Community-maintained fork: This is a community-maintained fork of Facebook Research's HiPlot, which has been archived. We aim to keep the project alive with bug fixes, security updates, and new features.
HiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.
There are several modes to HiPlot:
- As a web-server (if your data is a CSV for instance)
- In a jupyter notebook (to visualize python data), or in Streamlit apps
- In CLI to render standalone HTML
# Render a CSV to interactive HTML (no install needed) uvx hiplot-mm data.csv > output.html # Or start an interactive server uvx --from 'hiplot-mm[server]' hiplot --port 8765
# Core package (HTML export, CLI rendering) pip install hiplot-mm # With Jupyter notebook support pip install hiplot-mm[notebook] # With web server support (hiplot command) pip install hiplot-mm[server] # With Streamlit support pip install hiplot-mm[streamlit] # Everything pip install hiplot-mm[all]
If you have a Jupyter notebook, you can get started with something as simple as:
import hiplot as hip data = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'}, {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'}, {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}] hip.Experiment.from_iterable(data).display()
- Repository: https://github.com/mindthemath/hiplot
- Documentation: https://mindthemath.github.io/hiplot/
- PyPI package: https://pypi.org/project/hiplot-mm/
- Examples: https://github.com/mindthemath/hiplot/tree/main/examples
- Original blog post: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/
To build from source:
# Install frontend dependencies bun install # Install default contributor dependencies (dev/test) uv sync # Build JavaScript bundles bun run build # Build Python package uv build # Or use the all-in-one build script ./build.sh
Common contributor tasks:
# Run Python tests uv run pytest # Build docs (docs deps resolved on-demand) uv run --no-default-groups --group docs sphinx-build -b html docs docs/_build/html
Output directories:
npm-dist/- NPM package artifactsdist/- Python wheel and sdisthiplot/static/built/- JS bundle included in Python package
Run the dev server:
uv run --extra server hiplot --port 8765
@misc{hiplot, author = {Haziza, D. and Rapin, J. and Synnaeve, G.}, title = {{Hiplot, interactive high-dimensionality plots}}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/facebookresearch/hiplot}}, }
Inspired by and based on code from Kai Chang, Mike Bostock and Jason Davies.
External contributors (please add your name when you submit your first pull request):