Counting 3,834 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Last Commit
May. 17, 2019
Mar. 10, 2018

Live Loss Plot

PyPI version PyPI license PyPI status Downloads

Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training!

A live training loss plot in Jupyter Notebook for Keras, PyTorch and other frameworks. An open source Python package by Piotr Migdał, and others. Open for collaboration! (Some tasks are as simple as writing code docstrings, so - no excuses! :))

from livelossplot.keras import PlotLossesCallback, Y_train,
          validation_data=(X_test, Y_test),

So remember, log your loss!

  • (The most FA)Q: Why not TensorBoard?
  • A: Jupyter Notebook compatibility (for exploration and teaching). Simplicity of use.


To install this verson from PyPI, type:

pip install livelossplot

To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:

pip install git+git://


Look at notebook files with full working examples:


Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting?

Visual feedback allows us to keep track of the training process. Now there is one for Jupyter.

If you want to get serious - use TensorBoard or even better - Neptune - Machine Learning Lab (as it allows to compare between models, in a Kaggle leaderboard style). Or, well use tensorboard_dir="./logs" or target='neptune'. Now these are included as well!

But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using livelossplot as a plug&play component.

It started as this gist. Since it went popular, I decided to rewrite it as a package.

To do

If you want more functionality - open an Issue or even better - prepare a Pull Request.

Latest Releases
 May. 6 2019
 Mar. 4 2019
 Jan. 1 2019
 Dec. 31 2018
 Nov. 27 2018