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        "author": "Joao Ramos",
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        "description": "# Atalaya\n\nThis [framework](https://pypi.org/project/atalaya/) provides a logger for pytorch models, it allows you to save the parameters, the state of the network, the state of the optimizer and allows also to save and visualize your data using tensorboardX or visdom.\n\n- [Install](#install)\n- [Examples](#Examples)\n- [Usage](#usage)\n  - [Init](#init)\n  - [Log Information](#Log-Information)\n  - [Store your Parameters](#Store-your-Parameters)\n  - [Store and Restore (models and optimizers)](<#Store-and-Restore-(models-and-optimizers)>)\n  - [Grapher](#Grapher)\n\n## Install\n\n```bash\n$ pip install atalaya\n```\n\n## Example\n\nAn example is provided [here](https://github.com/jacr13/Atalaya/tree/master/example).\n\nLaunch the example doing :\n\n```bash\n$ ./run.sh\n```\n\nAn example of logs produced by the logger are given in the [logs](https://github.com/jacr13/Atalaya/tree/master/example/logs) folder of the example.\n\n## Usage\n\n### Init\n\n```python\nfrom atalaya import Logger\n\nlogger = Logger(\n    name=\"exp\",         # name of the logger\n    path=\"logs\",        # path to logs\n    verbose=True,       # logger in verbose mode\n)\n\n# by default Logger uses no grapher\n# you can setup it by specifying if you want visdom or tensorboardX\nlogger = Logger(\n    name=\"exp\",         # name of the logger\n    path=\"logs\",        # path to logs\n    verbose=True,       # logger in verbose mode\n    grapher=\"visdom\",\n    server=\"http://localhost\",\n    port=8097,\n    username=\"user\",    # if needed for visdom\n    password=\"pwd\",     # if needed for visdom\n)\n\n# your code here\n...\n\n# to close the logger\nlogger.close()\n```\n\n### Log Information\n\n```python\n# logs information in console and in log file.\nlogger.info(\"your\", \"information\", \"here\", \"like\", \"a\", \"print\")\n\n# same as logger.info but for warning messages\nlogger.warning(\"your warning\")\n```\n\n### Store your Parameters\n\n```python\n# save your parameters into a json file\nlogger.add_parameters(args)\n\n# load the parameters froma previous experiment\nlogger.restore_parameters(path_to_folder)\n```\n\n### Store and Restore (models and optimizers)\n\n1. Add the model (or optimizer or whatever that has a state_dict in pytorch)\n\n   Before starting storing or restoring objects you need to add them to the logger:\n\n   ```python\n       logger.add(\"model\", model)\n       logger.add(\"optimizer\", optimizer)\n   ```\n\n2. Store the model\n\n   In training loop we can add this method, it allows us to save checkpoints,\n   and the best model.\n\n   - The parameter valid_loss is simply the parameter to know when to save the best. It looks if\n     the new valid_loss is less than the value keep by the logger as lower if it's the case save as best and\n     update the value keep in memory.\n   - The parameter save_every specify how often to save a checkpoint during training.\n   - overwrite specify if we want to overwrite the last checkpoint to keep only the last one\n     or if we want to keep them all (saves a model per epoch --> DANGEROUS)\n\n   ```python\n       logger.store(valid_loss, save_every=1, overwrite=True)\n\n   ```\n\n3. Restore the model\n   To restore the best after taining simply do\n\n   ```python\n       logger.restore(best=True)\n   ```\n\n   To restore a checkpoint from another exp :\n\n   ```python\n       logger.restore(folder=path_to_folder)\n   ```\n\n   To restore the best from another exp :\n\n   ```python\n       logger.restore(folder=path_to_folder, best=True)\n   ```\n\n### Grapher\n\nSome examples of grapher methods.\n\n```python\nlogger.add_scalar(\"train_mse\", scalar_value, global_step=None, save_csv=True)\n\nlogger.add_text(\"tag\", \"your text here\")\n\n# values for each batch size at a given epoch\nvalues = {\n    \"mse\": [10, 9, 8, 7],\n    \"acc\": [0.3, 0.5, 0.55, 0.6]\n}\nlogger.register_plots(values, epoch, \"train\", apply_mean=True, save_csv=True, info=True)\n```\n\n\n",
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