sample/google_colab/katawrap_sample_colab.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Sample for katawrap on Google Colaboratory"
      ],
      "metadata": {
        "id": "wRf2TbxpeBaQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**YOU NEED TO CHANGE RUNTIME TYPE TO \"GPU\" IN \"RUNTIME\" MENU FIRST!**"
      ],
      "metadata": {
        "id": "onJRQAyxefpc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download a helper script."
      ],
      "metadata": {
        "id": "PVC8t5P7ensZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -L https://raw.githubusercontent.com/kaorahi/katawrap/_colab1/sample/google_colab/katawrap_colab.sh > katawrap_colab.sh\n",
        "!chmod a+x katawrap_colab.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHfaP40weeZ2",
        "outputId": "eb49991c-0485-442b-9d15-77f79179cd31"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  2880  100  2880    0     0   7700      0 --:--:-- --:--:-- --:--:--  7680\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setup KataGo and katawrap."
      ],
      "metadata": {
        "id": "fOI3sg9hhjeH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!./katawrap_colab.sh setup"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqTfeZ-0gnNS",
        "outputId": "eccb3316-b3b1-406e-dffa-30324a18fed2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@@@@@@@@@@ Fix libzip issue...\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
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            "Selecting previously unselected package libzip4:amd64.\n",
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            "@@@@@@@@@@ Download...\n",
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            "@@@@@@@@@@ Unzip...\n",
            "Archive:  katago.zip\n",
            "  inflating: katago_dir/README.txt   \n",
            "  inflating: katago_dir/analysis_example.cfg  \n",
            "  inflating: katago_dir/cacert.pem   \n",
            "  inflating: katago_dir/contribute_example.cfg  \n",
            "  inflating: katago_dir/default_gtp.cfg  \n",
            "  inflating: katago_dir/katago       \n",
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            "Archive:  katawrap.zip\n",
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            "  inflating: katawrap-0ff2cf6d6bb80e6067413e64d18e4a5f58952701/katawrap/katrain/sgf_parser.py  \n",
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            "  inflating: katawrap-0ff2cf6d6bb80e6067413e64d18e4a5f58952701/sample/katawrap_sample.ipynb  \n",
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            "@@@@@@@@@@ Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run KataGo with katawrap. (Modify input and output files if you like.)"
      ],
      "metadata": {
        "id": "YV4grY6Ihunn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls katawrap_dir/sample/sgf/*.sgf | ./katawrap_colab.sh run -visits 400 > result.jsonl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCHE4oN4hCel",
        "outputId": "086bb327-2fa4-44de-fae0-0778e07e8676"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[q 1/2] [res ? > 52 > 0 > 0 > 0] 00:00 ... \r[q 2/2] [res 0 > 79 > 0 > 0 > 0] 00:00 ... 2023-01-09 10:18:25+0000: Running with following config:\n",
            "logDir = analysis_logs\n",
            "maxVisits = 500\n",
            "nnCacheSizePowerOfTwo = 23\n",
            "nnMaxBatchSize = 64\n",
            "nnMutexPoolSizePowerOfTwo = 17\n",
            "nnRandomize = true\n",
            "numAnalysisThreads = 2\n",
            "numSearchThreads = 16\n",
            "reportAnalysisWinratesAs = BLACK\n",
            "\n",
            "2023-01-09 10:18:25+0000: Analysis Engine starting...\n",
            "2023-01-09 10:18:25+0000: KataGo v1.12.0\n",
            "2023-01-09 10:18:25+0000: nnRandSeed0 = 13754789479535553147\n",
            "2023-01-09 10:18:25+0000: After dedups: nnModelFile0 = /content/katago_dir/default_model.bin.gz useFP16 auto useNHWC auto\n",
            "2023-01-09 10:18:25+0000: Initializing neural net buffer to be size 19 * 19 allowing smaller boards\n",
            "2023-01-09 10:18:26+0000: Cuda backend thread 0: Found GPU Tesla T4 memory 15843721216 compute capability major 7 minor 5\n",
            "2023-01-09 10:18:26+0000: Cuda backend thread 0: Model version 11 useFP16 = true useNHWC = true\n",
            "2023-01-09 10:18:26+0000: Cuda backend thread 0: Model name: b18c384nbt-uec-20221121b\n",
            "2023-01-09 10:18:34+0000: Loaded config katago_dir/analysis_example.cfg\n",
            "2023-01-09 10:18:34+0000: Loaded model /content/katago_dir/default_model.bin.gz\n",
            "2023-01-09 10:18:34+0000: Analyzing up to 2 positions at at time in parallel\n",
            "2023-01-09 10:18:34+0000: Started, ready to begin handling requests\n",
            "[q 2/2] [res 0 > 0 > 0 > 0 > 79] 00:44 ... All done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the results in Python."
      ],
      "metadata": {
        "id": "3wSb5dGjjFih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_json('./result.jsonl', lines=True)\n",
        "print(df[['sgfFile', 'turnNumber', 'winrate']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00WSAwSdh0Td",
        "outputId": "d224f420-9299-4be0-c210-0367c09d6a12"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  sgfFile  turnNumber   winrate\n",
            "0   katawrap_dir/sample/sgf/sample001.sgf           0  0.326399\n",
            "1   katawrap_dir/sample/sgf/sample001.sgf           1  0.333773\n",
            "2   katawrap_dir/sample/sgf/sample001.sgf           2  0.594019\n",
            "3   katawrap_dir/sample/sgf/sample001.sgf           3  0.183481\n",
            "4   katawrap_dir/sample/sgf/sample001.sgf           4  0.571730\n",
            "..                                    ...         ...       ...\n",
            "74  katawrap_dir/sample/sgf/sample009.sgf          22  0.887344\n",
            "75  katawrap_dir/sample/sgf/sample009.sgf          23  0.911252\n",
            "76  katawrap_dir/sample/sgf/sample009.sgf          24  0.993918\n",
            "77  katawrap_dir/sample/sgf/sample009.sgf          25  0.011904\n",
            "78  katawrap_dir/sample/sgf/sample009.sgf          26  0.015735\n",
            "\n",
            "[79 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find the worst 5 blunders excluding those that have nothing to do with winning or losing."
      ],
      "metadata": {
        "id": "jzjOK4bvjMHJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "blunders = df.query('nextWinrateGain < -0.1') \\\n",
        "    .sort_values('nextScoreGain') \\\n",
        "    .head(5)[['sgfFile', 'turnNumber', 'nextScoreGain']]\n",
        "print(blunders)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vingZQONjOXW",
        "outputId": "ab833237-626a-4a84-d48f-7459d4e586d0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  sgfFile  turnNumber  nextScoreGain\n",
            "34  katawrap_dir/sample/sgf/sample001.sgf          34     -36.179983\n",
            "76  katawrap_dir/sample/sgf/sample009.sgf          24     -35.812483\n",
            "31  katawrap_dir/sample/sgf/sample001.sgf          31     -25.962390\n",
            "67  katawrap_dir/sample/sgf/sample009.sgf          15     -11.562915\n",
            "7   katawrap_dir/sample/sgf/sample001.sgf           7      -9.494816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**You need to copy result.jsonl to somewhere outside colab to keep it.** (To download it, click the folder mark in the left side to show the file list. Then right-click on result.jsonl and download it.)"
      ],
      "metadata": {
        "id": "pLIEl-yijf9j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "See katawrap_sample.ipynb for more snippets."
      ],
      "metadata": {
        "id": "mpXPyGD-BOaJ"
      }
    }
  ]
}