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        "description": "# Move-carrots\n\nSimple helper for reproducible (research) experiments.\n\n## Goals\n\nThis package aims to make one's life easier with machine learning experiments.\nTo achieve this goal, this package basically does the following two things.\n\n### 1. Run experiments with some config files. \n\n- No need for long commands. \n- Reliable and elegant than notebooks.\n\n### 2. Built-in support to record, view, and reproduce your experiments.\n\n:P)\n\n## Quick start\nWIP.\n\n## Core features\n\n1. Define your experiment in a human friendly YAML file: main function, parameters, data files, logging, etc.\n2. Codes will be automatically checked in to git with proper tags. Datas will be also version-controlled in a proper manner. \n3. Each experiment will be dumped into a database, including the config, the logging, and the versions of code and data.\n\n## Design\n\n### Config file\nThe config file includes:\n- PYTHONPATH\n- main function and parameters\n- misc\n    - experiment name\n    - logging\n\n### Version control\nThe goal is to re-run one experiment within one command. To achieve this, we will have both codes and data versioned in a proper way.\n\n**Codes** All local changes, if there are any, will be committed into Git before a config runs. Commits information will be stored in log files and the head commit will be tagged as f\"{name}@{time}\" for future reference. \n\n**Data** Data will be stored in the database, which will be keyed by name and md5 value. \n\n### Database\n\nThe database has two parts: experiment logging and version control. \n\n1. **Experiment logging** shows what experiments have been conducted, what's the input/output of one experiment. Based on it, more analysis can be elaborated.\n2. **Version control** is responsible for recording all versions of codes (through Git) and data (local storage).\n\n\n",
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        "license": "MIT",
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        "name": "move-carrots",
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