{"id":45813,"date":"2023-05-22T22:21:45","date_gmt":"2023-02-26T17:13:14","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/"},"modified":"2024-04-30T05:28:51","modified_gmt":"2024-04-29T21:28:51","slug":"45813-2","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/","title":{"rendered":""},"content":{"rendered":"

Jupyter Notebook\u3067pytest<\/h1>\n

Jupyter Notebook\u3067\u4f5c\u6210\u3057\u305f.ipynb\u30d5\u30a1\u30a4\u30eb\u306bpytest\u3067\u5358\u4f53\u30c6\u30b9\u30c8\u3059\u308b\u30c4\u30fc\u30eb\u3068\u3057\u3066pytest_ipynb\u304c\u3042\u308a\u307e\u3059\u3002
\nhttps:\/\/pypi.python.org\/pypi\/pytest-ipynb<\/p>\n

\u3053\u3061\u3089\u3067\u3054\u7d39\u4ecb\u3055\u308c\u3066\u3044\u308b\u3068\u304a\u308a\u3001py.test -v\u3067\u547c\u3073\u51fa\u3057\u3066\u30bb\u30eb\u6bce\u306b\u30c6\u30b9\u30c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u512a\u308c\u3082\u306e\u3067\u3059\u3002
\n\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\u306f\u7c21\u5358\u3067\u3001\u4ee5\u4e0b\u3092\u5b9f\u884c\u3059\u308b\u3060\u3051\u3067\u4f7f\u3044\u59cb\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

pip install <\/span>pytest-ipynb\r\n<\/code><\/pre>\n
\u65e5\u3005\u6a5f\u68b0\u5b66\u7fd2\u3084\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092Jupyter Notebook\u3067\u66f8\u3044\u3066\u3044\u308b\u306e\u3067\u3001\u8a66\u3057\u306b\u4f7f\u3063\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n
\u3068\u308a\u3042\u3048\u305aMNIST<\/h2>\n
\u8a66\u3057\u306bMNIST\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u4f5c\u3063\u3066\u5b9f\u884c\u3057\u3066\u307f\u307e\u3057\u305f\u3002
\n\u306a\u304a\u3001\u4ee5\u4e0b\u30d7\u30ed\u30b0\u30e9\u30e0\u4e2d\u306e#####################\u306f\u30bb\u30eb\u306e\u533a\u5207\u308a\u3067\u3059\u3002
\n\u5b9f\u969b\u306eNotebook\u306b\u306f\u66f8\u3044\u3066\u3044\u307e\u305b\u3093\u3002<\/p>\n
\r\n#####################\r\n<\/span>\"\"\"fixture\"\"\"<\/span>\r\nimport<\/span> os<\/span>\r\nimport<\/span> time<\/span>\r\nimport<\/span> string<\/span>\r\nimport<\/span> pytest<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"import\"\"\"<\/span>\r\nimport<\/span> keras<\/span>\r\nfrom<\/span> keras.datasets<\/span> import<\/span> mnist<\/span>\r\nfrom<\/span> keras.models<\/span> import<\/span> Sequential<\/span>\r\nfrom<\/span> keras.layers<\/span> import<\/span> Dense<\/span>,<\/span> Dropout<\/span>,<\/span> Flatten<\/span>\r\nfrom<\/span> keras.layers<\/span> import<\/span> Conv2D<\/span>,<\/span> MaxPooling2D<\/span>\r\nfrom<\/span> keras<\/span> import<\/span> backend<\/span> as<\/span> K<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"data preparation\"\"\"<\/span>\r\nbatch_size<\/span> =<\/span> 128<\/span>\r\nnum_classes<\/span> =<\/span> 10<\/span>\r\nepochs<\/span> =<\/span> 4<\/span>\r\n\r\n# input image dimensions\r\n<\/span>img_rows<\/span>,<\/span> img_cols<\/span> =<\/span> 28<\/span>,<\/span> 28<\/span>\r\n\r\n# the data, shuffled and split between train and test sets\r\n<\/span>(<\/span>x_train<\/span>,<\/span> y_train<\/span>),<\/span> (<\/span>x_test<\/span>,<\/span> y_test<\/span>)<\/span> =<\/span> mnist<\/span>.<\/span>load_data<\/span>()<\/span>\r\n\r\nif<\/span> K<\/span>.<\/span>image_data_format<\/span>()<\/span> ==<\/span> 'channels_first'<\/span>:<\/span>\r\n    x_train<\/span> =<\/span> x_train<\/span>.<\/span>reshape<\/span>(<\/span>x_train<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> 1<\/span>,<\/span> img_rows<\/span>,<\/span> img_cols<\/span>)<\/span>\r\n    x_test<\/span> =<\/span> x_test<\/span>.<\/span>reshape<\/span>(<\/span>x_test<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> 1<\/span>,<\/span> img_rows<\/span>,<\/span> img_cols<\/span>)<\/span>\r\n    input_shape<\/span> =<\/span> (<\/span>1<\/span>,<\/span> img_rows<\/span>,<\/span> img_cols<\/span>)<\/span>\r\nelse<\/span>:<\/span>\r\n    x_train<\/span> =<\/span> x_train<\/span>.<\/span>reshape<\/span>(<\/span>x_train<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> img_rows<\/span>,<\/span> img_cols<\/span>,<\/span> 1<\/span>)<\/span>\r\n    x_test<\/span> =<\/span> x_test<\/span>.<\/span>reshape<\/span>(<\/span>x_test<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> img_rows<\/span>,<\/span> img_cols<\/span>,<\/span> 1<\/span>)<\/span>\r\n    input_shape<\/span> =<\/span> (<\/span>img_rows<\/span>,<\/span> img_cols<\/span>,<\/span> 1<\/span>)<\/span>\r\n\r\nassert<\/span> x_train<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>60000<\/span>,<\/span>28<\/span>,<\/span>28<\/span>,<\/span>1<\/span>)<\/span>\r\nassert<\/span> x_test<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>10000<\/span>,<\/span>28<\/span>,<\/span>28<\/span>,<\/span>1<\/span>)<\/span>\r\nassert<\/span> y_train<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>60000<\/span>,<\/span> )<\/span>\r\nassert<\/span> y_test<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>10000<\/span>,<\/span> )<\/span>\r\nassert<\/span> input_shape<\/span> ==<\/span> (<\/span>img_rows<\/span>,<\/span> img_cols<\/span>,<\/span> 1<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"prepare x\"\"\"<\/span>\r\n@<\/span>pytest<\/span>.<\/span>mark<\/span>.<\/span>timeout<\/span>(<\/span>180<\/span>)<\/span>\r\ndef<\/span> prepareX<\/span>(<\/span>x_train<\/span>,<\/span> x_test<\/span>):<\/span>\r\n    x_train<\/span> =<\/span> x_train<\/span>.<\/span>astype<\/span>(<\/span>'float32'<\/span>)<\/span>\r\n    x_test<\/span> =<\/span> x_test<\/span>.<\/span>astype<\/span>(<\/span>'float32'<\/span>)<\/span>\r\n    x_train<\/span> \/=<\/span> 255<\/span>\r\n    x_test<\/span> \/=<\/span> 255<\/span>\r\n    return<\/span> x_train<\/span>,<\/span>x_test<\/span>\r\nx_train<\/span>,<\/span>x_test<\/span> =<\/span> prepareX<\/span>(<\/span>x_train<\/span>,<\/span>x_test<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"print x shape\"\"\"<\/span>\r\nprint<\/span>(<\/span>'x_train shape:'<\/span>,<\/span> x_train<\/span>.<\/span>shape<\/span>)<\/span>\r\nprint<\/span>(<\/span>x_train<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> 'train samples'<\/span>)<\/span>\r\nprint<\/span>(<\/span>x_test<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>],<\/span> 'test samples'<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"prepare y\"\"\"<\/span>\r\n# convert class vectors to binary class matrices\r\n<\/span>y_train<\/span> =<\/span> keras<\/span>.<\/span>utils<\/span>.<\/span>to_categorical<\/span>(<\/span>y_train<\/span>,<\/span> num_classes<\/span>)<\/span>\r\ny_test<\/span> =<\/span> keras<\/span>.<\/span>utils<\/span>.<\/span>to_categorical<\/span>(<\/span>y_test<\/span>,<\/span> num_classes<\/span>)<\/span>\r\n\r\nassert<\/span> y_train<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>60000<\/span>,<\/span>10<\/span>)<\/span>\r\nassert<\/span> y_test<\/span>.<\/span>shape<\/span> ==<\/span> (<\/span>10000<\/span>,<\/span>10<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"build model\"\"\"<\/span>\r\n@<\/span>pytest<\/span>.<\/span>mark<\/span>.<\/span>timeout<\/span>(<\/span>10<\/span>)<\/span>\r\ndef<\/span> test_buildModel<\/span>():<\/span>\r\n    model<\/span> =<\/span> Sequential<\/span>()<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Conv2D<\/span>(<\/span>32<\/span>,<\/span> kernel_size<\/span>=<\/span>(<\/span>3<\/span>,<\/span> 3<\/span>),<\/span>\r\n                     activation<\/span>=<\/span>'relu'<\/span>,<\/span>\r\n                     input_shape<\/span>=<\/span>input_shape<\/span>))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Conv2D<\/span>(<\/span>64<\/span>,<\/span> (<\/span>3<\/span>,<\/span> 3<\/span>),<\/span> activation<\/span>=<\/span>'relu'<\/span>))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>MaxPooling2D<\/span>(<\/span>pool_size<\/span>=<\/span>(<\/span>2<\/span>,<\/span> 2<\/span>)))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Dropout<\/span>(<\/span>0.25<\/span>))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Flatten<\/span>())<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Dense<\/span>(<\/span>128<\/span>,<\/span> activation<\/span>=<\/span>'relu'<\/span>))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Dropout<\/span>(<\/span>0.5<\/span>))<\/span>\r\n    model<\/span>.<\/span>add<\/span>(<\/span>Dense<\/span>(<\/span>num_classes<\/span>,<\/span> activation<\/span>=<\/span>'softmax'<\/span>))<\/span>\r\n    return<\/span> model<\/span>\r\nmodel<\/span> =<\/span> test_buildModel<\/span>()<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"compile model\"\"\"<\/span>\r\n@<\/span>pytest<\/span>.<\/span>mark<\/span>.<\/span>timeout<\/span>(<\/span>10<\/span>)<\/span>\r\ndef<\/span> test_compileModel<\/span>(<\/span>model<\/span>):<\/span>\r\n    model<\/span>.<\/span>compile<\/span>(<\/span>loss<\/span>=<\/span>keras<\/span>.<\/span>losses<\/span>.<\/span>categorical_crossentropy<\/span>,<\/span>\r\n                  optimizer<\/span>=<\/span>keras<\/span>.<\/span>optimizers<\/span>.<\/span>Adadelta<\/span>(),<\/span>\r\n                  metrics<\/span>=<\/span>[<\/span>'accuracy'<\/span>])<\/span>\r\n    return<\/span> model<\/span>\r\nmodel<\/span> =<\/span> test_compileModel<\/span>(<\/span>model<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"train model\"\"\"<\/span>\r\n@<\/span>pytest<\/span>.<\/span>mark<\/span>.<\/span>timeout<\/span>(<\/span>600<\/span>)<\/span>\r\ndef<\/span> test_trainModel<\/span>(<\/span>model<\/span>):<\/span>\r\n    history<\/span> =<\/span> model<\/span>.<\/span>fit<\/span>(<\/span>x_train<\/span>,<\/span> y_train<\/span>,<\/span>\r\n                        batch_size<\/span>=<\/span>batch_size<\/span>,<\/span>\r\n                        epochs<\/span>=<\/span>epochs<\/span>,<\/span>\r\n                        verbose<\/span>=<\/span>0<\/span>,<\/span>\r\n                        validation_data<\/span>=<\/span>(<\/span>x_test<\/span>,<\/span> y_test<\/span>))<\/span>\r\n    return<\/span> model<\/span>,<\/span>history<\/span>\r\nmodel<\/span>,<\/span>history<\/span> =<\/span> test_trainModel<\/span>(<\/span>model<\/span>)<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"score model\"\"\"<\/span>\r\n@<\/span>pytest<\/span>.<\/span>mark<\/span>.<\/span>timeout<\/span>(<\/span>120<\/span>)<\/span>\r\ndef<\/span> test_scoreModel<\/span>(<\/span>model<\/span>):<\/span>\r\n    score<\/span> =<\/span> model<\/span>.<\/span>evaluate<\/span>(<\/span>x_test<\/span>,<\/span> y_test<\/span>,<\/span> verbose<\/span>=<\/span>0<\/span>)<\/span>\r\n    print<\/span>(<\/span>score<\/span>)<\/span>\r\n    return<\/span> score<\/span>\r\nscore<\/span> =<\/span> test_scoreModel<\/span>(<\/span>model<\/span>)<\/span>\r\nassert<\/span> score<\/span>[<\/span>0<\/span>]<\/span> <<\/span> 0.05<\/span>\r\nassert<\/span> score<\/span>[<\/span>1<\/span>]<\/span> ><\/span> 0.98<\/span>\r\n\r\n#####################\r\n<\/span>\"\"\"print score\"\"\"<\/span>\r\nprint<\/span>(<\/span>'Test loss:'<\/span>,<\/span> score<\/span>[<\/span>0<\/span>])<\/span>\r\nprint<\/span>(<\/span>'Test accuracy:'<\/span>,<\/span> score<\/span>[<\/span>1<\/span>])<\/span>\r\n\r\n<\/code><\/pre>\n
\u3064\u307e\u305a\u3044\u305f\u70b9<\/h2>\n
Jupyter Notebook\u4e0a\u306b\u66f8\u3044\u305fassert xxx\u304c\u30bb\u30eb\u6bce\u306b\u8a55\u4fa1\u3055\u308c\u307e\u3059\u3002
\nJupyter Notebook\u3067\u5404\u30bb\u30eb\u3092\u5b9f\u884c\u3059\u308b\u969b\u306b\u3082assert\u3055\u308c\u307e\u3059\u3002
\n\u305f\u3060\u3057\u3001\u3053\u3053\u3067\u4f7f\u3063\u3066\u3044\u308b\u306e\u306fpytest\u3067\u3042\u3063\u3066\u3001pytest-ipynb\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002
\npytest\u3068\u3057\u3066assert\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
pytest-ipynb\u306f.ipynb\u30d5\u30a1\u30a4\u30eb\u3092py.test -v\u3067\u30c6\u30b9\u30c8\u3059\u308b\u305f\u3081\u306e\u30c4\u30fc\u30eb\u306b\u306a\u308a\u307e\u3059\u3002
\n\u30b3\u30f3\u30bd\u30fc\u30eb\u4e0a\u3067py.test -v\u3092\u5b9f\u884c\u3059\u308c\u3070\u3001test_**.ipynb\u30d5\u30a1\u30a4\u30eb\u306e\u5404\u30bb\u30eb\u306b\u5bfe\u3057\u3066\u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u3066\u304f\u308c\u308b\u3082\u306e\u3067\u3059\u3002<\/p>\n
\u305d\u3057\u3066pytest-ipynb\u306e\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30bb\u30eb\u306b\u8a18\u8f09\u3057\u305f\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u5024\u3092\u53c2\u7167\u3057\u307e\u305b\u3093\u3002
\n\u3069\u3053\u3092\u53c2\u7167\u3059\u308b\u304b\u3068\u3044\u3046\u3068\u3001pytest-ipynb\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u305fpytest_ipynb\/plugin.py\u306e\u4e2d\u306b20\u79d2\u3068\u30cf\u30fc\u30c9\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3055\u308c\u3066\u3044\u307e\u3059\u3002
\npy.test -v –timeout=600\u306e\u3088\u3046\u306b–timeout\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4ed8\u3051\u3066\u3082\u7121\u99c4\u3067\u3057\u305f\u3002
\n\u305d\u306e\u305f\u3081\u300120\u79d2\u3092\u8d85\u3048\u308b\u30bb\u30eb\u3092\u30c6\u30b9\u30c8\u3059\u308b\u3068\u300120\u79d2\u7d4c\u904e\u3057\u3066Failure\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\"1.PNG\"<\/div>\n
\u3068\u3044\u3046\u308f\u3051\u3067\u3001pytest-ipynb\u3067\u30b3\u30f3\u30bd\u30fc\u30eb\u4e0a\u304b\u3089.ipynb\u30d5\u30a1\u30a4\u30eb\u3092\u30c6\u30b9\u30c8\u3059\u308b\u5834\u5408\u3001\u5b9f\u884c\u306b20\u79d2\u3092\u8d85\u3048\u308b\u30bb\u30eb\u306e\u305f\u3081\u306b\u306f\u3001pytest_ipynb\/plugin.py\u306e\u4ee5\u4e0b\u90e8\u5206\u3092\u5909\u66f4\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
\r\ndef<\/span> runtest<\/span>(<\/span>self<\/span>):<\/span>\r\n    #self.parent.runner.km.restart_kernel()\r\n<\/span>\r\n    if<\/span> self<\/span>.<\/span>parent<\/span>.<\/span>notebook_folder<\/span>:<\/span>\r\n        self<\/span>.<\/span>parent<\/span>.<\/span>runner<\/span>.<\/span>kc<\/span>.<\/span>execute<\/span>(<\/span>\r\n\"\"\"import os\r\nos.chdir(\"%s\")\"\"\"<\/span> %<\/span> self<\/span>.<\/span>parent<\/span>.<\/span>notebook_folder<\/span>)<\/span>\r\n\r\n    if<\/span> (<\/span>\"SKIPCI\"<\/span> in<\/span> self<\/span>.<\/span>cell_description<\/span>)<\/span> and<\/span> (<\/span>\"CI\"<\/span> in<\/span> os<\/span>.<\/span>environ<\/span>):<\/span>\r\n        pass<\/span>\r\n    else<\/span>:<\/span>\r\n        if<\/span> self<\/span>.<\/span>parent<\/span>.<\/span>fixture_cell<\/span>:<\/span>\r\n            self<\/span>.<\/span>parent<\/span>.<\/span>runner<\/span>.<\/span>kc<\/span>.<\/span>execute<\/span>(<\/span>self<\/span>.<\/span>parent<\/span>.<\/span>fixture_cell<\/span>.<\/span>input<\/span>,<\/span> allow_stdin<\/span>=<\/span>False<\/span>)<\/span>\r\n        msg_id<\/span> =<\/span> self<\/span>.<\/span>parent<\/span>.<\/span>runner<\/span>.<\/span>kc<\/span>.<\/span>execute<\/span>(<\/span>self<\/span>.<\/span>cell<\/span>.<\/span>input<\/span>,<\/span> allow_stdin<\/span>=<\/span>False<\/span>)<\/span>\r\n        if<\/span> self<\/span>.<\/span>cell_description<\/span>.<\/span>lower<\/span>().<\/span>startswith<\/span>(<\/span>\"fixture\"<\/span>)<\/span> or<\/span> self<\/span>.<\/span>cell_description<\/span>.<\/span>lower<\/span>().<\/span>startswith<\/span>(<\/span>\"setup\"<\/span>):<\/span>\r\n            self<\/span>.<\/span>parent<\/span>.<\/span>fixture_cell<\/span> =<\/span> self<\/span>.<\/span>cell<\/span>\r\n        timeout<\/span> =<\/span> 20<\/span> #\u2190\u2190\u3000\u3053\u3053\uff01\uff01\uff01\u3000\r\n<\/span>        while<\/span> True<\/span>:<\/span>\r\n            try<\/span>:<\/span>\r\n                msg<\/span> =<\/span> self<\/span>.<\/span>parent<\/span>.<\/span>runner<\/span>.<\/span>kc<\/span>.<\/span>get_shell_msg<\/span>(<\/span>block<\/span>=<\/span>True<\/span>,<\/span> timeout<\/span>=<\/span>timeout<\/span>)<\/span>\r\n                if<\/span> msg<\/span>.<\/span>get<\/span>(<\/span>\"parent_header\"<\/span>,<\/span> None<\/span>)<\/span> and<\/span> msg<\/span>[<\/span>\"parent_header\"<\/span>].<\/span>get<\/span>(<\/span>\"msg_id\"<\/span>,<\/span> None<\/span>)<\/span> ==<\/span> msg_id<\/span>:<\/span>\r\n                    break<\/span>\r\n            except<\/span> Empty<\/span>:<\/span>\r\n                raise<\/span> IPyNbException<\/span>(<\/span>\"Timeout of %d seconds exceeded executing cell: %s\"<\/span> (<\/span>timeout<\/span>,<\/span> self<\/span>.<\/span>cell<\/span>.<\/span>input<\/span>))<\/span>\r\n\r\n        reply<\/span> =<\/span> msg<\/span>[<\/span>'content'<\/span>]<\/span>\r\n\r\n        if<\/span> reply<\/span>[<\/span>'status'<\/span>]<\/span> ==<\/span> 'error'<\/span>:<\/span>\r\n            raise<\/span> IPyNbException<\/span>(<\/span>self<\/span>.<\/span>cell_num<\/span>,<\/span> self<\/span>.<\/span>cell_description<\/span>,<\/span> self<\/span>.<\/span>cell<\/span>.<\/span>input<\/span>,<\/span> '<\/span>\\n<\/span>'<\/span>.<\/span>join<\/span>(<\/span>reply<\/span>[<\/span>'traceback'<\/span>]))<\/span>\r\n\r\n<\/code><\/pre>\n
vi\u304b\u306a\u306b\u304b\u3067\u7de8\u96c6\u3057\u307e\u3057\u3087\u3046\u3002
\n\u3053\u308c\u304c\u308f\u304b\u3089\u306a\u304f\u3066\u540c\u3058\u30c6\u30b9\u30c8\u309210\u56de\u304f\u3089\u3044\u6d41\u3057\u307e\u3057\u305f\u3002<\/p>\n
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Jupyter Notebook\u3067pytest Jupyter Notebook\u3067\u4f5c\u6210\u3057\u305f.ipynb\u30d5\u30a1\u30a4\u30eb […]<\/p>\n","protected":false},"author":12,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-45813","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\/45813-2\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:description\" content=\"Jupyter Notebook\u3067pytest Jupyter Notebook\u3067\u4f5c\u6210\u3057\u305f.ipynb\u30d5\u30a1\u30a4\u30eb […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - Silicon Cloud\" \/>\n<meta property=\"article:published_time\" content=\"2023-02-26T17:13:14+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-04-29T21:28:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn.silicloud.com\/blog-img\/blog\/img\/657d616937434c4406cfd7ff\/12-0.png\" \/>\n<meta name=\"author\" content=\"\u9038, \u79d1\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u9038, \u79d1\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/\",\"url\":\"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/\",\"name\":\"- Blog - Silicon Cloud\",\"isPartOf\":{\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#website\"},\"datePublished\":\"2023-02-26T17:13:14+00:00\",\"dateModified\":\"2024-04-29T21:28:51+00:00\",\"author\":{\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#\/schema\/person\/85c1dae56e6ea1e695c73d33c684d487\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.silicloud.com\/zh\/blog\/45813-2\/\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#website\",\"url\":\"https:\/\/www.silicloud.com\/zh\/blog\/\",\"name\":\"Blog - 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