sample-notebooks/02_NII謹製_Jupyterの機能拡張について.ipynb

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   "source": [
    "# Literate Computing for Reproducible Infrastructure\n",
    "<img src=\"./images/literate_computing-logo.png\" align=\"left\" />\n",
    "\n",
    "計算機インフラの御守では種々雑多なドキュメンテーションが不可欠です。日々の作業で証跡を残す、手順を整理して共有・再利用する、ユーザマニュアルや教材を整備する.. 国立情報学研究所（NII）のクラウド運用担当では、これらをシームレスに記述・蓄積する方法を研究しています。\n",
    "\n",
    "表題の Literate Computing for Reproducible Infrastructure（以下 「LC4RI」）は、インフラ運用の場面において **機械的に再現できる、人が読み解ける手順** を手段として、過度に自動化に依存することのない、レジリエントな **人間中心の機械化** をめざしています。そこでは、作業を効率化しつつもブラックボックス化せず、作業に対する理解をチーム内でコミュニケーションできる、また、目的と手段の整合性や限界を理解し議論・評価できると言った場を維持することで、ノウハウの移転・共有を促し運用者のスキル向上とエンジニアリングチームの再生産をはかることを重視しています。\n",
    "\n",
    "多くの現場では、管理サーバにログインしコンソール上で作業を行う、作業内容や証跡はWiki等に随時転記して共有する.. といった形態が一般的と思います。これに対しLC4RIでは運用管理サーバ上にNotebookサーバを配備し、作業単位毎にNotebookを作成、作業内容やメモを記述しながら随時実行するといった作業形態を推奨しています。作業の証跡を齟齬なく記録する仕組み、過去の作業記録を参照して機械的に再現あるいは流用できる仕組み、機械的に実行できるとともに人が読み解き補完することもできるNotebook手順を整備しています。\n",
    "\n",
    "プロジェクトの成果物は GitHub [NII Cloud Operation Team]( https://github.com/NII-cloud-operation )で公開しています。\n",
    "\n",
    "---\n",
    "この Notebook では拡張した機能の概要を紹介します。"
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    "## Jupyter-run_through - まとめ実行機能\n",
    "\n",
    "用途:\n",
    "* 詳細は気にせずNotebook形式の手順を気軽に利用したい。\n",
    "* 定型的な作業をまとめて実行する。アレンジやカスタマイズはあまり必要がない。\n",
    "* まとめて実行してエラーが発生した場合には、詳細を確認したい。\n",
    "\n",
    "<i class='fa fa-fw fa-external-link'></i> [GitHub: Jupyter-LC_run_through](https://github.com/NII-cloud-operation/Jupyter-LC_run_through)\n",
    "\n",
    "---\n",
    "\n",
    "**畳み込み表示 と まとめ実行の機能** <br>\n",
    "表題の左側にある <i class='fa fa-fw fa-caret-right' style='color:#a0a0a0;'></i> は、詳細な手順が畳み込まれていることを示しています。  <i class='fa fa-fw fa-caret-right' style='color:#a0a0a0;'></i>  をクリックすると、 <i class='fa fa-fw fa-caret-down' style='color:#a0a0a0;'></i>  に変化し、畳み込まれている内容を表示することができます。\n",
    "\n",
    "<img src=\"./images/run_through.png\" align=\"right\" width=30% /> 畳みこまれている内容に「手順」が含まれていると、右のように表示されます。<i class='fa fa-fw fa-square' style='color:#cccccc;'></i>の数は畳み込まれている手順のステップ数を示しています。\n",
    "表示から <i class='fa fa-fw fa-play-circle'></i> を押すと、配下の手順をまとめて実行することができます。\n",
    "\n",
    "<img src=\"./images/run_through_OK.png\" width=30% align=right /> すべてのステップが終了すると右のように表示が変化します。実行が完了したステップは薄緑 <i class='fa fa-fw fa-square' style='color:#88ff88;'></i>に表示されます。また、完了したステップは、再度 まとめ実行ボタン<i class='fa fa-fw fa-play-circle'></i>をクリックしても実行されないよう凍結 <i class='fa fa-fw fa-snowflake-o' style='background-color:cornflowerblue;color:white;'></i>\n",
    "されます。\n",
    "\n",
    "<img src=\"./images/run_through_NG.png\" width=30% align=right /> 途中でエラーが発生した場合は当該のステップが薄紅<i class='fa fa-fw fa-square' style='color:#ff8888;'></i>に表示されるので、 <i class='fa fa-fw fa-caret-right'></i>をクリックして内容を確認します。\n",
    "\n"
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    "### **畳み込んだステップのまとめ実行**\n",
    "\n",
    "<i class='fa fa-fw fa-play-circle'></i> をクリックすると４ステップをまとめ実行します。実行が終わったステップは薄緑の表示<i class='fa fa-fw fa-square' style='color:#88ff88;'></i>となります。以下の例では３番目がエラーとなり薄紅表示<i class='fa fa-fw fa-square' style='color:#ff8888;'></i>され、実行が中断します。\n",
    "\n",
    "<i class='fa fa-fw fa-caret-right' style='color:#a0a0a0;'></i> をクリックすることで畳み込み表示が解除され、それぞれのステップの実行内容を確認することができます。実行が終わったセルは凍結 <i class='fa fa-fw fa-snowflake-o' style='background-color:cornflowerblue;color:white;'></i>\n",
    "されます、そのままでは実行できません。修正する、再度実行する場合にはウインドウ上部の<i class='fa fa-fw fa-snowflake-o' style='color:gray;'></i>をクリックすることで凍結を解除します。\n",
    "\n",
    "エラーのセル<i class='fa fa-fw fa-square' style='color:#ff8888;'></i>は内容を修正して実行することができます。継続する未実行のセル<i class='fa fa-fw fa-square' style='color:#cccccc;'></i>は、そのまま実行することもできます。"
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    "! echo \"This is 1st step\" > foo; cat foo"
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   "outputs": [],
   "source": [
    "! echo \".. 2nd step...\" >> foo && cat foo"
   ]
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     "frozen": false,
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   "source": [
    "!echooooo \".. 3rd step... will fail\" >> foo && cat foo"
   ]
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   "source": [
    "\"echo\" に修正して実行してみましょう。 "
   ]
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    "run_through_control": {
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   },
   "outputs": [],
   "source": [
    "! cat foo"
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   "source": [
    "## セルの実行結果の保存と要約表示\n",
    "\n",
    "用途:\n",
    "* Cell単位の実行ログを各々ファイルに常時保存し、後で全体を参照したり、結果を比較できるようにする。\n",
    "* Output Cell に大量出力される場合に、必要に応じて要約表示する（出力全体は上記の機能によって保存されている）。\n",
    "\n",
    "<i class='fa fa-fw fa-external-link'></i> [GitHub: Jupyter-LC_wrapper](https://github.com/NII-cloud-operation/Jupyter-LC_wrapper)\n",
    "\n",
    "---\n",
    "例えば、某かのパッケージ群をインストールしたりすると大量のログが出力されますが、JupyetrのWeb UI上で大量の出力を扱うのはなにかと不便です。 Jupyterの Cell の中でログの内容を検索したり、比較したりするのはまどろっこしく手間がかかります。\n",
    "\n",
    "Jupyter-LC_wrapper を用いると:\n",
    "* 毎回 Cell を実行する度に、出力結果がファイルに保存されます (./notebooks/.log/yyyymmdd/yyyymmdd-hhmmss-xxxx.log)。\n",
    "* 実行毎に各々のファイルに結果が保存されるので出力結果を比較することができます。\n",
    "\n",
    "\n",
    "* Cellでコマンドを実行する際に先頭に \"!!\" を付加すると、Output Cell には要約された結果が出力されるようになります（デフォルト：50行を超える出力があった場合、最初20行と最後の20行; また、要約表示で shell を実行する場合は \"!!!\" )。\n",
    "* 要約中でも、エラーなど特定のパターン含む行を表示するので結果の確認が容易です（~/.lc_wrapper_regex.txt などいくつかの方法でカスタマイズ可）。\n"
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      {
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       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "8f956e50-3c65-11e7-b4fc-0242ac110002"
      },
      {
       "current": "8f91607c-45fb-11e7-9f10-02420aff0008",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "325b2b76-3f76-11e7-806f-0242ac110002"
      },
      {
       "current": "8f91607c-45fb-11e7-9f10-02420aff0008",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "12ba33ce-49e0-11e7-9220-02420aff0006"
      },
      {
       "current": "8f91607c-45fb-11e7-9f10-02420aff0008",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "325b2b76-3f76-11e7-806f-0242ac110002"
      },
      {
       "current": "8f91607c-45fb-11e7-9f10-02420aff0008",
       "next": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "previous": "325b2b76-3f76-11e7-806f-0242ac110002"
      }
     ],
     "next": "52addd26-8054-11e9-8f9b-0242ac110002",
     "previous": "325b2b76-3f76-11e7-806f-0242ac110002"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "!!from time import sleep\n",
    "with open(\"./resources/bootstrap.log\", \"r\") as f:\n",
    "    count = 0\n",
    "    limit = 100\n",
    "    for line in f:\n",
    "        count = count+1\n",
    "        if count > limit: break\n",
    "        print(line,end=''),\n",
    "        sleep(0.05)\n",
    "print (\"after\", limit, \"lines are ignored\")\n",
    "#\n",
    "# ここでは擬似的にログが大量に出力される状況を再現しています\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "lc_cell_meme": {
     "current": "52addd26-8054-11e9-8f9b-0242ac110002-1-25a0",
     "next": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
     "previous": "8f91607c-45fb-11e7-9f10-02420aff0008"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "ログファイルは 日付毎のディレクトリの下に保存されます。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "lc_cell_meme": {
     "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002-1-22c7",
     "history": [
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "8f91607c-45fb-11e7-9f10-02420aff0008"
      },
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "eda23c84-8052-11e9-8f9b-0242ac110002",
       "previous": "8f91607c-45fb-11e7-9f10-02420aff0008"
      },
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "8f91607c-45fb-11e7-9f10-02420aff0008"
      },
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "52addd26-8054-11e9-8f9b-0242ac110002"
      },
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "9a33e9b0-8054-11e9-8f9b-0242ac110002",
       "previous": "52addd26-8054-11e9-8f9b-0242ac110002"
      },
      {
       "current": "5e05fe9e-8052-11e9-8f9b-0242ac110002",
       "next": "ab49925a-45ef-11e7-a8e4-0242ac110002",
       "previous": "52addd26-8054-11e9-8f9b-0242ac110002"
      }
     ],
     "next": "1c09f9d2-8057-11e9-8f9b-0242ac110002",
     "previous": "52addd26-8054-11e9-8f9b-0242ac110002"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "!ls -laR .log/`date \"+%Y%m%d\"`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "lc_cell_meme": {
     "current": "1c09f9d2-8057-11e9-8f9b-0242ac110002-1-6ef0",
     "history": [
      {
       "current": "1c09f9d2-8057-11e9-8f9b-0242ac110002",
       "next": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "previous": "5e05fe9e-8052-11e9-8f9b-0242ac110002"
      },
      {
       "current": "1c09f9d2-8057-11e9-8f9b-0242ac110002",
       "next": "630519ca-8066-11e9-a0d7-0242ac110002",
       "previous": "5e05fe9e-8052-11e9-8f9b-0242ac110002"
      }
     ],
     "next": "aa896968-8066-11e9-a0d7-0242ac110002",
     "previous": "5e05fe9e-8052-11e9-8f9b-0242ac110002"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## セル実行結果の pin 留め"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "lc_cell_meme": {
     "current": "aa896968-8066-11e9-a0d7-0242ac110002-1-ce4d",
     "history": [
      {
       "current": "aa896968-8066-11e9-a0d7-0242ac110002",
       "next": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "previous": "1c09f9d2-8057-11e9-8f9b-0242ac110002"
      }
     ],
     "next": "f20f2f34-8066-11e9-a0d7-0242ac110002",
     "previous": "1c09f9d2-8057-11e9-8f9b-0242ac110002"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "lc_cell_meme": {
     "current": "f20f2f34-8066-11e9-a0d7-0242ac110002-1-f654",
     "next": "f20f309c-8066-11e9-a0d7-0242ac110002",
     "previous": "aa896968-8066-11e9-a0d7-0242ac110002"
    },
    "pinnedOutputTabIndex": 0,
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": true,
    "lc_cell_meme": {
     "current": "f20f309c-8066-11e9-a0d7-0242ac110002-1-0724",
     "next": "f20f3132-8066-11e9-a0d7-0242ac110002",
     "previous": "f20f2f34-8066-11e9-a0d7-0242ac110002"
    },
    "pinnedOutputTabIndex": 1,
    "pinned_outputs": [
     {
      "execution_count": 8,
      "outputs": [
       {
        "data": {
         "text/plain": "<Axes: >"
        },
        "execution_count": 8,
        "metadata": {},
        "output_type": "execute_result"
       },
       {
        "data": {
         "image/png": 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",
         "text/plain": "<Figure size 640x480 with 1 Axes>"
        },
        "execution_count": 8,
        "metadata": {},
        "output_type": "display_data"
       }
      ]
     }
    ],
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "plot_df = pandas.DataFrame({\n",
    "    'col1': [1, 3, random.randint(1,10), 4],\n",
    "    'col2': [3, random.randint(5,10), 5, 1],\n",
    "    'col3': [4, 7, 6, 2],\n",
    "})\n",
    "\n",
    "plot_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "lc_cell_meme": {
     "current": "f20f3132-8066-11e9-a0d7-0242ac110002-1-2532",
     "next": "d65dc74c-8056-11e9-8f9b-0242ac110002",
     "previous": "f20f309c-8066-11e9-a0d7-0242ac110002"
    },
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Output Cell の左側に表示されている <i class=\"fa fa-thumb-tack\"></i> をクリックすると、出力内容をタブとして保存することができます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "lc_cell_meme": {
     "current": "d65dc74c-8056-11e9-8f9b-0242ac110002-1-eb1f",
     "history": [
      {
       "current": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "next": null,
       "previous": "56854f66-4ba8-11e7-9954-02420aff0008"
      },
      {
       "current": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "next": null,
       "previous": "1c09f9d2-8057-11e9-8f9b-0242ac110002"
      },
      {
       "current": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "next": null,
       "previous": "630519ca-8066-11e9-a0d7-0242ac110002"
      },
      {
       "current": "d65dc74c-8056-11e9-8f9b-0242ac110002",
       "next": null,
       "previous": "aa896968-8066-11e9-a0d7-0242ac110002"
      }
     ],
     "next": null,
     "previous": "f20f3132-8066-11e9-a0d7-0242ac110002"
    },
    "run_through_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## 参考\n",
    "<span class='fa fa-fw fa-external-link'></span> [Jupyter-code_cell_status](https://github.com/NII-cloud-operation/Jupyter-code_cell_status)\n",
    "\n",
    "<span class='fa fa-fw fa-external-link'></span> [Jupyter-multi-outputs](https://github.com/NII-cloud-operation/Jupyter-multi_outputs)\n",
    "\n",
    "<span class='fa fa-fw fa-external-link'></span> [Jupyter-LC_nblineage](https://github.com/NII-cloud-operation/Jupyter-LC_nblineage)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "lc_notebook_meme": {
   "current": "29f2fd34-3c64-11e7-b4fc-0242ac110002",
   "lc_server_signature": {
    "current": {
     "notebook_dir": "/home/jovyan",
     "notebook_path": "/02_NII謹製_Jupyterの機能拡張について.ipynb",
     "signature_id": "1e7c9bf6-244a-11ef-a5e1-02e388e7b309"
    },
    "history": [
     {
      "notebook_dir": "/home/jovyan",
      "notebook_path": "/",
      "server_url": "http://localhost:8000/",
      "signature_id": "345312fe-8060-11e9-a0d7-0242ac110002"
     }
    ]
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}