trainplot 0.4.0

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

trainplot

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

pip install trainplot

Usage

Basic usage:

from trainplot import plot

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

If you use keras, you can use the TrainPlotKerasCallback:

from trainplot import TrainPlotKeras

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.