{
    "info": {
        "author": "wqy",
        "author_email": "qiyingwangwqy@gmail.com",
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            "Operating System :: POSIX :: Linux",
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        "description": "# aidockermon\n\n[![Build Status](https://travis-ci.org/WqyJh/aidockermon.svg?branch=master)](https://travis-ci.org/WqyJh/aidockermon)\n[![license](https://img.shields.io/badge/LICENCE-GPLv3-brightgreen.svg)](https://raw.githubusercontent.com/WqyJh/aidockermon/master/LICENSE)\n\nMonitor system load of the server running the nvidia/cuda docker containers.\n\n## Feature\n\n- sysinfo: system static info\n- sysload: system cpu/memory load\n- gpu: nvidia gpu load\n- disk: disk load\n- containers: containers' load that based on the nvidia/cuda image\n\n## Prerequisite\n\nPython >= 3\n\n## Installation\n\n```bash\npip install aidockermon\n```\n\nOr use `setuptools`\n```bash\npython setup.py install\n```\n\n## Usage\n\n```\n$ aidockermon -h\nusage: aidockermon [-h] [-v] {query,create-esindex,delete-esindex} ...\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -v, --version         show program's version number and exit\n\ncommand:\n  {query,create-esindex,delete-esindex}\n    query               Query system info, log them via syslog protocol\n    create-esindex      Create elasticsearch index\n    delete-esindex      Delete elasticsearch index\n```\n\n```\n$ aidockermon query -h\nusage: aidockermon query [-h] [-l] [-r REPEAT] [-f FILTERS [FILTERS ...]] type\n\npositional arguments:\n  type                  info type: sysinfo, sysload, gpu, disk, containers\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -l, --stdout          Print pretty json to console instead of send a log\n  -r REPEAT, --repeat REPEAT\n                        n/i repeat n times every i seconds\n  -f FILTERS [FILTERS ...], --filters FILTERS [FILTERS ...]\n                        Filter the disk paths for disk type; filter the\n                        container names for containers type\n```\n\n**For example:**\n\nShow sysinfo\n\n```bash\n$ aidockermon query -l sysinfo\n{\n    \"gpu\": {\n        \"gpu_num\": 2,\n        \"driver_version\": \"410.104\",\n        \"cuda_version\": \"10.0\"\n    },\n    \"mem_tot\": 67405533184,\n    \"kernel\": \"4.4.0-142-generic\",\n    \"cpu_num\": 12,\n    \"docker\": {\n        \"version\": \"18.09.3\"\n    },\n    \"system\": \"Linux\"\n}\n```\n\nShow sys load\n\n```bash\n$ aidockermon query -l sysload\n{\n    \"mem_free\": 11866185728,\n    \"mem_used\": 8023793664,\n    \"cpu_perc\": 57.1,\n    \"mem_perc\": 12.8,\n    \"mem_avail\": 58803163136,\n    \"mem_tot\": 67405533184\n}\n```\n\nShow gpu load\n\n```bash\n$ aidockermon query -l gpu\n{\n    \"mem_tot\": 11177,\n    \"gpu_temperature\": 76.0,\n    \"mem_free\": 1047,\n    \"mem_used\": 10130,\n    \"gpu_perc\": 98.0,\n    \"gpu_id\": 0,\n    \"mem_perc\": 46.0\n}\n{\n    \"mem_tot\": 11178,\n    \"gpu_temperature\": 66.0,\n    \"mem_free\": 3737,\n    \"mem_used\": 7441,\n    \"gpu_perc\": 95.0,\n    \"gpu_id\": 1,\n    \"mem_perc\": 44.0\n}\n```\n\nShow disk usage\n\n```bash\n$ aidockermon query disk -l -f /\n{\n    \"path\": \"/\",\n    \"device\": \"/dev/nvme0n1p3\",\n    \"total\": 250702176256,\n    \"used\": 21078355968,\n    \"free\": 216865271808,\n    \"percent\": 8.9\n}\n\n$ aidockermon query disk -l -f / /disk\n{\n    \"path\": \"/\",\n    \"device\": \"/dev/nvme0n1p3\",\n    \"total\": 250702176256,\n    \"used\": 21078355968,\n    \"free\": 216865271808,\n    \"percent\": 8.9\n}\n{\n    \"path\": \"/disk\",\n    \"device\": \"/dev/sda1\",\n    \"total\": 1968874311680,\n    \"used\": 1551374692352,\n    \"free\": 317462949888,\n    \"percent\": 83.0\n}\n```\n\nShow containers' load\n\nNote that the `app_name` would be read from environment variable `APP_NAME`, which\nis a short description for this training program.\n\n```bash\n$ aidockermon query containers -l -f DianAI\n{\n    \"proc_name\": \"python3 test_run.py\",\n    \"app_name\": \"\u6d4b\u8bd5\u7a0b\u5e8f\",\n    \"pid\": 13540,\n    \"container\": \"DianAI\",\n    \"started_time\": 1554698236,\n    \"running_time\": 9343,\n    \"mem_used\": 9757\n}\n{\n    \"proc_name\": \"python train.py\",\n    \"app_name\": \"\",\n    \"pid\": 15721,\n    \"container\": \"DianAI\",\n    \"started_time\": 1554698236,\n    \"running_time\": 19343,\n    \"mem_used\": 1497\n}\n{\n    \"mem_limit\": 67481047040,\n    \"net_output\": 47863240948,\n    \"block_read\": 1327175626752,\n    \"net_input\": 18802869033,\n    \"mem_perc\": 14.637655604461704,\n    \"block_write\": 132278439936,\n    \"name\": \"DianAI\",\n    \"cpu_perc\": 0.0,\n    \"mem_used\": 9877643264\n}\n```\n\n## Config\n\n### logging\n```yaml\ndebug: false\nlog:\n  version: 1\n\n  # This is the default level, which could be ignored.\n  # CRITICAL = 50\n  # FATAL = CRITICAL\n  # ERROR = 40\n  # WARNING = 30\n  # WARN = WARNING\n  # INFO = 20\n  # DEBUG = 10\n  # NOTSET = 0\n  #level: 20\n  disable_existing_loggers: false\n  formatters:\n    simple:\n      format: '%(levelname)s %(message)s'\n    monitor:\n      format: '%(message)s'\n  filters:\n    require_debug_true:\n      (): 'aidockermon.handlers.RequireDebugTrue'\n  handlers:\n    console:\n      level: DEBUG\n      class: logging.StreamHandler\n      formatter: simple\n      filters: [require_debug_true]\n    monitor:\n      level: INFO\n      class: rfc5424logging.handler.Rfc5424SysLogHandler\n      address: [127.0.0.1, 1514]\n      enterprise_id: 1\n  loggers:\n    runtime:\n      handlers: [console]\n      level: DEBUG\n      propagate: false\n    monitor:\n      handlers: [monitor, console]\n      level: INFO\n      propagate: false\n\n```\n\nThis is the default config, which should be located at `/etc/aidockermon/config.yml`.\n\nYou can modify the `address` value to specify the logging target.\n- `address: [127.0.0.1, 1514]`: UDP to 127.0.0.1:1514\n- `address: /var/log/aidockermon`: unix domain datagram socket\n\nIf you add an `socktype` argument, you can specify whether to use UDP or TCP as transport protocol.\n- `socktype: 1`: TCP\n- `socktype: 2`: UDP\n\nEnable TLS/SSL:\n```yaml\ntls_enable: true\ntls_verify: true\ntls_ca_bundle: /path/to/ca-bundle.pem\n```\n\nSet `debug` as `true`, you can see message output in the console.\n\n### Cronjob\n\n```bash\nsudo cp etc/cron.d/aidockermon /etc/cron.d\nsudo systemctl restart cron\n```\n\n### syslog-ng\n\nUsing syslog-ng to collect logs and send them to elasticsearch \nfor future use such as visualization with kibana.\n\n```bash\ncp etc/syslog-ng/syslog-ng.conf /etc/syslog-ng/\nsudo systemctl restart syslog-ng\n```\n\nSample config:\n\n```bash\n@version: 3.20\n\ndestination d_elastic {\n\telasticsearch2(\n\t\tindex(\"syslog-ng\")\n\t\ttype(\"${.SDATA.meta.type}\")\n\t\tflush-limit(\"0\")\n\t\tcluster(\"es-syslog-ng\")\n\t\tcluster-url(\"http://localhost:9200\")\n\t\tclient-mode(\"http\")\n\t\tclient-lib-dir(/usr/share/elasticsearch/lib)\n\t\ttemplate(\"${MESSAGE}\\n\")\n\t);\n};\n\nsource s_python {\n  #unix-dgram(\"/var/log/aidockermon\");\n\tsyslog(ip(127.0.0.1) port(1514) transport(\"udp\") flags(no-parse));\n};\n\nlog {\n\tsource (s_python);\n  parser { syslog-parser(flags(syslog-protocol)); };\n\tdestination (d_elastic);\n};\n```\nModify it to specify the elasticsearch server and the log source's port and protocol.\n\n\n\n",
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