https://github.com/techytushar/tf_notification_callback

TensorFlow/Keras Callback for receiving notifications
https://github.com/techytushar/tf_notification_callback

deep-learning keras machine-learning python3 tensorflow

Last synced: 10 days ago
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TensorFlow/Keras Callback for receiving notifications

• Host: GitHub
• URL: https://github.com/techytushar/tf_notification_callback
• Owner: techytushar
• Created: 2020-02-15T12:06:09.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-01-20T21:23:24.000Z (almost 4 years ago)
• Last Synced: 2024-10-15T20:17:25.199Z (22 days ago)
• Topics: deep-learning, keras, machine-learning, python3, tensorflow
• Language: Python
• Homepage: https://pypi.org/project/tf-notification-callback/0.2/
• Size: 4.88 KB
• Stars: 10
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# TensorFlow Notification Callback

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)

[![PyPI version](https://badge.fury.io/py/tf-notification-callback.svg)](https://badge.fury.io/py/tf-notification-callback)

A Tensorflow/Keras callback which sends information about your model training, on various messaging platforms.

## Installation

Using `pip`:

```bash

pip install tf_notification_callback

```

## Usage

Import the required module and add it to the list callbacks while training your model.

**Example:**

```python

>>> from tf_notification_callback import TelegramCallback

>>> telegram_callback = TelegramCallback('',

                                         '',

	                                 'CNN Model',

	                                 ['loss', 'val_loss'],

	                                 ['accuracy', 'val_accuracy'],

	                                 True)

>>> model.fit(x_train, y_train,

              batch_size=32,

              epochs=10,

              validation_data=(x_test, y_test),

              callbacks=[telegram_callback])

```

### Telegram

1. Create a telegram bot using BotFather

	* Search for @BotFather on telegram.

	* Send `/help` to get list of all commands.

	* Send `/newbot` to create a new bot and complete the setup.

	* Copy the **bot token** after creating the bot.

2. Get the **chat ID**

	* Search for the bot you created and send it any random message.

	* Go to this URL `https://api.telegram.org/bot/getUpdates` (replace  with your bot token)

	* Copy the `chat id` of the user you want to send messages to.

3. Use the `TelegramCallback()` class.

```python

TelegramCallback(bot_token=None, chat_id=None, modelName='model', loss_metrics=['loss'], acc_metrics=[], getSummary=False):

```

**Arguments:**

* `bot_token` : unique token of Telegram bot `{str}`

* `chat_id` : Telegram chat id you want to send message to `{str}`

* `modelName` : name of your model `{str}`

* `loss_metrics` : loss metrics you want in the loss graph `{list of strings}`

* `acc_metrics` : accuracy metrics you want in the accuracy graphs `{list of strings}`

* `getSummary` : Do you want message for each epoch (False) or a single message containing information about all epochs (True). `{bool}`

### Slack

1. Create a Slack workspace

2. Create a new channel

3. Search for the **Incoming Webhooks** in the Apps and install it.

4. Copy the **Webhook URL**

5. Use the `SlackCallback()` class.

```python

SlackCallback(bot_token=None, chat_id=None, modelName='model', loss_metrics=['loss'], acc_metrics=[], getSummary=False):

```

**Arguments:**

* `webhookURL` : unique webhook URL of the app `{str}`

* `channel` :  channel name or username you want to send message to `{str}`

* `modelName` : name of your model `{str}`

* `loss_metrics` : loss metrics you want in the loss graph `{list of strings}`

* `acc_metrics` : accuracy metrics you want in the accuracy graph `{list of strings}`

* `getSummary` : Do you want message for each epoch (False) or a single message containing information about all epochs (True). `{bool}`

*Sending images in Slack is not supported currently.*

## ToDo

* WhatsApp

* E-Mail

* Zulip

* Messages

## Motivation

As the Deep Learning models are getting more and more complex and computationally heavy, they take a very long time to train. During my internship, people used to start the model training and left it overnight. They could only check its progress the next day. So I thought it would be great if there was a simple way to get the training info remotely on their devices.