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
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: google-cloud-storage
Requires-Dist: Keras

# Keras Bucket Tensorboard Callback

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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.

