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TensorFlow Notification Callback

forthebadge made-with-python

PyPI version

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

Installation

Using pip:

pip install tf_notification_callback

Usage

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

Example:

>>> from tf_notification_callback import TelegramCallback
>>> telegram_callback = TelegramCallback('<BotToken>',
                                         '<ChatID>',
	                                 '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<BOT_TOKEN>/getUpdates (replace <BOT_TOKEN> with your bot token)
    • Copy the chat id of the user you want to send messages to.
  3. Use the TelegramCallback() class.
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.
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.