{
    "info": {
        "author": "Lucas Liu",
        "author_email": "llychinalz@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
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            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
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        "description": "# Torch-Scope\n\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Documentation Status](https://readthedocs.org/projects/tensorboard-wrapper/badge/?version=latest)](http://tensorboard-wrapper.readthedocs.io/en/latest/?badge=latest)\n[![Downloads](https://pepy.tech/badge/torch-scope)](https://pepy.tech/project/torch-scope)\n[![PyPI version](https://badge.fury.io/py/torch-scope.svg)](https://badge.fury.io/py/torch-scope)\n\nA Toolkit for training pytorch models, which has three features:\n\n- Tracking environments, dependency, implementations and checkpoints;\n- Providing a logger wrapper with two handlers (to ```std``` and ```file```);\n- Supporting automatic device selection;\n- Providing a tensorboard wrapper;\n- Providing a spreadsheet writer to automatically summarizing notes and results;\n\nWe are in an early-release beta. Expect some adventures and rough edges.\n\n## Quick Links\n\n- [Installation](#installation)\n- [Usage](#usage)\n\n## Installation\n\nTo install via pypi:\n```\npip install torch-scope\n```\n\nTo build from source:\n```\npip install git+https://github.com/LiyuanLucasLiu/Torch-Scope\n```\nor\n```\ngit clone https://github.com/LiyuanLucasLiu/Torch-Scope.git\ncd Torch-Scope\npython setup.py install\n```\n\n## Usage\n\nAn example is provided as below, please read the doc for a detailed api explaination.\n\n* set up the git in the server & add all source file to the git\n* use tensorboard to track the model stats (tensorboard --logdir PATH/log/ --port ####)\n\n```\nfrom torch_scope import wrapper\n...\nlogger = logging.getLogger(__name__)\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--checkpoint_path', type=str, ...)\n    parser.add_argument('--name', type=str, ...)\n    parser.add_argument('--gpu', type=str, ...)\n    ...\n    args = parser.parse_args()\n\n    pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, enable_git_track = False)\n    # Or if the current folder is binded with git, you can turn on the git tracking as below\n    # pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, enable_git_track = True)\n    # if you properly set the path to credential_path and want to use spreadsheet writer, turn on sheet tracking as below\n    # pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, \\\n    #             enable_git_track=args.git_tracking, sheet_track_name=args.spreadsheet_name, \\ \n    #             credential_path=\"/data/work/jingbo/ll2/Torch-Scope/torch-scope-8acf12bee10f.json\")\n    \n    \n    gpu_index = pw.auto_device() if 'auto' == args.gpu else int(args.gpu)\n    device = torch.device(\"cuda:\" + str(gpu_index) if gpu_index >= 0 else \"cpu\")\n\n    pw.save_configue(args) # dump the config to config.json\n\n    # if the spreadsheet writer is enabled, you can add a description about the current model\n    # pw.add_description(args.description) \n\n    logger.info(str(args)) # would be plotted to std & file if level is 'info' or lower\n\n    ...\n\n    batch_index = 0\n\n    for index in range(epoch):\n\n    \t...\n\n    \tfor instance in ... :\n\n    \t\tloss = ...\n\n    \t\ttot_loss += loss.detach()\n    \t\tloss.backward()\n\n    \t\tif batch_index % ... = 0:\n    \t\t\tpw.add_loss_vs_batch({'loss': tot_loss / ..., ...}, batch_index, False)\n    \t\t\tpw.add_model_parameter_stats(model, batch_index, save=True)\n    \t\t\toptimizer.step()\n    \t\t\tpw.add_model_update_stats(model, batch_index)\n    \t\t\ttot_loss = 0\n    \t\telse:\n    \t\t\toptimizer.step()\n\n    \t\tbatch_index += 1\n\n    \tdev_score = ...\n    \tpw.add_loss_vs_batch({'dev_score': dev_score, ...}, index, True)\n\n    \tif dev_score > best_score:\n    \t\tpw.save_checkpoint(model, optimizer, is_best = True)\n    \t\tbest_score = dev_score\n    \telse:\n    \t\tpw.save_checkpoint(model, optimizer, is_best = False)\n```\n\n## Advanced Usage\n\n### Auto Device\n\n### Git Tracking\n\n### Spreadsheet Logging\n\nShare the spreadsheet with the following account ```torch-scope@torch-scope.iam.gserviceaccount.com```. And access the table with its name.",
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