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Meet MLNotify: Get Notified the Second Your Training is Done

Meet MLNotify
Dor Schwartz Dor Schwartz 3 min read Aug 24, 2021

MLNotify is an open-source tool that notifies you when model training is complete via web, mobile, and email notifications – with just one import line.


pip3 install mlnotify
# STEP 1: Import package
import mlnotify

# STEP 2: Do your thing.

# STEP 3: Open https://mlnotify.aporia.com
#         Enter training ID, and get notified when training is done!

After training hundreds of models, every data scientist knows that waiting for your training to end is tedious. It takes a long time and requires you to Alt+Tab back and forth every now and then to check up on it.

I thought it might be cool to have a tool that will do the waiting for me and send me a notification once it’s done.

Introducing MLNotify

MLNotify is an open-source tool that watches model training for you and sends a notification once training is complete.

Code is available on GitHub.


To try MLNotify, we’ve prepared a quick snippet for you to copy-paste:

import mlnotify
from sklearn import datasets, svm

iris = datasets.load_iris()

print("Training model...")

clf = svm.SVC(tol=1e-100, max_iter=10 ** 9)
clf.fit(X=iris.data, y=iris.target)


This should train a simple model for a few minutes. Once training begins, MLNotify will print your unique tracking URL for it:

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You can scan the QR, copy the URL, or go to https://mlnotify.aporia.com and enter the code.

You’ll then see the progress of your training. You can enable web, mobile, or email notifications to know exactly when your training finishes.

MLNotify waiting for training

When training ends, you’ll be notified automatically!

Product design

Hurray! No more switching tabs and manually checking for training 🙂

How MLNotify Works

MLNotify is a Python library that hooks into the fit() method of popular ML libraries, and notifies upon completion of the method execution.

See supported libraries here or use the manual API if your library is not supported. 

The library features a plugin system to extend its functionality. 

Now the important question, what should you do while training?

Now that we’ve cleared a lot of time, what should we do with it? It’s an important issue that’s been pondered and debated by data scientists since the beginning of time.

Some suggestions:

Contributing to MLNotify

We have plenty of ideas for MLNotify’s roadmap, but we would love to get your help!

The project is available on GitHub: https://github.com/aporia-ai/mlnotify so please feel free to add new features and capabilities.

Have fun!

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