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    "info": {
        "author": "Adriano Dennanni",
        "author_email": "adriano.dennanni@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# Keras Bucket Tensorboard Callback\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![PyPI version](https://badge.fury.io/py/keras-bucket-tensorboard-callback.svg)](https://badge.fury.io/py/keras-bucket-tensorboard-callback)\n[![Maintainability](https://api.codeclimate.com/v1/badges/74540030ca0b44ff2dfb/maintainability)](https://codeclimate.com/github/neuronio-ai/keras_bucket_tensorboard_callback/maintainability)\n[![Test Coverage](https://api.codeclimate.com/v1/badges/74540030ca0b44ff2dfb/test_coverage)](https://codeclimate.com/github/neuronio-ai/keras_bucket_tensorboard_callback/test_coverage)\n\nA Keras Callback that uploads your Tensorboard logs to a Cloud Bucket\n\n*Currently, only Google Cloud Platform Storage is supported. Very little effort\nis needed to support AWS S3, so feel free to contribute to this project.*\n\n## Installation\n```bash\npip install keras-bucket-tensorboard-callback\n```\n\n## Basic usage\n\nThe following example trains uploads the Tensorboard logs to you GCP Storage\nbucket `my-bucket`, inside the directory `any_dir`:\n\n```python\n# Import the class\nfrom keras_bucket_tensorboard_callback import BucketTensorBoard\n\n# Create the callback instance, passing the bucket URI\nbucket_callback = BucketTensorBoard('gs://my-bucket/any_dir')\n\n# Train the model with the callback\nmodel.fit(\n    x=X,\n    y=Y,\n    epochs=20,\n    callbacks=[bucket_callback]\n)\n```\n\nMake sure you have access to the provided bucket. For GCP, you should have\nthe `GOOGLE_APPLICATION_CREDENTIALS` env set, pointing to your `json` key\nfile.\n\n## Viewing the results on TensorBoard\nWith tensorboard installed your environment, run:\n```bash\ntensorboard --logdir=gs://my-bucket/any_dir\n```\n\nThe TensorBoard will show your metrics and graphs saved on the bucket.\n\n",
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        "name": "keras-bucket-tensorboard-callback",
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