(base) chibi@1604:~$ conda -V conda 4.6.11 (base) chibi@1604:~$ conda activate tensor (tensor) chibi@1604:~$ ls Anaconda3-2019.03-Linux-x86_64.sh chainer-5.1.0 テンプレート ピクチャ NVIDIA_CUDA-10.1_Samples examples.desktop デスクトップ ミュージック anaconda3 keras ドキュメント 公開 caffe ダウンロード ビデオ (tensor) chibi@1604:~$ cd chainer-5.1.0/examples/mnist (tensor) chibi@1604:~/chainer-5.1.0/examples/mnist$ ls README.md train_mnist.py train_mnist_model_parallel.py __pycache__ train_mnist_custom_loop.py result_parallel train_mnist_data_parallel.py (tensor) chibi@1604:~/chainer-5.1.0/examples/mnist$ time python train_mnist_data_parallel.py GPU: 0, 1 # unit: 1000 # Minibatch-size: 400 # epoch: 20 epoch main/loss validation/main/loss main/accuracy validation/main/accuracy elapsed_time 1 0.274282 0.115424 0.9203 0.9631 5.1321 2 0.0867528 0.0810139 0.9743 0.9737 6.94632 3 0.054935 0.0679963 0.9834 0.979 8.75719 4 0.0347653 0.0743131 0.989834 0.9772 10.5643 5 0.0227992 0.065761 0.992601 0.979 12.3844 6 0.0182269 0.0625305 0.994367 0.9823 14.1954 7 0.0138057 0.0701176 0.995367 0.9791 16.1016 8 0.012327 0.0690631 0.996 0.9806 17.9167 9 0.00993545 0.0638363 0.996634 0.9834 19.7251 10 0.00641677 0.0736377 0.9977 0.9832 21.5426 11 0.007406 0.0729153 0.9974 0.9821 23.3586 12 0.0102155 0.0735999 0.9965 0.9803 25.2601 13 0.00751048 0.0711929 0.997534 0.9827 27.0784 14 0.00528458 0.0780963 0.998333 0.9827 28.8867 15 0.00629708 0.0853543 0.9979 0.9806 30.6989 16 0.0100287 0.0811843 0.996567 0.982 32.5801 17 0.00556478 0.0857182 0.998233 0.9813 34.3961 18 0.00432221 0.079638 0.9986 0.9824 36.2123 19 0.0047541 0.111151 0.998333 0.9788 38.0227 20 0.00835689 0.104239 0.9972 0.9803 39.8926 real 0m43.159s user 0m41.567s sys 0m2.065s (tensor) chibi@1604:~/chainer-5.1.0/examples/mnist$ time python train_mnist_data_parallel.py GPU: 0, 1 # unit: 1000 # Minibatch-size: 400 # epoch: 20 epoch main/loss validation/main/loss main/accuracy validation/main/accuracy elapsed_time 1 0.284627 0.11537 0.9173 0.9632 4.40681 2 0.0879655 0.0785932 0.973267 0.9753 6.23098 3 0.0531775 0.0672076 0.983167 0.9778 8.04492 4 0.0349778 0.0666743 0.9889 0.9784 9.86593 5 0.0237014 0.0687982 0.992633 0.9807 11.6858 6 0.0166031 0.0655213 0.995067 0.9786 13.5182 7 0.0130841 0.0611077 0.995833 0.9823 15.3321 8 0.0120216 0.0745543 0.996 0.9791 17.1514 9 0.0124193 0.0729016 0.996034 0.9804 18.9642 10 0.0101633 0.0709979 0.996433 0.9815 20.7827 11 0.0076502 0.0672552 0.997633 0.9828 22.6083 12 0.00598451 0.0773641 0.998134 0.9805 24.4241 13 0.00727973 0.0834079 0.997533 0.9808 26.247 14 0.0100392 0.0914496 0.996634 0.9783 28.0571 15 0.00778329 0.0680686 0.997833 0.984 29.8738 16 0.00838753 0.0916685 0.997367 0.9797 31.6945 17 0.00608489 0.0907727 0.997767 0.9802 33.5102 18 0.00495106 0.0814183 0.9984 0.983 35.3338 19 0.00310424 0.085442 0.9988 0.9832 37.1426 20 0.00214057 0.0898567 0.9992 0.9827 38.9706 real 0m41.395s user 0m41.360s sys 0m1.876s (tensor) chibi@1604:~/chainer-5.1.0/examples/mnist$ cd (tensor) chibi@1604:~$ cat /etc/os-release NAME="Ubuntu" VERSION="16.04.6 LTS (Xenial Xerus)" ID=ubuntu ID_LIKE=debian PRETTY_NAME="Ubuntu 16.04.6 LTS" VERSION_ID="16.04" HOME_URL="http://www.ubuntu.com/" SUPPORT_URL="http://help.ubuntu.com/" BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/" VERSION_CODENAME=xenial UBUNTU_CODENAME=xenial (tensor) chibi@1604:~$ nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Wed_Apr_24_19:10:27_PDT_2019 Cuda compilation tools, release 10.1, V10.1.168 (tensor) chibi@1604:~$ sudo hddtemp /dev/sda [sudo] chibi のパスワード: /dev/sda: TS240GSSD220S: 39°C (tensor) chibi@1604:~$ conda list WARNING: The conda.compat module is deprecated and will be removed in a future release. # packages in environment at /home/chibi/anaconda3/envs/tensor: # # Name Version Build Channel _tflow_select 2.1.0 gpu absl-py 0.7.1 py37_0 astor 0.7.1 py37_0 blas 1.0 mkl c-ares 1.15.0 h7b6447c_1 ca-certificates 2019.1.23 0 certifi 2019.3.9 py37_0 chainer 5.3.0 py37_0 cudatoolkit 10.0.130 0 cudnn 7.3.1 cuda10.0_0 cupti 10.0.130 0 cupy 5.1.0 py37hc0ce245_0 fastrlock 0.4 py37he6710b0_0 filelock 3.0.10 py37_0 gast 0.2.2 py37_0 grpcio 1.16.1 py37hf8bcb03_1 h5py 2.9.0 py37h7918eee_0 hdf5 1.10.4 hb1b8bf9_0 intel-openmp 2019.3 199 keras 2.2.4 0 keras-applications 1.0.7 py_0 keras-base 2.2.4 py37_0 keras-preprocessing 1.0.9 py_0 libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 8.2.0 hdf63c60_1 libgfortran-ng 7.3.0 hdf63c60_0 libprotobuf 3.7.1 hd408876_0 libstdcxx-ng 8.2.0 hdf63c60_1 markdown 3.1 py37_0 mkl 2019.3 199 mkl_fft 1.0.12 py37ha843d7b_0 mkl_random 1.0.2 py37hd81dba3_0 mock 2.0.0 py37_0 nccl 1.3.5 cuda10.0_0 ncurses 6.1 he6710b0_1 numpy 1.16.3 py37h7e9f1db_0 numpy-base 1.16.3 py37hde5b4d6_0 openssl 1.1.1b h7b6447c_1 pbr 5.1.3 py_0 pip 19.1.1 py37_0 protobuf 3.7.1 py37he6710b0_0 python 3.7.3 h0371630_0 pyyaml 5.1 py37h7b6447c_0 readline 7.0 h7b6447c_5 scipy 1.2.1 py37h7c811a0_0 setuptools 41.0.1 py37_0 six 1.12.0 py37_0 sqlite 3.28.0 h7b6447c_0 tensorboard 1.13.1 py37hf484d3e_0 tensorflow 1.13.1 gpu_py37hc158e3b_0 tensorflow-base 1.13.1 gpu_py37h8d69cac_0 tensorflow-estimator 1.13.0 py_0 tensorflow-gpu 1.13.1 h0d30ee6_0 termcolor 1.1.0 py37_1 tk 8.6.8 hbc83047_0 werkzeug 0.15.2 py_0 wheel 0.33.4 py37_0 xz 5.2.4 h14c3975_4 yaml 0.1.7 had09818_2 zlib 1.2.11 h7b6447c_3 (tensor) chibi@1604:~$