Treescope#
Treescope is an interactive HTML pretty-printer and N-dimensional array (“tensor”) visualizer, designed for machine learning and neural networks research in IPython notebooks. It’s a drop-in replacement for the standard IPython/Colab renderer, and adds support for:
Expanding and collapsing subtrees of rendered objects, to let you focus on the parts of your model that you care about,
Automatically embedding faceted visualizations of arbitrary-dimensional arrays and tensors directly into the output renderings, so you can quickly understand their shapes and the distribution of their values,
Color-coding parts of neural network models to emphasize shared structures,
Inserting “copy path” buttons that let you easily copy the path to any part of a rendered object,
Customizing the visualization strategy to support rendering your own data structures,
And more!
Treescope was originally developed as the pretty-printer for the Penzai neural network library, but it also supports rendering neural networks developed with other libraries, including Equinox, Flax NNX, and PyTorch. You can also use it with basic JAX and Numpy code.
With Treescope, instead of looking at this:
You could be looking at this:
Or even this:
These are interactive visualizations; try clicking the ▶
buttons to expand
parts of the output! (You can also hold shift while scrolling to
scroll horizontally instead of vertically.)
Getting Started#
You can install Treescope using
pip install treescope
and import it using
import treescope
To render a specific object in an IPython notebook with Treescope, you can use
treescope.show
, which is like print
but produces a rich interactive output.
Alternatively, you can simply configure Treescope as the default pretty printer for your notebook via
treescope.register_as_default()
To turn on automatic array visualization, you can run
treescope.active_autovisualizer.set_globally(treescope.ArrayAutovisualizer())
Or, if you’d like to both set up Treescope as the default pretty printer and enable automatic array visualization, you can simply run
treescope.basic_interactive_setup(autovisualize_arrays=True)
Once you’ve rendered an object, try clicking on it and pressing the r
key!
This turns on “roundtrip mode”, and adds qualified names to every type in the
visualization, making it easier to identify what the types in your object are.
Looking for a neural network library with first-class support for Treescope’s visualization features? Try Penzai!
Citation#
If you have found Treescope to be useful for your research, please consider citing the following writeup (also available on arXiv):
@article{johnson2024penzai,
author={Daniel D. Johnson},
title={{Penzai} + {Treescope}: A Toolkit for Interpreting, Visualizing, and Editing Models As Data},
year={2024},
journal={ICML 2024 Workshop on Mechanistic Interpretability}
}
License#
Treescope is licensed under the Apache 2.0 License.