trainplot

Dynamically updating plots in Jupyter notebooks, e.g. for visualizing machine learning training progress.

pip install trainplot

Usage

Basic usage (in a Jupyter notebook):

from trainplot import plot

for i in range(100):
    loss = ...
    acc = ...
    plot(loss=loss, accuracy=acc)

You can also update plots from a different cell or add extra configuration options.

# %%
from trainplot import TrainPlot
tp = TrainPlot(update_period=0.2)

# %%
# New cell

from time import sleep
for i in range(100):
    tp(loss = 1/(i+1), acc = 1-1/(.01*i**2+1))
    sleep(0.05)

For keras, you can use the TrainPlotKerasCallback:

from trainplot import TrainPlotKerasCallback

model = ...
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[TrainPlotKerasCallback()])

For more examples, see the examples folder.

Features

• Lightweight: No external plotting dependencies
• Custom rendering: Uses HTML5 Canvas for fast, smooth updates
• Multiple series: Automatically handles multiple data series with different colors
• Real-time updates: Configurable update periods to balance performance and responsiveness
• Keras support: Built-in callback for TensorFlow/Keras models

How it works

Trainplot uses a custom HTML5 Canvas-based plotting solution that renders directly in Jupyter notebooks. For synchronization between Python and the JavaScript-based plotting function, anywidget is used. To avoid wasting resources and flickering, the plot is only updated with a given update_period. A post_run_cell callback is added to the IPython instance, so that all updated TrainPlot figures include all new data when a cell execution is finished. When using trainplot.plot, a TrainPlot object is created for the current cell.

Trainplot supports various notebook environments, including Jupyter Notebook, Jupyter Lab, VS Code Notebooks, and Google Colab.