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        "description": "# Mode Rage client\n\n[![PyPI version](https://badge.fury.io/py/moderage-python.svg)](https://badge.fury.io/py/moderage-python)\n\n## What is ModeRage\n\nModeRage is a light-weight tool for storing experimental results and models. Experiments are referenced by their \n*metacategory* and *id*.\n\n### Meta Categories\n\nExperiments in ModeRage have a meta-category, which basically defines the *type* of experiment. \nThink of meta-categories as an identifier for a project that may contain many experiments or datasets of the same type.\n\nFor example, when running many experiments with several sets of hyperparameters, those experiments will be saved into \nthe same meta-category.\n\n### Ids\n\nOnce an experiment is saved it has an *id*. This can be used to load the experiment.\n\n## Configuration\n\nModeRage can be started in `local` or `server` mode.\n\n### Local\n\nIn local mode, ModeRage will save files locally to a `~/.moderage` folder\n\n### Server\n\nThe ModeRage Server hosts experiments, data and metadata so it can be access from anywhere. \n\nYou can view it here (CURRENTLY UNDER DEVELOPMENT):\n[Server](https://gitlab.com/chrisbam4d/modage-backend)\n\n#### UI\n\nThe ModeRage UI communicates with the ModeRage server and allows browsing of experiments and data\n\nYou can view it here (CURRENTLY UNDER DEVELOPMENT):\n[UI](https://gitlab.com/chrisbam4d/moderage-ui)\n\n## Configuration file\n\nConfiguration in ModeRage is defined in a `.mrconfig` file. If no config file is created, \nModeRage will automatically start in `local` Mode\n\n## Saving results\n\nTo save any number of files with some meta data you do the following:\n\n### 1. Define a Meta object\n\n```python\nmymeta = {\n    'hyperparameter1': 100,\n    'hyperparameter2': 200,\n    'hyperparameter3': 0.7,\n}\n```\n\n### 2. (Optional) Define any files you want to upload\n\n```python\nmyfiles = [\n    {\n        'filename': './path/to/myfile.csv',\n        'caption': 'This is my file that contains my results'\n    },\n    {\n        'filename': './path/to/mygraph.png',\n        'caption': 'This is my file that contains my graph'\n    },\n    ...\n]\n```\n\n### 4. (Optional) Reference any other experiments that this experiment is dependent on.\n\nIn many situations your experiment may rely on generated datasets or pre-trained models that also have many hyper-parameters.\nYou can reference those `parent` experiments by adding them to the parent object\n\n```python\nmyparents = [\n    {\n        'id': [THE ID OF THE PARENT EXPERIEMENT],\n        'metaCategory': 'generated_dataset'\n    },\n    {\n        'id': [THE ID OF THE PARENT EXPERIEMENT],\n        'metaCategory': 'pretrained_model'\n    }\n]\n```\n\n### 5. Call `save`\n\n```python\nexperiment = mr.save('category_name', mymeta, files=myfiles)\n```\n\n## Loading results\n\nLoading a saved experiment is simple, you just need to know the *meta-category* and the *id* of the experiment.\n\n```python\nexperiment = mr.load(id, meta_category)\n```\n\nOnce the experiment is loaded, the meta information and files from the experiment can be accessed.\n\nFor example: \n```python\nmeta = experiment.meta\nparents = experiment.parents\nfiles = experiment.files\n\nfile = experiment.get_file('mygraph.png')\n```\n\n\n\n",
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