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        "author": "Jogle Lew",
        "author_email": "author@example.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development :: Libraries :: Application Frameworks",
            "Topic :: Utilities"
        ],
        "description": "# Nine Six\n\nUnified tools for Neural Network logging and managing.\n\n## Quick Start\n\nUse pip to install Nine Six modules.\n\n```\npip3 install ninesix\n```\n\n### Pretty Logging \n\n```python\nfrom ninesix import Logger # import logger\nlogger = Logger(\"example_nn\") # Initialize logger\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser() # Define your argument parser\n    ... \n    args = parser.args()\n    args = logger.config(args, \"argparse\") # Wrap the argparse object to log config\n\n    logger.msg(\"We're going to start training...\") # Log text message\n    ... # Prepare your model\n    for epoch in range(args.epoch):\n        logger.progress(\"epoch\", epoch + 1, total=args.epoch) # Record epoch as a progress variable\n        ... # Train your model\n        logger.value({\"loss\": loss, \"f1\": f1}) # Log value(s) associated with progress variable(s)\n        ...\n    logger.unwatch(\"epoch\") # Unregister progress variable\n    ...\n    logger.value({\"final_f1\": final_f1}) # Log value(s)\n    logger.msg(\"All done, have fun!\") # Log text message\n```\nThe output you will get at stdout:\n```\n2019-05-14 17:29:09 [Log] (log.py: 44 in __init__()):\nLogger [example_nn] Initialized.\n\n2019-05-14 17:29:09 [Config] (example.py: 189 in <module>()):\n{\n    \"epoch\": 300,\n    \"lr\": 0.01,\n    \"lr_decay\": 0.0001\n}\n\n2019-05-14 17:29:11 [Log] (example.py: 171 in <module>()):\nWe're going to start training...\n\n2019-05-14 17:29:22 [Log] (example.py: 237 in <module>()):\nepoch     : 300 / 300                                       \n------------------------------------------------------------------\nloss: 0.22311973571777344  f1: 90.58  \n\n2019-05-14 17:29:22 [Log] (example.py: 247 in <module>()):\nfinal_f1: 90.58\n\n2019-05-14 17:29:11 [Log] (example.py: 371 in <module>()):\nAll done, have fun!\n```\n\nAt the same time, you'll get a JSON log file `~/96log/example_nn/2019-05-14/172909.json`:\n\n```json\n[{\"type\": \"msg\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:09\", \"content\": \"JSON Writer Initialized.\"},\n{\"type\": \"config\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:09\", \"content\": {\"lr\": 0.01, \"lr_decay\": 0.0001, \"epoch\": 300}},\n{\"type\": \"msg\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:11\", \"content\": \"We're going to start training...\"},\n{\"type\": \"value\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:12\", \"content\": {\"progress\": {\"epoch\": {\"current\": 50, \"max\": 300}}, \"value\": {\"loss\": 5.2108306884765625, \"f1\": 73.26}}},\n{\"type\": \"value\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:13\", \"content\": {\"progress\": {\"epoch\": {\"current\": 100, \"max\": 300}}, \"value\": {\"loss\": 1.3068847656252345, \"f1\": 85.81}}},\n...\n{\"type\": \"value\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:22\", \"content\": {\"progress\": {\"epoch\": {\"current\": 300, \"max\": 300}}, \"value\": {\"loss\": 0.22311973571777344, \"f1\": 90.58}}},\n{\"type\": \"value\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:22\", \"content\": {\"progress\": {}, \"value\": {\"final_f1\": 90.58}}},\n{\"type\": \"msg\", \"tag\": \"Log\", \"time\": \"2019-05-14 17:29:11\", \"content\": \"All done, have fun!\"}]\n```\n\nYou can try `example.py` which is a complete example.\n\n### Grid Search\n\nCommand Line: \n```\n96grid -p lr 0.1 0.01 0.001 -p epoch 100 to 201 jump 50 -g 1 2 3 -c \"python example.py\"\n```\n\nYou can use `python3 ninesix/tool/grid.py -h` for more help.\n\nPython code:\n\n```python\nfrom ninesix import GridSearch\n\ngs = GridSearch()\n# set environment settings\ngs.cmd_env = \"\"\n# set default command\ngs.cmd_template = \"python3 example.py\" \n# set available GPUs\ngs.gpus = [0]\n# set parameter options\ngs.param_grid = {\n    \"lr\": [0.1, 0.01, 0.001],\n    \"epoch\": [100, 150, 200]\n}\n# generate scripts\ngs.generate_grid_search()\n# execute scripts\ngs.execute_script()\n```\n\n\n",
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