{"id":46562,"date":"2023-02-05T04:14:35","date_gmt":"2023-10-06T04:38:38","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/46562-2\/"},"modified":"2024-04-29T13:10:57","modified_gmt":"2024-04-29T05:10:57","slug":"46562-2","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/46562-2\/","title":{"rendered":""},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h1>\n

Jupyter lab\u3084Jupyter notebook\u304c\u975e\u5e38\u306b\u4fbf\u5229\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308b\u3053\u3068\u306f\u8a00\u3046\u307e\u3067\u3082\u306a\u3044\u3053\u3068\u3067\u3059\u3002
\n\u3057\u304b\u3057\u3001\u843d\u3061\u3066\u3044\u308b\u30d6\u30ed\u30b0\u30e9\u30e0\u3092Jupyter\u3067\u7de8\u96c6\u3057\u3088\u3046\u3068\u3057\u3066\u3001<\/p>\n

\r\ndef<\/span> main<\/span>():<\/span>\r\n    parser<\/span> =<\/span> argparse<\/span>.<\/span>ArgumentParser<\/span>(<\/span>description<\/span>=<\/span>'ChainerMN example: MNIST'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--batchsize'<\/span>,<\/span> '-b'<\/span>,<\/span> type<\/span>=<\/span>int<\/span>,<\/span> default<\/span>=<\/span>100<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Number of images in each mini-batch'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--communicator'<\/span>,<\/span> type<\/span>=<\/span>str<\/span>,<\/span>\r\n                        default<\/span>=<\/span>'hierarchical'<\/span>,<\/span> help<\/span>=<\/span>'Type of communicator'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--epoch'<\/span>,<\/span> '-e'<\/span>,<\/span> type<\/span>=<\/span>int<\/span>,<\/span> default<\/span>=<\/span>20<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Number of sweeps over the dataset to train'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--gpu'<\/span>,<\/span> '-g'<\/span>,<\/span> action<\/span>=<\/span>'store_true'<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Use GPU'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--out'<\/span>,<\/span> '-o'<\/span>,<\/span> default<\/span>=<\/span>'result'<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Directory to output the result'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--resume'<\/span>,<\/span> '-r'<\/span>,<\/span> default<\/span>=<\/span>''<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Resume the training from snapshot'<\/span>)<\/span>\r\n    parser<\/span>.<\/span>add_argument<\/span>(<\/span>'--unit'<\/span>,<\/span> '-u'<\/span>,<\/span> type<\/span>=<\/span>int<\/span>,<\/span> default<\/span>=<\/span>1000<\/span>,<\/span>\r\n                        help<\/span>=<\/span>'Number of units'<\/span>)<\/span>\r\n    args<\/span> =<\/span> parser<\/span>.<\/span>parse_args<\/span>()<\/span>\r\n\r\n......<\/span>\r\n\r\n<\/code><\/pre>\n
usage: ipykernel_launcher.py [-h] [--batchsize BATCHSIZE]\r\n                             [--communicator COMMUNICATOR] [--epoch EPOCH]\r\n                             [--gpu] [--out OUT] [--resume RESUME]\r\n                             [--unit UNIT]\r\nipykernel_launcher.py: error: unrecognized arguments: -f \/run\/user\/1218\/jupyter\/kernel-662a3733-81e6-4d71-9647-27185d96086a.json\r\nAn exception has occurred, use %tb to see the full traceback.\r\n\r\nSystemExit: 2\r\n<\/code><\/pre>\n
\u3053\u306e\u69d8\u306a\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8\u306b\u51fa\u304f\u308f\u3057\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f<\/p>\n
\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u5f15\u6570\u306f\u3000jupyter \u3067\u306f\u5bfe\u5fdc\u3067\u304d\u306a\u3044\u3093\u3060\u3001\u306a\u3093\u3066\u601d\u3063\u3066\u3044\u307e\u305b\u3093\u304b\uff1f<\/p>\n
\u89e3\u6c7a\u7b56<\/h1>\n
\u3053\u308c\u3092\u3001<\/p>\n
args<\/span> =<\/span> parser<\/span>.<\/span>parse_args<\/span>()<\/span>\r\n<\/code><\/pre>\n
\u3053\u3046\u5909\u3048\u307e\u3059\u3002<\/p>\n
args<\/span> =<\/span> parser<\/span>.<\/span>parse_args<\/span>(<\/span>args<\/span>=<\/span>[])<\/span>\r\n<\/code><\/pre>\n
\u306a\u3093\u3068\u3053\u308c\u3060\u3051\u3067\u3001\u540c\u69d8\u306e\u30a8\u30e9\u30fc\u304c\u51fa\u306a\u304f\u306a\u308a\u3001
\n\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3067\u5b9f\u884c\u3057\u3066\u3044\u308b\u3088\u3046\u306bJupyter\u4e0a\u3067\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u3061\u306a\u307f\u306b\u4e0a\u306e\u72b6\u614b\u3067\u306f\u5168\u304f\u5f15\u6570\u3092\u6307\u5b9a\u3057\u3066\u306a\u3044\u72b6\u614b\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n
\u5f15\u6570\u306e\u6307\u5b9a\u304c\u3042\u308b\u5834\u5408\u306f<\/h2>\n
\u5f15\u6570\u306e\u6307\u5b9a\u304c\u3042\u308b\u969b\u306f\u3001arg=[ ]\u306e[ ]\u5185\u306b
\n\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u3067\u6253\u3061\u8fbc\u3080\u9806\u306b\u30ea\u30b9\u30c8\u306b\u5165\u308c\u307e\u3059\u3002<\/p>\n
\u4f8b\u3048\u3070\u4ee5\u4e0b\u306e\u69d8\u306b\u5f15\u6570\u3092\u6307\u5b9a\u30d7\u30ed\u30b0\u30e9\u30e0\u5b9f\u884c\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/p>\n
\r\n$ <\/span>python test.py --batchsize<\/span> 200 --epoch<\/span> 100 -g<\/span>\r\n<\/code><\/pre>\n
\u3053\u306e\u5b9f\u884c\u3068\u540c\u3058\u5f15\u6570\u6307\u5b9a\u3092\u3057\u3066\u3001jupyter\u4e0a\u3067\u5b9f\u884c\u3059\u308b\u5834\u5408\u306f\u3001\u8a72\u5f53\u90e8\u5206\u3092\u3001<\/p>\n
args<\/span> =<\/span> parser<\/span>.<\/span>parse_args<\/span>(<\/span>args<\/span>=<\/span>[<\/span>'--batchsize'<\/span>,<\/span>'200'<\/span>,<\/span>'--epoch'<\/span>,<\/span>'100'<\/span>,<\/span>'-g'<\/span>])<\/span>\r\n<\/code><\/pre>\n
\u3068\u3059\u308c\u3070\u3001\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u7686\u3055\u3093\u3088\u304dJupyter\u751f\u6d3b\u3092!!<\/p>\n
reference<\/h4>\n","protected":false},"excerpt":{"rendered":"
\u306f\u3058\u3081\u306b Jupyter lab\u3084Jupyter notebook\u304c\u975e\u5e38\u306b\u4fbf\u5229\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308b\u3053\u3068\u306f\u8a00\u3046\u307e\u3067\u3082\u306a […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-46562","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\n- Blog - Silicon Cloud<\/title>\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.silicloud.com\/zh\/blog\/46562-2\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:description\" content=\"\u306f\u3058\u3081\u306b Jupyter lab\u3084Jupyter notebook\u304c\u975e\u5e38\u306b\u4fbf\u5229\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308b\u3053\u3068\u306f\u8a00\u3046\u307e\u3067\u3082\u306a […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/zh\/blog\/46562-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - 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