CPU \u306e\u307f\u3067\u306e\u5b66\u7fd2\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308b\u306e\u3067\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u3092\u8cfc\u5165\u3057\u3066 Tensorflow GPU\u7248\u3092\u8a66\u3057\u3066\u307f\u307e\u3057\u305f\u3002
\nNVIDIA\u793e \u306e CUDA \u3068 cuDNN \u306f\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u306e GPU\uff08Graphics Processing Unit\uff09\u3092\u5229\u7528\u3057\u3066\u6df1\u5c64\u5b66\u7fd2\u306e\u51e6\u7406\u3092\u9ad8\u901f\u5316\u3057\u307e\u3059\u3002
\n\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30ab\u30fc\u30c9\u306e GPU \u306f\u753b\u50cf\u51e6\u7406\u306e\u9ad8\u901f\u5316\u306e\u305f\u3081\u306b\u4e26\u5217\u51e6\u7406\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u304c\u3001\u6df1\u5c64\u5b66\u7fd2\u3067\u3053\u306e\u6a5f\u80fd\u3092\u5229\u7528\u3059\u308b\u306e\u304c cuDNN \u3067\u3059\u3002
\nCUDA\uff08Compute Unified Device Archtecture\uff09\u306f\u3001GPU\u3092\u5229\u7528\u3059\u308b\u305f\u3081\u306e\u7d71\u5408\u958b\u767a\u74b0\u5883\u3067\u3059<\/p>\n
<\/p>\n
\uff1c\uff1c\uff1c\u3000\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u3000\uff1e\uff1e\uff1e<\/p>\n
\u30cf\u30fc\u30c9\uff1aNEC Express5800 110Ge (CPU\u4ea4\u63db\u3001\u30e1\u30e2\u30ea\u5897)
\nCPU\uff1a Intel Core2Duo 2.8 GHz
\n\u30e1\u30e2\u30ea\uff1a 6 GByte
\n\u672c\u4f53\u96fb\u6e90\uff1a 380W
\n\uff08\u3054\u6ce8\u610f\uff1a\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u306e\u6d88\u8cbb\u96fb\u529b\u304c\u5927\u304d\u3044\u306e\u3067\u96fb\u6e90\u306e\u4f9b\u7d66\u80fd\u529b\u304c\u4f4e\u3044\u3068\u5371\u967a\u3067\u3059\uff09
\n\u305d\u306e\u4ed6\uff1a \u30de\u30b6\u30fc\u30dc\u30fc\u30c9\u306e PCI Express x1 \u3092PCI Express x16 \u306b\u5909\u63db\u3059\u308b\u30ad\u30c3\u30c8\uff08\u672c\u4f53\u306b\u306f\u3001PCI Express x8 \u3057\u304b\u306a\u3044\u306e\u3067\uff09<\/p>\n
\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\uff1a
\nGPU \u306f\u4e0b\u8a18\u306e\uff12\u7a2e\u985e\u3092\u8a66\u3057\u3066\u307f\u307e\u3057\u305f
\n\u2460 NVIDIA GEFORCE GTX960 \u30e1\u30e2\u30ea 4GB
\n\u2461 NVIDIA GEFORCE GT1030 \u30e1\u30e2\u30ea 2GB
\n\uff08\u304a\u305d\u3089\u304f\u6700\u4f4e\u9650\u306e\u30b9\u30da\u30c3\u30af\u3067\u3042\u307e\u308a\u304a\u52e7\u3081\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u8a73\u7d30\u306f\u4e0b\u8a18\u306e\u901a\u308a\u3067\u3059\uff09<\/p>\n
<\/p>\n
\uff1c\uff1c\u3000\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3000\uff1e\uff1e\uff1e<\/p>\n
(1) \u30c9\u30e9\u30a4\u30d0\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\nUbuntu \u306e \u753b\u9762\u304b\u3089\u3001[\u30b7\u30b9\u30c6\u30e0\u8a2d\u5b9a]->[\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3068\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8]->[\u8ffd\u52a0\u306e\u30c9\u30e9\u30a4\u30d0\u30fc]\u3092\u9078\u629e\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002<\/p>\n
NVIDIA binary driver version 390.30 \u3092\u9078\u629e\u3057\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002 cuda-repo-ubuntu1604_8.0.61-1_amd64.deb<\/p>\n \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb cuDNN v6.0 Runtime Library for Ubuntu16.04 (Deb) \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb \u30d1\u30b9\u8a2d\u5b9a > pip install tensorflow-gpu==1.3.0 —- \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306f\u4ee5\u4e0a\u3067\u3059\u3002<\/p>\n <\/p>\n \uff1c\uff1c\uff1c\u3000\u5b66\u7fd2\u3068\u30c6\u30b9\u30c8\u3000\uff1e\uff1e\uff1e<\/p>\n (1) NVIDIA GEFORCE GTX960 \u30e1\u30e2\u30ea 4GB \u5b66\u7fd2\u6642\u9593————————————————- \u5b66\u7fd2\u6642\u9593\u304c\u3001\uff12\u6642\u9593\u4ee5\u4e0a\u304b\u3089\uff16\u5206\u7a0b\u5ea6\u306b\u5927\u5e45\u6539\u5584\u3057\u307e\u3057\u305f\u3002<\/p>\n \u2461 \u300cAI\u8ab2\u984c1\uff1aDNA\u76f8\u540c\u6027\u691c\u7d22\u3068AI(2) 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e\u300d<\/a> \u306e\u300c\u5168\u7d50\u5408\u578b\uff11\u5c64\u3001\u30ce\u30fc\u30c9\u6570\u306f\u30011024\u5869\u57fa\u00d716(4\u5869\u57fa\u00d74\u5869\u57fa)=16834\u300d\u3067\u30c6\u30b9\u30c8\u3057\u305f\u7d50\u679c\u3067\u3059\u3002<\/p>\n \u5b66\u7fd2\u6642\u9593————————————————- \u5b66\u7fd2\u6642\u9593\u306f\u3001\uff12\u6642\u9593\u534a\u7a0b\u5ea6\u304b\u3089\uff16\u5206\u7a0b\u5ea6\u306b\u5927\u5e45\u6539\u5584\u3057\u307e\u3057\u305f\u3002 \u2460\u300c\u6df1\u5c64\u5b66\u7fd2 TensorFlow \u3092 MNIST \u3067\u8a66\u3059(2)\u300d<\/span><\/u>\u306e\u4e2d\u9593\u5c64\uff12\u5c64\u3067\u30c6\u30b9\u30c8\u3002<\/p>\n \u5b66\u7fd2\u7d50\u679c\u306e\u30c6\u30b9\u30c8\u306e\u3068\u304d\u306b\u3001\u8cc7\u6e90\u4e0d\u8db3\u306e\u30a8\u30e9\u30fc\u304c\u3067\u307e\u3057\u305f\u3002\u304a\u305d\u3089\u304f GPU \u30e1\u30e2\u30ea 2G \u3067\u306f\u4e0d\u8db3\u306e\u3088\u3046\u3067\u3059\u3002<\/p>\n \u30a8\u30e9\u30fc \u4e00\u90e8\u629c\u7c8b ————————————- \u30c6\u30b9\u30c8\u95a2\u6570\uff08 predict \uff09\u3067\u306e\u30a8\u30e9\u30fc\u3060\u3063\u305f\u306e\u3067\u3001\u3068\u308a\u3042\u3048\u305a\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u6570\u3092\u6e1b\u3089\u3057\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n ————————————————— ## \u30c6\u30b9\u30c8 \u30c6\u30b9\u30c8\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3001\uff12\u6642\u9593\u4ee5\u4e0a\u306e\u51e6\u7406\u304c\uff17\u5206\u7a0b\u5ea6\u306b\u6539\u5584\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n \u5b66\u7fd2\u6642\u9593——————– \u3053\u306e\u5834\u5408\u306f\u3001\u30c6\u30b9\u30c8\u6642\u70b9\u3067\u306e\u8cc7\u6e90\u4e0d\u8db3\u30a8\u30e9\u30fc\u306a\u306e\u3067\u3001\u5206\u5272\u3057\u3066\u547c\u3073\u51fa\u3057\u3066\u5f8c\u304b\u3089\u307e\u3068\u3081\u3066\u96c6\u8a08\u3059\u308c\u3070\u826f\u3044\u3068\u601d\u3044\u307e\u3059\u3002 \u5b66\u7fd2\u6642\u9593—————————– \u3053\u3061\u3089\u3067\u306f\u3001\u8cc7\u6e90\u4e0d\u8db3\u306e\u30a8\u30e9\u30fc\u306f\u51fa\u3066\u307e\u305b\u3093\u3002 —- \u30c6\u30b9\u30c8\u7d50\u679c\u306f\u4ee5\u4e0a\u3067\u3059\u3002<\/p>\n \u4eca\u56de\u306f\u3001\u300c\u304a\u8a66\u3057\u300d\u3067\u4f4e\u4fa1\u683c\u306e\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u3092\u9078\u3073\u307e\u3057\u305f\u304c\u3001 (S.Onda 2018\/5\/1)<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":"CPU \u306e\u307f\u3067\u306e\u5b66\u7fd2\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308b\u306e\u3067\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u3092\u8cfc\u5165\u3057\u3066 Tensorflow GPU\u7248\u3092\u8a66\u3057\u3066\u307f\u307e\u3057\u305f\u3002 NVIDIA\u793e \u306e CUDA \u3068 cuDNN \u306f\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u306e GPU\uff08Graphics …","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-511","post","type-post","status-publish","format-standard","hentry","category-ai_ecobio"],"_links":{"self":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=511"}],"version-history":[{"count":12,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/511\/revisions"}],"predecessor-version":[{"id":1488,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/511\/revisions\/1488"}],"wp:attachment":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n(2) CUDA + cuDNN \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\nUbuntu 16.04 \uff08Anaconda Python\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6e08\u307f\uff09\u306b CUDA \u3068 cuDNN \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3002
\nCUDA version 9.1 \u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u30a8\u30e9\u30fc\u3068\u306a\u3063\u3066\u3057\u307e\u3063\u305f\u306e\u3067\u3001CUDA 8.0\u3068cuDNN 6.0\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u305f\u3002
\n\u2460 CUDA \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\n\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9URL\uff08\u30a2\u30fc\u30ab\u30a4\u30d6\uff09
\nhttps:\/\/developer.nvidia.com\/cuda-toolkit-archive<\/a>
\nhttps:\/\/developer.nvidia.com\/cuda-80-ga2-download-archive<\/a><\/p>\n
\n> sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
\n> sudo apt-get update
\n> sudo apt-get install cuda-8-0
\n\u2461 cuDNN \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\ncuDNN \u30c0\u30a6\u30f3\u30ed\u30fc\u30c9
\nhttps:\/\/developer.nvidia.com\/rdp\/cudnn-download<\/a>
\n\uff08\u767b\u9332\u304c\u5fc5\u8981\u3067\u3059\uff09<\/p>\n
\ncuDNN v6.0 Developer Library for Ubuntu16.04 (Deb)
\ncuDNN v6.0 Code Samples and User Guide for Ubuntu16.04 (Deb)<\/p>\n
\n> sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb
\n> sudo dpkg -i libcudnn6-dev_6.0.21-1+cuda8.0_amd64.deb
\n> sudo dpkg -i libcudnn6-doc_6.0.21-1+cuda8.0_amd64.deb<\/p>\n
\nexport PATH=”\/usr\/local\/cuda\/bin:$PATH”
\nexport LD_LIBRARY_PATH=”\/usr\/local\/cuda\/lib64:$LD_LIBRARY_PATH”
\n(3)TensorFlow, TfLearn \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb
\nTensorFlow\u306f\u3001GPU\u7248 version 1.3.0 \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/p>\n
\n> pip install tflearn<\/p>\n
\n\u2460 MNIST CNN\uff12\u5c64 \u3067\u30c6\u30b9\u30c8
\n\u300c\u6df1\u5c64\u5b66\u7fd2 TensorFlow \u3092 MNIST \u3067\u8a66\u3059(2)\u300d<\/a>\u306e CNN \u4e2d\u9593\u5c64\uff12\u5c64\u3067\u30c6\u30b9\u30c8\u3057\u305f\u7d50\u679c\u3067\u3059\u3002<\/p>\n
\nstart:2018-04-30 20:59:00.928822
\nend:2018-04-30 21:05:15.549459
\n\u30c6\u30b9\u30c8\u5b9f\u884c\u6642\u9593
\nstart2018-04-30 21:05:15.667921
\nend2018-04-30 21:05:17.031135
\n\u7cbe\u5ea6
\n0.9927
\n——————————<\/p>\n
\nstart:2018-04-30 21:11:07.972981
\nend:2018-04-30 21:17:16.844291
\n\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u30c6\u30b9\u30c8\u7d50\u679c——————————
\nstart:2018-04-30 21:26:32.257176
\nend:2018-04-30 21:26:45.378530
\n[\u00a0 0\u00a0\u00a0 1\u00a0\u00a0 2 … 114\u00a0 44\u00a0 31]
\n[\u00a0 0\u00a0\u00a0 1\u00a0\u00a0 2 … 114\u00a0 68\u00a0 31]
\n\u7cbe\u5ea6
\n0.9249
\n——————————<\/p>\n
\n(2) NVIDIA GEFORCE GT1030 \u30e1\u30e2\u30ea 2GB<\/p>\n
\nResourceExhaustedError\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Traceback (most recent call last)
\n<ipython-input-21-4f8e54c2bd97> in <module>()
\n50 ## \u30c6\u30b9\u30c8
\n51 time3 = dt.datetime.now();
\n—> 52 result = model.predict(testX)
\n53 time4 = dt.datetime.now();
\n54
\n—————————————————<\/p>\n
\n## \u3068\u308a\u3042\u3048\u305a\u4e00\u90e8\u3092\u62bd\u51fa
\ntestX2 = cp.deepcopy(testX[0:4000])
\ntestY2 = cp.deepcopy(testY[0:4000])<\/p>\n
\ntime3 = dt.datetime.now();
\nresult = model.predict(testX2)
\ntime4 = dt.datetime.now();
\n—————————————————<\/p>\n
\nstart:2018-04-25 21:56:22.291248
\nend:2018-04-25 22:03:54.374067
\n—————————————————<\/p>\n
\n\u4eca\u56de\u306f\u30012GB \u3067\u30e1\u30e2\u30ea\u4e0d\u8db3\u304c\u51fa\u307e\u3057\u305f\u304c\u3001\u4eca\u5f8c\u3001\u8907\u96d1\u306a\u30cd\u30c3\u30c8\u69cb\u6210\u3067\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u9001\u3063\u305f\u5834\u5408\u306f\u30014GB \u306e GPU \u3067\u3082\u51fa\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002
\nWeb\u691c\u7d22\u3059\u308b\u3068 ResourceExhaustedError \u306b\u3064\u3044\u3066\u306e\u56de\u907f\u65b9\u6cd5\u304c\u51fa\u3066\u304d\u307e\u3059\u306e\u3067\u3001\u4eca\u5f8c\u306e\u53c2\u8003\u306b\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002
\n\u2461 \u300cAI\u8ab2\u984c1\uff1aDNA\u76f8\u540c\u6027\u691c\u7d22\u3068AI(2) 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e\u300d<\/span><\/a> \u306e\u300c\u5168\u7d50\u5408\u578b\uff11\u5c64\u3001\u30ce\u30fc\u30c9\u6570\u306f\u30011024\u5869\u57fa\u00d716(4\u5869\u57fa\u00d74\u5869\u57fa)=16834\u300d\u3067\u30c6\u30b9\u30c8<\/p>\n
\nstart:2018-04-25 21:11:09.902056
\nend:2018-04-25 21:18:14.969081
\nsaving:.\/data\/dna\/model_full.ckpt
\nINFO:tensorflow:\/home\/on\/anaconda3\/envs\/tensorflow\/data\/dna\/model_full.ckpt is not in all_model_checkpoint_paths. Manually adding it.
\nfinished.
\n\u30c6\u30b9\u30c8\u5b9f\u884c\u6642\u9593———————-
\nstart:2018-04-25 21:22:19.040007
\nend:2018-04-25 21:22:20.201880
\n[\u00a0 0\u00a0\u00a0 1\u00a0\u00a0 2 … 114\u00a0 68\u00a0 31]
\n[\u00a0 0\u00a0\u00a0 1\u00a0\u00a0 2 … 114\u00a0 68\u00a0 31]
\n\u7cbe\u5ea6
\n0.9372
\n————————————<\/p>\n
\n\uff12\u6642\u9593\u534a\u7a0b\u5ea6\uff0d\uff1e\uff17\u5206\u7a0b\u5ea6\u306b\u6539\u5584\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
\n\u672c\u683c\u7684\u306b Tensorflow \u3067\u958b\u767a\u3059\u308b\u306a\u3089\u3001\u5b66\u7fd2\u74b0\u5883\u306b\u3064\u3044\u3066\u306f\u3088\u308a\u30b9\u30da\u30c3\u30af\u306e\u9ad8\u3044\u306e\u3082\u304c\u5fc5\u8981\u3068\u306a\u308b\u3068\u601d\u3044\u307e\u3059\u3002
\n\u307e\u305f\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u306f\u6d88\u8cbb\u96fb\u529b\u304c\u5927\u304d\u3044\u306e\u3067\u3001\u96fb\u6e90\u306e\u4f9b\u7d66\u80fd\u529b\u306b\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n