{"id":3546,"date":"2020-08-05T10:00:34","date_gmt":"2020-08-05T01:00:34","guid":{"rendered":"https:\/\/www.kagoya.jp\/howto\/?p=3546"},"modified":"2023-07-18T17:46:42","modified_gmt":"2023-07-18T08:46:42","slug":"gpu-container2","status":"publish","type":"post","link":"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container2\/","title":{"rendered":"TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011"},"content":{"rendered":"<div class=\"easy-series-toc\">  <table class=\"easy-series-toc-table\">    <thead>      <tr>        <th>\u3010\u9023\u8f09\u4f01\u753b\u3011GPU\u30b3\u30f3\u30c6\u30ca\u6d3b\u7528 \u3010\u51686\u56de\u3011<\/th>      <\/tr>    <\/thead>    <tbody>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container1\/\">GPU\u30b3\u30f3\u30c6\u30ca\u3068\u306f\u4f55\u304b\uff1f\u4f55\u304c\u4fbf\u5229\u306a\u306e\u304b\uff1f\u3010\u7b2c1\u56de\uff1aGPU\u30b3\u30f3\u30c6\u30ca\u3067\u901f\u653b\u74b0\u5883\u69cb\u7bc9\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td>TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container3\/\">TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2461\u3010\u7b2c3\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u5b9f\u8df5\u7de8\u301c\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container4\/\">Chainer\u3092\u4f7f\u3063\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3010\u7b2c4\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u6a5f\u68b0\u5b66\u7fd2\u3059\u308b\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container5\/\">PyTorch\u3067\u6a5f\u68b0\u5b66\u7fd2\u3010\u7b2c5\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u30c6\u30f3\u30bd\u30eb\u306e\u57fa\u672c\u3092\u7406\u89e3\u3059\u308b\u3011<\/a>        <\/td>      <\/tr>    <\/tbody>  <\/table><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" width=\"620\" height=\"420\" src=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202006a01.png\" alt=\"TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011\" class=\"wp-image-3543\" srcset=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202006a01.png 620w, https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202006a01-300x203.png 300w\" sizes=\"(max-width: 620px) 100vw, 620px\" \/><\/figure>\n<\/div>\n\n\n<p>AI\u521d\u5b66\u8005\u306b\u3068\u3063\u3066\u30cf\u30fc\u30c9\u30eb\u306e\u3072\u3068\u3064\u306b\u306a\u3063\u3066\u3044\u308bGPU\u74b0\u5883\u69cb\u7bc9\u3092\u3067\u304d\u308b\u3060\u3051\u52b9\u7387\u7684\u306b\u884c\u3046\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u6709\u52b9\u6d3b\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u9023\u8f09\u3092\u30b9\u30bf\u30fc\u30c8\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3068\u3057\u3066\u30e1\u30b8\u30e3\u30fc\u306aTensorFlow\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u3053\u306e\u9023\u8f09\u306b\u3064\u3044\u3066<\/h2>\n\n\n\n<p>\u672c\u9023\u8f09\u306f\u3001\u51686\u56de\u306e\u30b7\u30ea\u30fc\u30ba\u3092\u901a\u3057\u3066\u3067\u304d\u308b\u3060\u3051\u52b9\u7387\u7684\u306b\u3001GPU\u306e\u74b0\u5883\u69cb\u7bc9\u3092\u884c\u3046\u305f\u3081\u306b\u30b3\u30f3\u30c6\u30ca\u306e\u6d3b\u7528\u3092\u884c\u3063\u3066\u3044\u304d\u307e\u3059\u3002\u300c\u6a5f\u68b0\u5b66\u7fd2\u3084\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092GPU\u3067\u5b9f\u884c\u3057\u3066\u307f\u305f\u3044\u3051\u3069\u96e3\u3057\u305d\u3046\u2026\u300d\u306a\u3069\u5c0e\u5165\u306b\u30cf\u30fc\u30c9\u30eb\u3092\u611f\u3058\u3089\u308c\u3066\u3044\u308b\u65b9\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u74b0\u5883\u69cb\u7bc9\u306b\u8981\u3059\u308b\u5de5\u6570\u3092\u5727\u5012\u7684\u306b\u524a\u6e1b\u3057\u3001\u5373\u5ea7\u306b\u8ab2\u984c\u306b\u53d6\u308a\u7d44\u3080\u3053\u3068\u304c\u3067\u304d\u308b\u30e1\u30ea\u30c3\u30c8\u3092\u611f\u3058\u3066\u3044\u305f\u3060\u304d\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u306b\u5fc5\u8981\u306a\u77e5\u8b58\u3084\u64cd\u4f5c\u65b9\u6cd5\u3092\u3001\u5f53\u793e\u306eGPU\u30b5\u30fc\u30d0\u30fc\u3092\u4f7f\u3044\u89e3\u8aac\u3057\u3066\u3044\u304d\u307e\u3059\u3002<br>\u9023\u8f09\u3092\u8aad\u307f\u7d42\u3048\u308b\u3053\u308d\u306b\u306f\u3001TensorFlow\u3084PyTorch\u306a\u3069\u306e\u30e1\u30b8\u30e3\u30fc\u306a\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3092\u4f7f\u3063\u305f\u6f14\u7fd2\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" width=\"620\" height=\"405\" src=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202007a01.jpg\" alt=\"\u753b\u50cf\u89e3\u6790\" class=\"wp-image-3545\" srcset=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202007a01.jpg 620w, https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/gpu202007a01-300x196.jpg 300w\" sizes=\"(max-width: 620px) 100vw, 620px\" \/><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\">\u672c\u9023\u8f09\u306f\u3001\u3053\u3061\u3089\u306e\u624b\u9806\u3067\u9032\u3081\u3066\u3044\u307e\u3059<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbGPU\u30b3\u30f3\u30c6\u30ca\u3068\u306f\u4f55\u304b\uff1f\u4f55\u304c\u4fbf\u5229\u306a\u306e\u304b\uff1f\uff08\u7b2c1\u56de\uff09<\/h3>\n\n\n\n<p>AI\u521d\u5b66\u8005\u304cGPU\u3092\u4f7f\u3063\u3066\u6a5f\u68b0\u5b66\u7fd2\u3084\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u53d6\u308a\u7d44\u307f\u305f\u3044\u5834\u5408\u3001\u74b0\u5883\u69cb\u7bc9\u306b\u60f3\u50cf\u4ee5\u4e0a\u306e\u5de5\u6570\u304c\u767a\u751f\u3059\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u4f5c\u696d\u306b\u8981\u3059\u308b\u6642\u9593\u3092\u6975\u529b\u524a\u6e1b\u3059\u308b\u305f\u3081\u306b\u30b3\u30f3\u30c6\u30ca\u6280\u8853\u3092\u9069\u7528\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u5185\u304b\u3089GPU\u3092\u5229\u7528\u3059\u308b\u305f\u3081\u306e\u6e96\u5099\u3068\u624b\u9806\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u25cfGPU\u30b3\u30f3\u30c6\u30ca\u3068\u306f\u4f55\u304b\uff1f\u4f55\u304c\u4fbf\u5229\u306a\u306e\u304b\uff1f\uff08\u7b2c1\u56de\uff09<br><a style=\"word-break: break-all;\" href=\"\/howto\/cloud\/gpu-container1\/\">https:\/\/www.kagoya.jp\/howto\/cloud\/gpu-container1\/<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbTensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\uff08\u7b2c2\u56de\uff09\u4eca\u56de\u306e\u8a18\u4e8b<\/h3>\n\n\n\n<p>OSS\uff08\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30bd\u30d5\u30c8\u30a6\u30a8\u30a2\uff09\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4e2d\u304b\u3089<mark>TensorFlow\uff08\u30c6\u30f3\u30bd\u30eb\u30d5\u30ed\u30fc\uff09\u3001Keras\uff08\u30b1\u30e9\u30b9\uff09<\/mark>\u3092\u53d6\u308a\u4e0a\u3052\u3001\u3053\u308c\u3089\u304c\u7a3c\u50cd\u3059\u308b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u5185\u304b\u3089GPU\u3092\u6307\u5b9a\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002TensorFlow\u306fKeras\u3092\u53d6\u308a\u8fbc\u3080\u5f62\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u3066\u3001\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u3059\u308b\u969b\u306e\u4f7f\u3044\u52dd\u624b\u306e\u826f\u3055\u304b\u3089\u3001\u591a\u304f\u306e\u30e6\u30fc\u30b6\u30fc\u306b\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u56de\u3067\u306f\u30b3\u30f3\u30c6\u30ca\u5185\u306eTensorFlow\u3067GPU\u3092\u5229\u7528\u3067\u304d\u308b\u72b6\u614b\u307e\u3067\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbTensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2461\uff08\u7b2c3\u56de\uff09<\/h3>\n\n\n\n<p><mark>TensorFlow\uff08\u30c6\u30f3\u30bd\u30eb\u30d5\u30ed\u30fc\uff09\u3084Theano\uff08\u30c6\u30a2\u30ce\uff09\uff0fCNTK\uff08Cognitive Toolkit\uff09<\/mark>\u306e\u8907\u6570\u306e\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u3068\u3057\u3066\u5229\u7528\u53ef\u80fd\u306a<mark>Keras\uff08\u30b1\u30e9\u30b9\uff09<\/mark>\u3092\u53d6\u308a\u4e0a\u3052\u3001TensorFlow\u3068Keras\u3092\u4f7f\u3063\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbChainer\u3092\u4f7f\u3063\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\uff08\u7b2c4\u56de\uff09<\/h3>\n\n\n\n<p>\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3068\u3057\u3066\u6709\u540d\u306a<mark>Chainer\uff08\u30c1\u30a7\u30a4\u30ca\u30fc\uff09<\/mark>\u306e\u5229\u7528\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u30b3\u30f3\u30c6\u30ca\u304b\u3089GPU\u3092\u5229\u7528\u3059\u308b\u624b\u9806\u3068\u3001GPU\u306b\u3088\u3063\u3066\u3069\u308c\u3060\u3051\u9ad8\u901f\u5316\u306b\u5bc4\u4e0e\u3067\u304d\u305f\u304b\u306b\u3064\u3044\u3066\u3001Python\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u5b9f\u884c\u7d50\u679c\u3092\u901a\u3057\u3066\u78ba\u8a8d\u3057\u307e\u3059\u3002C\u8a00\u8a9e\u306b\u6bd4\u3079\u3066\u51e6\u7406\u6642\u9593\u304c\u304b\u304b\u308b\u3068\u8a00\u308f\u308c\u3066\u3044\u308bPython\u3067\u3059\u304c\u3001\u6570\u5024\u8a08\u7b97\u3092\u52b9\u7387\u7684\u306b\u884c\u3046\u305f\u3081\u306e\u62e1\u5f35\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u3042\u308b<mark>NumPy\uff08\u30ca\u30e0\u30d1\u30a4\uff09<\/mark>\u306b\u3064\u3044\u3066\u3082\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbPytorch\u3067\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08\u7b2c5\u56de\uff09<\/h3>\n\n\n\n<p>Python\u306e\u6a5f\u68b0\u5b66\u7fd2\u7528\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3067\u3042\u308b<mark>Pytorch\uff08\u30d1\u30a4\u30c8\u30fc\u30c1\uff09<\/mark>\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002PyTorch\u3067\u306f<mark>Tensor\uff08\u30c6\u30f3\u30bd\u30eb\uff09<\/mark>\u3068\u3044\u3046\u578b\u3067\u884c\u5217\u3092\u8868\u73fe\u3057\u307e\u3059\u3002Tensor\u306f\u591a\u6b21\u5143\u914d\u5217\u3092\u6271\u3046\u305f\u3081\u306e\u30c7\u30fc\u30bf\u69cb\u9020\u3067\u3042\u308a\u3001GPU\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u3044\u308b\u3053\u3068\u304b\u3089\u3001Pytorch\u304c\u7a3c\u50cd\u3059\u308b\u30b3\u30f3\u30c6\u30ca\u3092\u5229\u7528\u3057\u3001GPU\u306b\u3088\u308b\u9ad8\u901f\u51e6\u7406\u3092\u884c\u3046\u624b\u9806\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u30fbOpenPose\u306b\u3088\u308b\u95a2\u7bc0\u70b9\u62bd\u51fa\u30fb\u59ff\u52e2\u63a8\u5b9a\uff08\u7b2c6\u56de\uff09<\/h3>\n\n\n\n<p>\u30ab\u30e1\u30e9\u753b\u50cf\u306eAI\u753b\u50cf\u8a8d\u8b58\u3068\u8a00\u3048\u3070\u300c\u9854\u8a8d\u8a3c\u300d\u3092\u601d\u3044\u6d6e\u304b\u3079\u308b\u4eba\u304c\u591a\u3044\u3068\u601d\u3044\u307e\u3059\u304c\u3001\u6700\u8fd1\u306f\u4e00\u6b69\u9032\u307f\u3001\u4eba\u304c\u6620\u3063\u305f\u9759\u6b62\u753b\u3084\u52d5\u753b\u304b\u3089\u95a2\u7bc0\u70b9\u62bd\u51fa\u30fb\u59ff\u52e2\u63a8\u5b9a\u306b\u53d6\u308a\u7d44\u3080\u30b1\u30fc\u30b9\u304c\u5897\u3048\u3066\u3044\u307e\u3059\u3002\u4eba\u4f53\u3001\u9854\u3001\u624b\u8db3\u306a\u3069\u306e\u30ad\u30fc\u30dd\u30a4\u30f3\u30c8\u3092\u753b\u50cf\u304b\u3089\u691c\u51fa\u3059\u308b\u6280\u8853\u304c\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u3088\u308a\u3001\u5b9f\u7528\u30ec\u30d9\u30eb\u307e\u3067\u5411\u4e0a\u3057\u3066\u3044\u308b\u304b\u3089\u3067\u3059\u3002\u3053\u306e\u56de\u3067\u306fOpenPose\uff08\u30aa\u30fc\u30d7\u30f3\u30dd\u30fc\u30ba\uff09\u3068\u3044\u3046\u30e9\u30a4\u30d6\u30e9\u30ea\u3092GPU\u4e0a\u3067\u52d5\u304b\u3059\u30b3\u30f3\u30c6\u30ca\u3092\u4f7f\u3044\u3001\u52d5\u753b\u30d5\u30a1\u30a4\u30eb\u306e\u95a2\u7bc0\u70b9\u62bd\u51fa\u624b\u9806\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u524d\u56de(\u7b2c1\u56de)\u306e\u632f\u308a\u8fd4\u308a<\/h2>\n\n\n\n<p>\u524d\u56de\u306f\u3001Docker\u3092\u4f7f\u3063\u3066GPU\u3092\u30b3\u30f3\u30c6\u30ca\u5316\u3059\u308b\u4f5c\u696d\u3092\u884c\u3044\u3001\u4e0b\u8a18\u306e\u74b0\u5883\u3092\u69cb\u7bc9\u3057\u307e\u3057\u305f\u3002<br>\u3010\u74b0\u5883\u3011<br>OS\uff1aUbuntu 16.04 LTS<br>GPU\uff1aNVIDIA Tesla P40<br>CUDA\u3000version\uff1a10.2<br>Docker version\uff1a19.03.2<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u4e0a\u8a18\u306e\u74b0\u5883\u306b\u3064\u3044\u3066\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/h3>\n\n\n\n<p>\u4e0a\u8a18\u306e\u74b0\u5883\u306b\u3064\u3044\u3066\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">\u30fbpci\u30c7\u30d0\u30a4\u30b9\u306e\u78ba\u8a8d<\/h4>\n\n\n\n<p>lspci\u30b3\u30de\u30f3\u30c9\u3067pci\u30c7\u30d0\u30a4\u30b9\u306e\u60c5\u5831\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001grep\u3067nvidia\u306e\u6587\u5b57\u5217\u306b\u8a72\u5f53\u3059\u308b\u3082\u306e\u3092\u691c\u7d22\u3059\u308b\u3053\u3068\u3067\u8868\u793a\u3059\u308b\u60c5\u5831\u3092\u7d5e\u308a\u8fbc\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ lspci -v | grep -i nvidia\n03:00.0 3D controller: NVIDIA Corporation GP102GL &#x5B;Tesla P40] (rev a1)\nSubsystem: NVIDIA Corporation Device 11d9\nKernel driver in use: nvidia\nKernel modules: nvidiafb, nouveau, nvidia_440, nvidia_440_drm\n<\/pre><\/div>\n\n\n<p>Tesla P40\u3092\u5b9f\u88c5\u3057\u305f\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u30dc\u30fc\u30c9\u306b\u95a2\u3059\u308b\u60c5\u5831\u304c\u8868\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">\u30fbNVIDIA Container Toolkit\u306e\u78ba\u8a8d<\/h4>\n\n\n\n<p>nvidia-container-cli info \u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u3063\u3066 NVIDIA Container Toolkit \u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ nvidia-container-cli info\nNVRM version: 440.64.00\nCUDA version: 10.2\n\nDevice Index: 0\nDevice Minor: 0\nModel: Tesla P40\nBrand: Tesla\nGPU UUID: GPU-ccffd358-4ab2-0dab-10cf-78df33604be1\nBus Location: 00000000:03:00.0\nArchitecture: 6.1\n<\/pre><\/div>\n\n\n<p>CUDA\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3084GPU\u306e\u30e2\u30c7\u30eb\u60c5\u5831\u304c\u8868\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">\u30fbDocker\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306e\u78ba\u8a8d<\/h4>\n\n\n\n<p>docker version\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3001\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ docker version\nClient: Docker Engine - Community\nVersion: 19.03.2\nAPI version: 1.40\nGo version: go1.12.8\nGit commit: 6a30dfc\nBuilt: Thu Aug 29 05:28:19 2019\nOS\/Arch: linux\/amd64\nExperimental: false\n\nServer: Docker Engine - Community\nEngine:&amp;lt;br \/&gt; Version: 19.03.7\nAPI version: 1.40 (minimum version 1.12)\nGo version: go1.12.17\nGit commit: 7141c199a2\nBuilt: Wed Mar 4 01:21:22 2020\nOS\/Arch: linux\/amd64\nExperimental: false\ncontainerd:\nVersion: 1.2.6\nGitCommit: 894b81a4b802e4eb2a91d1ce216b8817763c29fb\nrunc:\nVersion: 1.0.0-rc8\nGitCommit: 425e105d5a03fabd737a126ad93d62a9eeede87f\ndocker-init:\nVersion: 0.18.0\nGitCommit: fec3683\n<\/pre><\/div>\n\n\n<p>Docker \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3001\u30b5\u30fc\u30d0\u30fc\u306e\u5404\u3005\u306b\u3064\u3044\u3066Docker\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3001\u30d3\u30eb\u30c9\u3055\u308c\u305f\u65e5\u6642\u3001OS\u3084\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u306b\u95a2\u9023\u3059\u308b\u60c5\u5831\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">\u30fbDocker\u30b3\u30f3\u30c6\u30ca\u304b\u3089GPU\u306e\u78ba\u8a8d<\/h4>\n\n\n\n<p>Docker \u30b3\u30f3\u30c6\u30ca\u5185\u3067nvidia-smi\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3001GPU\u3092\u8a8d\u8b58\u3057\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ docker run --gpus all nvidia\/cuda nvidia-smi\nFri Jul 17 01:10:29 2020\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage\/Cap| Memory-Usage | GPU-Util Compute M. |\n|===============================+======================+======================|\n| 0 Tesla P40 Off | 00000000:03:00.0 Off | 0 |\n| N\/A 27C P8 9W \/ 250W | 10MiB \/ 22919MiB | 0% Default |\n+-------------------------------+----------------------+----------------------+\n\n+-----------------------------------------------------------------------------+\n| Processes: GPU Memory |\n| GPU PID Type Process name Usage |\n|=============================================================================|\n| No running processes found |\n+-----------------------------------------------------------------------------+\n<\/pre><\/div>\n\n\n<p>Docker \u30b3\u30f3\u30c6\u30ca\u5185\u304b\u3089GPU(Tesla P40)\u3092\u8a8d\u8b58\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u30b3\u30f3\u30c6\u30ca\u304b\u3089TensorFlow\u3092\u7a3c\u50cd\u3055\u305b\u308b<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">GPU\u306b\u5bfe\u5fdc\u3057\u3066\u3044\u308bTensorFlow\u306eDocker\u30a4\u30e1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/h3>\n\n\n\n<p>Docker Hub \u30ea\u30dd\u30b8\u30c8\u30ea\u306b\u3042\u308bTensorFlow \u306e\u516c\u5f0f\u306e Docker \u30a4\u30e1\u30fc\u30b8(GPU\u5bfe\u5fdc)\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3001\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ docker run --gpus all -it --rm tensorflow\/tensorflow:latest-gpu \\\npython -c &quot;import tensorflow as tf; print(tf.reduce_sum(tf.random.normal(&#x5B;1000, 1000])))&quot;\n2020-07-17 02:13:12.860229: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2020-07-17 02:13:12.864310: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1561] Found device 0 with properties:\npciBusID: 0000:03:00.0 name: Tesla P40 computeCapability: 6.1\ncoreClock: 1.531GHz coreCount: 30 deviceMemorySize: 22.38GiB deviceMemoryBandwidth: 323.21GiB\/s\n2020-07-17 02:13:12.864490: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-07-17 02:13:12.866178: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-07-17 02:13:12.867793: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-07-17 02:13:12.868044: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-07-17 02:13:12.869794: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-07-17 02:13:12.870677: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-07-17 02:13:12.874366: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-07-17 02:13:12.876594: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-07-17 02:13:12.876903: I tensorflow\/core\/platform\/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n2020-07-17 02:13:12.890118: I tensorflow\/core\/platform\/profile_utils\/cpu_utils.cc:102] CPU Frequency: 3499645000 Hz\n2020-07-17 02:13:12.891104: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x7fed98000b20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2020-07-17 02:13:12.891135: I tensorflow\/compiler\/xla\/service\/service.cc:176] StreamExecutor device (0): Host, Default Version\n2020-07-17 02:13:13.012255: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x477cfc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n2020-07-17 02:13:13.012312: I tensorflow\/compiler\/xla\/service\/service.cc:176] StreamExecutor device (0): Tesla P40, Compute Capability 6.1\n2020-07-17 02:13:13.015060: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1561] Found device 0 with properties:\npciBusID: 0000:03:00.0 name: Tesla P40 computeCapability: 6.1\ncoreClock: 1.531GHz coreCount: 30 deviceMemorySize: 22.38GiB deviceMemoryBandwidth: 323.21GiB\/s\n2020-07-17 02:13:13.015133: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-07-17 02:13:13.015159: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-07-17 02:13:13.015182: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-07-17 02:13:13.015204: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-07-17 02:13:13.015241: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-07-17 02:13:13.015281: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-07-17 02:13:13.015315: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-07-17 02:13:13.019195: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-07-17 02:13:13.019243: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-07-17 02:13:13.022311: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n2020-07-17 02:13:13.022340: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1108] 0\n2020-07-17 02:13:13.022354: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1121] 0: N\n2020-07-17 02:13:13.027246: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1247] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 21397 MB memory) -&gt; physical GPU (device: 0, name: Tesla P40, pci bus id: 0000:03:00.0, compute capability: 6.1)\ntf.Tensor(1241.9949, shape=(), dtype=float32)\n<\/pre><\/div>\n\n\n<p>\u78ba\u8a8d\u306e\u305f\u3081\u3001REPOSITORY\u304ctensorflow\/tensorflow\u306e\u30b3\u30f3\u30c6\u30ca\u30a4\u30e1\u30fc\u30b8\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ docker images tensorflow\/tensorflow\nREPOSITORY              TAG                      IMAGE ID            CREATED             SIZE\ntensorflow\/tensorflow   latest-gpu               f5ba7a196d56        2 months ago        3.84GB\n\n<\/pre><\/div>\n\n\n<p>\u30b3\u30f3\u30c6\u30ca\u30a4\u30e1\u30fc\u30b8\u304c\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<p>\u6700\u65b0\u306e TensorFlow GPU \u30a4\u30e1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b3\u30f3\u30c6\u30ca\u5185\u3067 bash \u30b7\u30a7\u30eb \u30bb\u30c3\u30b7\u30e7\u30f3\u3092\u958b\u59cb\u3057\u307e\u3059\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n$ docker run --gpus all -it tensorflow\/tensorflow:latest-gpu bash\n\n________ _______________\n___ __\/__________________________________ ____\/__ \/________ __\n__ \/ _ _ \\_ __ \\_ ___\/ __ \\_ ___\/_ \/_ __ \/_ __ \\_ | \/| \/ \/\n_ \/ \/ __\/ \/ \/ \/(__ )\/ \/_\/ \/ \/ _ __\/ _ \/ \/ \/_\/ \/_ |\/ |\/ \/\n\/_\/ \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/ \/_\/ \/_\/ \\____\/____\/|__\/\n\nWARNING: You are running this container as root, which can cause new files in\nmounted volumes to be created as the root user on your host machine.\n\nTo avoid this, run the container by specifying your user&#039;s userid:\n\n$ docker run -u $(id -u):$(id -g) args...\n\nroot@0a1280997aba:\/#\n<\/pre><\/div>\n\n\n<p>python\u304b\u3089GPU\u3092\u78ba\u8a8d\u3057\u307e\u3059<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nroot@2ec6a2a6b71b:\/# python\nPython 3.6.9 (default, Apr 18 2020, 01:56:04)\n&#x5B;GCC 8.4.0] on linux\nType &quot;help&quot;, &quot;copyright&quot;, &quot;credits&quot; or &quot;license&quot; for more information.\n&gt;&gt;&gt; import tensorflow as tf\n&gt;&gt;&gt; tf.test.gpu_device_name()\n<\/pre><\/div>\n\n\n<p>\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u793a\u3055\u308c\u3001\u6700\u5f8c\u306bdevice:GPU:0\u3068\u8868\u793a\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n2020-07-17 04:40:26.355144: I tensorflow\/core\/platform\/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n2020-07-17 04:40:26.369373: I tensorflow\/core\/platform\/profile_utils\/cpu_utils.cc:102] CPU Frequency: 3499645000 Hz\n2020-07-17 04:40:26.370812: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x7f54e8000b20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2020-07-17 04:40:26.370847: I tensorflow\/compiler\/xla\/service\/service.cc:176] StreamExecutor device (0): Host, Default Version\n2020-07-17 04:40:26.375080: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2020-07-17 04:40:26.497987: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x5032be0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n2020-07-17 04:40:26.498049: I tensorflow\/compiler\/xla\/service\/service.cc:176] StreamExecutor device (0): Tesla P40, Compute Capability 6.1\n2020-07-17 04:40:26.500908: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1561] Found device 0 with properties:\npciBusID: 0000:03:00.0 name: Tesla P40 computeCapability: 6.1\ncoreClock: 1.531GHz coreCount: 30 deviceMemorySize: 22.38GiB deviceMemoryBandwidth: 323.21GiB\/s\n2020-07-17 04:40:26.501319: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-07-17 04:40:26.504781: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-07-17 04:40:26.507834: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-07-17 04:40:26.508346: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-07-17 04:40:26.511885: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-07-17 04:40:26.513896: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-07-17 04:40:26.521098: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-07-17 04:40:26.525612: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-07-17 04:40:26.525668: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-07-17 04:40:26.530543: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n2020-07-17 04:40:26.530574: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1108] 0\n2020-07-17 04:40:26.530585: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1121] 0: N\n2020-07-17 04:40:26.534811: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1247] Created TensorFlow device (\/device:GPU:0 with 21397 MB memory) -&gt; physical GPU (device: 0, name: Tesla P40, pci bus id: 0000:03:00.0, compute capability: 6.1)\n&#039;\/device:GPU:0&#039;\n<\/pre><\/div>\n\n\n<p>\u6700\u5f8c\u306edevice:GPU:0\u3068\u3044\u3046\u8868\u793a\u306fGPU\u304c\u6b63\u3057\u304f\u30a2\u30b5\u30a4\u30f3\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u304a\u308a\u3001\u30b3\u30f3\u30c6\u30ca\u306ePython\u30b7\u30a7\u30eb\u5185\u304b\u3089GPU\u3092\u5229\u7528\u3067\u304d\u308b\u72b6\u614b\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<br>\uff08TensorFlow\u304cGPU\u3092\u5229\u7528\u3067\u304d\u308b\u72b6\u614b\u3067\u3059\uff09<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u6b21\u56de\u4e88\u544a \u7b2c3\u56de\uff1aTensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2461\uff08\u7b2c3\u56de\uff09<\/h2>\n\n\n\n<p>TensorFlow\u306e\u30e9\u30c3\u30d1\u30fc\u3068\u3057\u3066\u89aa\u548c\u6027\u306e\u9ad8\u3044Keras\u3092\u4f7f\u3063\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u53d6\u308a\u7d44\u307f\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n\n\n<div class=\"kh-cta\"><div class=\"entry-content\">\n<div class=\"kh-box has-label is-style-yellow\"><div class=\"kh-box__label\">HPC\u30b5\u30fc\u30d3\u30b9 SX-Aurora TSUBASA \u30af\u30e9\u30a6\u30c9<\/div><div class=\"kh-box__content\">\n<p>NEC\u306e\u30b9\u30fc\u30d1\u30fc\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u300cSX-Aurora TSUBASA\u300d\u3092\u30af\u30e9\u30a6\u30c9\u74b0\u5883\u3067\u3054\u5229\u7528\u3067\u304d\u308b\u696d\u754c\u968f\u4e00\u306e\u30b5\u30fc\u30d3\u30b9\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u4e16\u754c\u30c8\u30c3\u30d7\u30af\u30e9\u30b9\u306e\u30b9\u30da\u30c3\u30af\u3067\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u306e\u9ad8\u901f\u51e6\u7406\u3092\u5b9f\u73fe\u3059\u308b\u30d9\u30af\u30c8\u30eb\u578b\u30b9\u30fc\u30d1\u30fc\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u3092\u3001\u6708\u984d\u5b9a\u984d\u6599\u91d1\u306e\u30af\u30e9\u30a6\u30c9\u30b5\u30fc\u30d3\u30b9\u3068\u3057\u3066\u5229\u7528\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full has-lightbox\"><a href=\"https:\/\/www.kagoya.jp\/cloud\/hpc\/?argument=vqHX23Xs&amp;dmai=a60c7897c68d93\" target=\"_blank\" rel=\"noreferrer noopener\"><img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/hpcbanner.jpg\" alt=\"\" class=\"wp-image-12397\" srcset=\"https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/hpcbanner.jpg 1200w, https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/hpcbanner-300x157.jpg 300w, https:\/\/www.kagoya.jp\/howto\/wp-content\/uploads\/hpcbanner-1024x536.jpg 1024w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/figure>\n<\/div><\/div>\n<\/div><\/div><div class=\"easy-series-toc\">  <table class=\"easy-series-toc-table\">    <thead>      <tr>        <th>\u3010\u9023\u8f09\u4f01\u753b\u3011GPU\u30b3\u30f3\u30c6\u30ca\u6d3b\u7528 \u3010\u51686\u56de\u3011<\/th>      <\/tr>    <\/thead>    <tbody>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container1\/\">GPU\u30b3\u30f3\u30c6\u30ca\u3068\u306f\u4f55\u304b\uff1f\u4f55\u304c\u4fbf\u5229\u306a\u306e\u304b\uff1f\u3010\u7b2c1\u56de\uff1aGPU\u30b3\u30f3\u30c6\u30ca\u3067\u901f\u653b\u74b0\u5883\u69cb\u7bc9\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td>TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container3\/\">TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2461\u3010\u7b2c3\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u5b9f\u8df5\u7de8\u301c\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container4\/\">Chainer\u3092\u4f7f\u3063\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3010\u7b2c4\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u6a5f\u68b0\u5b66\u7fd2\u3059\u308b\u3011<\/a>        <\/td>      <\/tr>      <tr>        <td><a href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container5\/\">PyTorch\u3067\u6a5f\u68b0\u5b66\u7fd2\u3010\u7b2c5\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u30c6\u30f3\u30bd\u30eb\u306e\u57fa\u672c\u3092\u7406\u89e3\u3059\u308b\u3011<\/a>        <\/td>      <\/tr>    <\/tbody>  <\/table><\/div>","protected":false},"excerpt":{"rendered":"\u3010\u9023\u8f09\u4f01\u753b\u3011GPU\u30b3\u30f3\u30c6\u30ca\u6d3b\u7528 \u3010\u51686\u56de\u3011 GPU\u30b3\u30f3\u30c6\u30ca\u3068\u306f\u4f55\u304b\uff1f\u4f55\u304c\u4fbf\u5229\u306a\u306e\u304b\uff1f\u3010\u7b2c1\u56de\uff1aGPU\u30b3\u30f3\u30c6\u30ca\u3067\u901f\u653b\u74b0\u5883\u69cb\u7bc9\u3011 TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011 TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2461\u3010\u7b2c3\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf [&hellip;]","protected":false},"author":10,"featured_media":3544,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[145],"tags":[34,56,67,100],"pickup":[],"sitedisplay":[],"sitetarget":[],"class_list":["post-3546","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hpc","tag-gpu","tag-56","tag-67","tag-hpc"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>TensorFlow\u3068Keras\u306b\u3088\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u2460\u3010\u7b2c2\u56de:GPU\u30b3\u30f3\u30c6\u30ca\u3067\u753b\u50cf\u89e3\u6790\u301c\u6e96\u5099\u7de8\u301c\u3011 - \u30ab\u30b4\u30e4\u306e\u30b5\u30fc\u30d0\u30fc\u7814\u7a76\u5ba4<\/title>\n<meta name=\"description\" content=\"AI\u521d\u5b66\u8005\u306b\u3068\u3063\u3066\u30cf\u30fc\u30c9\u30eb\u306e\u3072\u3068\u3064\u306b\u306a\u3063\u3066\u3044\u308bGPU\u74b0\u5883\u69cb\u7bc9\u3092\u3067\u304d\u308b\u3060\u3051\u52b9\u7387\u7684\u306b\u884c\u3046\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u6709\u52b9\u6d3b\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u9023\u8f09\u3092\u30b9\u30bf\u30fc\u30c8\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306f\u30c7\u30a3 AI\u521d\u5b66\u8005\u306b\u3068\u3063\u3066\u30cf\u30fc\u30c9\u30eb\u306e\u3072\u3068\u3064\u306b\u306a\u3063\u3066\u3044\u308bGPU\u74b0\u5883\u69cb\u7bc9\u3092\u3067\u304d\u308b\u3060\u3051\u52b9\u7387\u7684\u306b\u884c\u3046\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u6709\u52b9\u6d3b\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u9023\u8f09\u3092\u30b9\u30bf\u30fc\u30c8\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3068\u3057\u3066\u30e1\u30b8\u30e3\u30fc\u306aTensorFlow\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kagoya.jp\/howto\/engineer\/hpc\/gpu-container2\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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