Metadata-Version: 2.1
Name: keras-bucket-tensorboard-callback
Version: 1.0.4
Summary: A Keras Callback that uploads your Tensorboard logs to a Cloud Bucket
Home-page: https://github.com/neuronio-ai/keras-bucket-tensorboard-callback
Author: Adriano Dennanni
Author-email: adriano.dennanni@gmail.com
License: UNKNOWN
Description: # Keras Bucket Tensorboard Callback
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![PyPI version](https://badge.fury.io/py/keras-bucket-tensorboard-callback.svg)](https://badge.fury.io/py/keras-bucket-tensorboard-callback)
        [![Maintainability](https://api.codeclimate.com/v1/badges/74540030ca0b44ff2dfb/maintainability)](https://codeclimate.com/github/neuronio-ai/keras_bucket_tensorboard_callback/maintainability)
        [![Test Coverage](https://api.codeclimate.com/v1/badges/74540030ca0b44ff2dfb/test_coverage)](https://codeclimate.com/github/neuronio-ai/keras_bucket_tensorboard_callback/test_coverage)
        
        A Keras Callback that uploads your Tensorboard logs to a Cloud Bucket
        
        *Currently, only Google Cloud Platform Storage is supported. Very little effort
        is needed to support AWS S3, so feel free to contribute to this project.*
        
        ## Installation
        ```bash
        pip install keras-bucket-tensorboard-callback
        ```
        
        ## Basic usage
        
        The following example trains uploads the Tensorboard logs to you GCP Storage
        bucket `my-bucket`, inside the directory `any_dir`:
        
        ```python
        # Import the class
        from keras_bucket_tensorboard_callback import BucketTensorBoard
        
        # Create the callback instance, passing the bucket URI
        bucket_callback = BucketTensorBoard('gs://my-bucket/any_dir')
        
        # Train the model with the callback
        model.fit(
            x=X,
            y=Y,
            epochs=20,
            callbacks=[bucket_callback]
        )
        ```
        
        Make sure you have access to the provided bucket. For GCP, you should have
        the `GOOGLE_APPLICATION_CREDENTIALS` env set, pointing to your `json` key
        file.
        
        ## Viewing the results on TensorBoard
        With tensorboard installed your environment, run:
        ```bash
        tensorboard --logdir=gs://my-bucket/any_dir
        ```
        
        The TensorBoard will show your metrics and graphs saved on the bucket.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
