Tensorflow Training Notifier

A python package which periodically notifies you of your Tensorflow job's progress.

Installation

The python package can be installed via pip.

pip install tf-training-notifier

Setup

The package relies on two envionrmnet variables ("TRAINING_NOTIFIER_TOKEN" and "TRAINING_NOTIFIER_CHAT_ID") being set. These variables point towards the Telegram bot token and the Telegram chat ID. Instructions on how to get and set those up can be found here.

Usage

Setting up the notifier

import trainingnotifier
EPOCHS = 15
# The default notifier which only notifies the user when the job is completed.
notifier = trainingnotifier.NotifierCallback(EPOCHS)
model.fit(x_train, y_train, epochs=EPOCHS, callbacks=[notifier])

# Creates a notifier that will update the user ONCE (if the job takes more than 2 minutes) 
#  when the job is done. It will also report the loss in this message.
notifier = trainingnotifier.NotifierCallback(EPOCHS, min_time="2m", metrics=["loss"])

# Creates a new notifier that will notify the user, at soonest, every 10 minutes with 
#  the jobs ETA and loss values. If the job takes longer than 10 minutes than it will send 
#  a notification that the job is done
notifier = trainingnotifier.NotifierCallback(EPOCHS, epoch_updates=True, min_time="10m", metrics=["loss"])

To test if the system was setup properly.

# This function tests the notification system. If the target user recieves a Telegram test 
#  message, then the enviornment variables have been set correctly.
trainingnotifier.test_notifier()