Bash on WindowsでChainerを動作させる

なんかツールのインストールだけで殆ど使いこなせていないのだが、TensorFlowの次にBash on WindowsにChainerをインストールしてみた。

といっても、インストール自体はpipで一発で実行可能だ。

masayuki@FIXEDESK:~$ pip install chainer
Collecting chainer
  Downloading chainer-1.16.0.tar.gz (1.1MB)
    100% |████████████████████████████████| 1.1MB 1.0MB/s
Collecting filelock (from chainer)
  Downloading filelock-2.0.6.tar.gz
Requirement already satisfied (use --upgrade to upgrade): nose in ./.pyenv/versions/anaconda3-4.1.1/lib/python3.5/site-packages (from chainer) Requirement already satisfied (use --upgrade to upgrade): numpy>=1.9.0 in ./.pyenv/versions/anaconda3-4.1.1/lib/python3.5/site-packages (from chainer)
Requirement already satisfied (use --upgrade to upgrade): protobuf in ./.pyenv/versions/anaconda3-4.1.1/lib/python3.5/site-packages (from chainer)
Requirement already satisfied (use --upgrade to upgrade): six>=1.9.0 in ./.pyenv/versions/anaconda3-4.1.1/lib/python3.5/site-packages (from chainer)
Requirement already satisfied (use --upgrade to upgrade): setuptools in ./.pyenv/versions/anaconda3-4.1.1/lib/python3.5/site-packages/setuptools-23.0.0-py3.5.egg (from protobuf->chainer)
Building wheels for collected packages: chainer, filelock
  Running setup.py bdist_wheel for chainer ... done
  Stored in directory: /home/masayuki/.cache/pip/wheels/66/41/b3/64e4fc7d00104fc20af6d704ba3aaece4890aa1d45d32b5b3b
  Running setup.py bdist_wheel for filelock ... done
  Stored in directory: /home/masayuki/.cache/pip/wheels/97/9b/c0/47c42b4f38b378a65671364fb62ae7822b68b13f8048221d07
Successfully built chainer filelock
Installing collected packages: filelock, chainer
Successfully installed chainer-1.16.0 filelock-2.0.6
masayuki@FIXEDESK:~$

さっそく、Chainerのサンプルプログラムをダウンロードし、実行してみよう。

github.com

masayuki@FIXEDESK:~/work$ git clone https://github.com/pfnet/chainer.git
Cloning into 'chainer'...
remote: Counting objects: 41306, done.
remote: Compressing objects: 100% (65/65), done.
remote: Total 41306 (delta 24), reused 0 (delta 0), pack-reused 41241
Receiving objects: 100% (41306/41306), 12.67 MiB | 2.53 MiB/s, done.
Resolving deltas: 100% (29679/29679), done.
Checking connectivity... done.
masayuki@FIXEDESK:~/work$ cd chainer/examples/mnist/
masayuki@FIXEDESK:~/work/chainer/examples/mnist$ python train_mnist.py -g -1
GPU: -1
# unit: 1000
# Minibatch-size: 100
# epoch: 20

OMP: Error #100: Fatal system error detected.
OMP: System error #22: Invalid argument
中止 (コアダンプ)

おや、エラーを吐いて終了してしまった。これはBash on Windows固有のエラーかな？

この、OMP: Error #100: Fatal system error detected.を回避するためには、環境変数の設定が必要そうだった。参考にしたのは、githubの以下のディスカッションだ。

github.com

KMP_AFFINITY=disabledという環境変数を設定する必要がありそうだ。設定して実行してみよう。

masayuki@FIXEDESK:~/work/chainer/examples/mnist$ KMP_AFFINITY=disabled python train_mnist.py -g -1
GPU: -1
# unit: 1000
# Minibatch-size: 100
# epoch: 20

epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy
1           0.191856    0.104489              0.941767       0.9672
2           0.0716813   0.0846484             0.977583       0.9732
3           0.0481666   0.0801073             0.98455        0.9777
4           0.0353043   0.0716036             0.9885         0.978
5           0.0287204   0.0856727             0.990267       0.9785
6           0.0237751   0.0714374             0.991783       0.9812
7           0.0206211   0.0803223             0.9933         0.9806
8           0.0207687   0.0877314             0.9934         0.9784
9           0.0170502   0.0871984             0.99455        0.9804
10          0.0140119   0.0920289             0.995417       0.9815
11          0.0174262   0.0906213             0.994433       0.9798
12          0.0129915   0.10583               0.995817       0.9782
13          0.0117543   0.0844722             0.996317       0.9838
14          0.00811143  0.0858693             0.99765        0.9833
15          0.0111486   0.0911458             0.996733       0.9832
16          0.00956446  0.093201              0.99695        0.9836
17          0.0161112   0.0958432             0.995533       0.9829
18          0.0075644   0.0988045             0.9978         0.9827
19          0.0108992   0.105838              0.997283       0.9828
20          0.00950929  0.0935197             0.997183       0.9832