html]
Saving to: ‘simple_valid_loss.npy’
simple_valid_loss.npy [ <=> ] 27.75K --.-KB/s in 0.002s
2018-10-23 10:48:23 (15.7 MB/s) - ‘simple_valid_loss.npy’ saved [28411]
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# wget https://git.rcc.uchicago.edu/ivy2/DL_Theano/blob/d664855aa9be2761bb5e8bf346c19b1671bd9e1b/labs/9/mlp_valid_loss.npy
--2018-10-23 10:49:20-- https://git.rcc.uchicago.edu/ivy2/DL_Theano/blob/d664855aa9be2761bb5e8bf346c19b1671bd9e1b/labs/9/mlp_valid_loss.npy
Resolving git.rcc.uchicago.edu (git.rcc.uchicago.edu)... 128.135.112.102
Connecting to git.rcc.uchicago.edu (git.rcc.uchicago.edu)|128.135.112.102|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/html]
Saving to: ‘mlp_valid_loss.npy’
mlp_valid_loss.npy [ <=> ] 27.69K --.-KB/s in 0.001s
2018-10-23 10:49:21 (38.2 MB/s) - ‘mlp_valid_loss.npy’ saved [28354]
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# python 4-plot.py
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py", line 440, in load
return pickle.load(fid, **pickle_kwargs)
_pickle.UnpicklingError: invalid load key, '<'.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "4-plot.py", line 5, in <module>
curves[0] = { 'data' : numpy.load("simple_valid_loss.npy"), 'name' : "simple"}
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py", line 443, in load
"Failed to interpret file %s as a pickle" % repr(file))
OSError: Failed to interpret file 'simple_valid_loss.npy' as a pickle
simple.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# python 6-display-activation-functions.py
Compiling
Display
length 12
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9.99329300e-01, 9.99393106e-01, 9.99450844e-01, 9.99503090e-01,
9.99550366e-01, 9.99593146e-01, 9.99631856e-01, 9.99666884e-01,
9.99698579e-01, 9.99727259e-01, 9.99753211e-01, 9.99776693e-01,
9.99797942e-01, 9.99817168e-01, 9.99834566e-01, 9.99850308e-01,
9.99864552e-01, 9.99877441e-01, 9.99889103e-01, 9.99899656e-01,
9.99909204e-01, 9.99917844e-01, 9.99925662e-01, 9.99932736e-01,
9.99939137e-01, 9.99944929e-01, 9.99950169e-01, 9.99954911e-01,
9.99959202e-01, 9.99963084e-01, 9.99966597e-01, 9.99969776e-01,
9.99972652e-01, 9.99975254e-01, 9.99977609e-01, 9.99979740e-01,
9.99981668e-01, 9.99983412e-01, 9.99984991e-01, 9.99986419e-01]), array([2.45765474e-05, 2.71612504e-05, 3.00177811e-05, 3.31747264e-05,
3.66636788e-05, 4.05195535e-05, 4.47809367e-05, 4.94904724e-05,
5.46952884e-05, 6.04474683e-05, 6.68045716e-05, 7.38302104e-05,
8.15946846e-05, 9.01756856e-05, 9.96590727e-05, 1.10139732e-04,
1.21722523e-04, 1.34523332e-04, 1.48670222e-04, 1.64304721e-04,
1.81583231e-04, 2.00678590e-04, 2.21781801e-04, 2.45103938e-04,
2.70878252e-04, 2.99362502e-04, 3.30841523e-04, 3.65630068e-04,
4.04075948e-04, 4.46563497e-04, 4.93517399e-04, 5.45406921e-04,
6.02750578e-04, 6.66121293e-04, 7.36152090e-04, 8.13542382e-04,
8.99064911e-04, 9.93573408e-04, 1.09801105e-03, 1.21341980e-03,
1.34095068e-03, 1.48187517e-03, 1.63759768e-03, 1.80966943e-03,
1.99980362e-03, 2.20989229e-03, 2.44202474e-03, 2.69850798e-03,
2.98188910e-03, 3.29498004e-03, 3.64088472e-03, 4.02302893e-03,
4.44519319e-03, 4.91154880e-03, 5.42669750e-03, 5.99571483e-03,
6.62419784e-03, 7.31831711e-03, 8.08487387e-03, 8.93136222e-03,
9.86603717e-03, 1.08979886e-02, 1.20372220e-02, 1.32947455e-02,
1.46826651e-02, 1.62142868e-02, 1.79042268e-02, 1.97685301e-02,
2.18247977e-02, 2.40923212e-02, 2.65922267e-02, 2.93476258e-02,
3.23837743e-02, 3.57282364e-02, 3.94110540e-02, 4.34649189e-02,
4.79253442e-02, 5.28308330e-02, 5.82230387e-02, 6.41469115e-02,
7.06508249e-02, 7.77866720e-02, 8.56099237e-02, 9.41796330e-02,
1.03558374e-01, 1.13812096e-01, 1.25009871e-01, 1.37223519e-01,
1.50527076e-01, 1.64996078e-01, 1.80706639e-01, 1.97734276e-01,
2.16152459e-01, 2.36030850e-01, 2.57433197e-01, 2.80414866e-01,
3.05019996e-01, 3.31278268e-01, 3.59201316e-01, 3.88778819e-01,
4.19974342e-01, 4.52721037e-01, 4.86917361e-01, 5.22422988e-01,
5.59055168e-01, 5.96585808e-01, 6.34739590e-01, 6.73193450e-01,
7.11577763e-01, 7.49479518e-01, 7.86447733e-01, 8.22001229e-01,
8.55638786e-01, 8.86851493e-01, 9.15136962e-01, 9.40014849e-01,
9.61042983e-01, 9.77833247e-01, 9.90066291e-01, 9.97504161e-01,
1.00000000e+00, 9.97504161e-01, 9.90066291e-01, 9.77833247e-01,
9.61042983e-01, 9.40014849e-01, 9.15136962e-01, 8.86851493e-01,
8.55638786e-01, 8.22001229e-01, 7.86447733e-01, 7.49479518e-01,
7.11577763e-01, 6.73193450e-01, 6.34739590e-01, 5.96585808e-01,
5.59055168e-01, 5.22422988e-01, 4.86917361e-01, 4.52721037e-01,
4.19974342e-01, 3.88778819e-01, 3.59201316e-01, 3.31278268e-01,
3.05019996e-01, 2.80414866e-01, 2.57433197e-01, 2.36030850e-01,
2.16152459e-01, 1.97734276e-01, 1.80706639e-01, 1.64996078e-01,
1.50527076e-01, 1.37223519e-01, 1.25009871e-01, 1.13812096e-01,
1.03558374e-01, 9.41796330e-02, 8.56099237e-02, 7.77866720e-02,
7.06508249e-02, 6.41469115e-02, 5.82230387e-02, 5.28308330e-02,
4.79253442e-02, 4.34649189e-02, 3.94110540e-02, 3.57282364e-02,
3.23837743e-02, 2.93476258e-02, 2.65922267e-02, 2.40923212e-02,
2.18247977e-02, 1.97685301e-02, 1.79042268e-02, 1.62142868e-02,
1.46826651e-02, 1.32947455e-02, 1.20372220e-02, 1.08979886e-02,
9.86603717e-03, 8.93136222e-03, 8.08487387e-03, 7.31831711e-03,
6.62419784e-03, 5.99571483e-03, 5.42669750e-03, 4.91154880e-03,
4.44519319e-03, 4.02302893e-03, 3.64088472e-03, 3.29498004e-03,
2.98188910e-03, 2.69850798e-03, 2.44202474e-03, 2.20989229e-03,
1.99980362e-03, 1.80966943e-03, 1.63759768e-03, 1.48187517e-03,
1.34095068e-03, 1.21341980e-03, 1.09801105e-03, 9.93573408e-04,
8.99064911e-04, 8.13542382e-04, 7.36152090e-04, 6.66121293e-04,
6.02750578e-04, 5.45406921e-04, 4.93517399e-04, 4.46563497e-04,
4.04075948e-04, 3.65630068e-04, 3.30841523e-04, 2.99362502e-04,
2.70878252e-04, 2.45103938e-04, 2.21781801e-04, 2.00678590e-04,
1.81583231e-04, 1.64304721e-04, 1.48670222e-04, 1.34523332e-04,
1.21722523e-04, 1.10139732e-04, 9.96590727e-05, 9.01756856e-05,
8.15946846e-05, 7.38302104e-05, 6.68045716e-05, 6.04474683e-05,
5.46952884e-05, 4.94904724e-05, 4.47809367e-05, 4.05195535e-05,
3.66636788e-05, 3.31747264e-05, 3.00177811e-05, 2.71612504e-05])]
(240,) (240,)
/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())
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter02# vi 6-display-activation-functions.py
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
1-train-CBOW.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter03# python 1-train-CBOW.py
Traceback (most recent call last):
File "1-train-CBOW.py", line 165, in <module>
words = get_words(args.data_file)
File "1-train-CBOW.py", line 38, in get_words
with open(fname) as fin:
FileNotFoundError: [Errno 2] No such file or directory: '/sharedfiles/text8'
2-plot.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter03# python 2-plot.py
Traceback (most recent call last):
File "2-plot.py", line 8, in <module>
with open('idx2word.pkl', 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'idx2word.pkl'
plot.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter04# python plot.py
Traceback (most recent call last):
File "plot.py", line 17, in <module>
data = numpy.load("train_loss_word_" + typ + "_h" + str(h) + "_e30.npy")
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py", line 384, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'train_loss_word_simple_h500_e30.npy'
predict.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter04# python predict.py
Traceback (most recent call last):
File "predict.py", line 6, in <module>
import models
File "/Deep-Learning-with-Theano/Chapter04/models/__init__.py", line 1, in <module>
import simple
ModuleNotFoundError: No module named 'simple'
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter04# pip install simple
Collecting simple
Could not find a version that satisfies the requirement simple (from versions: )
No matching distribution found for simple
train.py
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter04# python train.py
Traceback (most recent call last):
File "train.py", line 8, in <module>
import models
File "/Deep-Learning-with-Theano/Chapter04/models/__init__.py", line 1, in <module>
import simple
ModuleNotFoundError: No module named 'simple'
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter04# cd ../Chapter05
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter05# ls
README.md bilstm.py download_tweets.py sem_eval2103.dev sem_eval2103.test sem_eval2103.train
(base) root@a221771835f7:/Deep-Learning-with-Theano/Chapter05# python bilstm.py
Using TensorFlow backend.
Train size: (5605, 43)
Dev size: (598, 43)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 43, 100) 987600
_________________________________________________________________
bidirectional_1 (Bidirection (None, 128) 84480
_________________________________________________________________
dense_1 (Dense) (None, 3) 387
_________________________________________________________________
activation_1 (Activation) (None, 3) 0
=================================================================
Total params: 1,072,467
Trainable params: 1,072,467
Non-trainable params: 0
_________________________________________________________________
None
Train on 5605 samples, validate on 598 samples
Epoch 1/30
2018-10-23 11:01:15.256878: 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
2018-10-23 11:01:15.257313: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
5605/5605 [==============================] - 64s 11ms/step - loss: 0.7264 - acc: 0.6806 - val_loss: 0.6455 - val_acc: 0.7090
Epoch 2/30
5605/5605 [==============================] - 56s 10ms/step - loss: 0.5864 - acc: 0.7682 - val_loss: 0.6417 - val_acc: 0.7057
Epoch 3/30
5605/5605 [==============================] - 62s 11ms/step - loss: 0.5210 - acc: 0.7995 - val_loss: 0.7114 - val_acc: 0.7241
Epoch 4/30
5605/5605 [==============================] - 62s 11ms/step - loss: 0.4776 - acc: 0.8182 - val_loss: 0.6850 - val_acc: 0.7074
Epoch 5/30
5605/5605 [==============================] - 60s 11ms/step - loss: 0.4406 - acc: 0.8316 - val_loss: 0.6630 - val_acc: 0.7140
Epoch 6/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.4079 - acc: 0.8409 - val_loss: 0.6768 - val_acc: 0.6806
Epoch 7/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.3775 - acc: 0.8505 - val_loss: 0.6932 - val_acc: 0.6839
Epoch 8/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.3515 - acc: 0.8608 - val_loss: 0.7348 - val_acc: 0.6789
Epoch 9/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.3342 - acc: 0.8710 - val_loss: 0.7590 - val_acc: 0.6739
Epoch 10/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.3197 - acc: 0.8721 - val_loss: 0.7194 - val_acc: 0.6656
Epoch 11/30
5605/5605 [==============================] - 60s 11ms/step - loss: 0.3005 - acc: 0.8840 - val_loss: 0.7525 - val_acc: 0.6438
Epoch 12/30
5605/5605 [==============================] - 61s 11ms/step - loss: 0.2944 - acc: 0.8869 - val_loss: 0.7490 - val_acc: 0.6639
Epoch 13/30
5605/5605 [==============================] - 53s 9ms/step - loss: 0.2863 - acc: 0.8864 - val_loss: 0.8113 - val_acc: 0.6120
Epoch 14/30
5605/5605 [==============================] - 66s 12ms/step - loss: 0.2755 - acc: 0.8899 - val_loss: 0.7793 - val_acc: 0.6137
Epoch 15/30
5605/5605 [==============================] - 64s 11ms/step - loss: 0.2695 - acc: 0.8928 - val_loss: 0.8315 - val_acc: 0.5819
Epoch 16/30
5605/5605 [==============================] - 80s 14ms/step - loss: 0.2619 - acc: 0.8926 - val_loss: 0.8053 - val_acc: 0.6472
Epoch 17/30
5605/5605 [==============================] - 63s 11ms/step - loss: 0.2590 - acc: 0.8944 - val_loss: 0.9162 - val_acc: 0.5786
Epoch 18/30
5605/5605 [==============================] - 54s 10ms/step - loss: 0.2493 - acc: 0.8951 - val_loss: 0.8483 - val_acc: 0.5803
Epoch 19/30
5605/5605 [==============================] - 60s 11ms/step - loss: 0.2438 - acc: 0.8969 - val_loss: 0.9386 - val_acc: 0.5485
Epoch 20/30
5605/5605 [==============================] - 68s 12ms/step - loss: 0.2394 - acc: 0.8999 - val_loss: 0.8595 - val_acc: 0.6020
Epoch 21/30
5605/5605 [==============================] - 57s 10ms/step - loss: 0.2356 - acc: 0.8972 - val_loss: 1.0282 - val_acc: 0.5301
Epoch 22/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2334 - acc: 0.8988 - val_loss: 1.0866 - val_acc: 0.5151
Epoch 23/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2304 - acc: 0.9013 - val_loss: 0.9207 - val_acc: 0.5485
Epoch 24/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2257 - acc: 0.8981 - val_loss: 0.9469 - val_acc: 0.5418
Epoch 25/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2231 - acc: 0.9008 - val_loss: 0.9997 - val_acc: 0.5050
Epoch 26/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2191 - acc: 0.9004 - val_loss: 0.9087 - val_acc: 0.5050
Epoch 27/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2144 - acc: 0.9051 - val_loss: 0.9829 - val_acc: 0.5602
Epoch 28/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2107 - acc: 0.9017 - val_loss: 0.9858 - val_acc: 0.4883
Epoch 29/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2125 - acc: 0.9012 - val_loss: 0.9735 - val_acc: 0.5184
Epoch 30/30
5605/5605 [==============================] - 52s 9ms/step - loss: 0.2108 - acc: 0.9028 - val_loss: 1.0839 - val_acc: 0.5234
Test size: (2588, 43)
2588/2588 [==============================] - 2s 695us/step
Testing loss: 1.2221; Testing Accuracy: 45.13%
#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
(base) root@d8857ae56e69:/# apt update; apt -y upgrade
(base) root@d8857ae56e69:/# apt-get install -y procps vim apt-utils sudo
ソース git
(base) root@f19e2f06eabb:/# git clone https://github.com/PacktPublishing/Deep-Learning-with-Theano
conda
# conda update --prefix /opt/conda anaconda
Solving environment: done
# conda install theano
pip
(base) root@f19e2f06eabb:/d# pip install --upgrade pip
Collecting pip
Downloading https://files.pythonhosted.org/packages/5f/25/e52d3f31441505a5f3af41213346e5b6c221c9e086a166f3703d2ddaf940/pip-18.0-py2.py3-none-any.whl (1.3MB)
100% |████████████████████████████████| 1.3MB 2.0MB/s
distributed 1.21.8 requires msgpack, which is not installed.
Installing collected packages: pip
Found existing installation: pip 10.0.1
Uninstalling pip-10.0.1:
Successfully uninstalled pip-10.0.1
Successfully installed pip-18.0
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-christopher
$ docker push kaizenjapan/anaconda-christopher
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
Ethernet 記事一覧 Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794
Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0
線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001
C++ Support(0)
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514
Coding Rules(0) C Secure , MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0
Autosar Guidelines C++14 example code compile list(1-169)
https://qiita.com/kaizen_nagoya/items/8ccbf6675c3494d57a76
Error一覧(C/C++, python, bash...) Error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8
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
言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4
プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394
一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39
官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3
プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945
LaTeX(0) 一覧
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792
自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b
Rust(0) 一覧
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927
小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53
文書履歴(document history)
ver. 0.10 初稿 20181023
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