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Lucid

Lucid is an open-source collection of research infrastructure, Jupyter notebooks and utilities for visualizing neural networks and studying interpretability, including work with TensorFlow models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Lucid — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Lucid for research-oriented exploration of neural network behavior, particularly when notebook examples and visualization utilities fit the investigation.

Who it is for

  • Researchers exploring neural network representations and interpretability.
  • Developers who want to visualize their own models using the project’s notebooks and model-import guidance.

Documented uses

  • Explore feature visualizations, including neuron diversity and interactions.
  • Study interpretability building blocks such as activation grids, semantic dictionaries and attribution.
  • Use notebook workflows to investigate a model’s internal representations.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. Hosted notebooks run in Colaboratory; local notebook use requires cloning the repository and running a Jupyter environment.

  • The project is research code, not production code, and provides no guarantee that it will fit a particular use case.
  • The maintainers are volunteers and say they cannot provide significant technical support.
  • The README provides a TensorFlow 1.x Colab notebook magic for use before importing TensorFlow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Explore Lucid visualization notebooks in Colaboratory

Open and execute a Lucid notebook in the browser to explore neural-network visualizations.

Before you start

  • A web browser with access to Google Colaboratory.

Steps

  1. Choose a tutorial or visualization notebook linked from the Lucid README.

  2. Open its Colaboratory link in the browser; the README describes these notebooks as requiring no setup.

  3. If using Colab's default TensorFlow 2 environment, add %tensorflow_version 1.x in a cell before importing TensorFlow.

  4. Run the notebook cells to explore the notebook's visualization workflow.

Expected outcome

A browser-based notebook workflow for exploring neural-network visualizations.

Guide limitations

  • Lucid does not currently support TensorFlow 2; the README gives the TensorFlow 1.x Colab magic as a workaround.
  • Lucid is research code, not production code, and its maintainers provide no guarantee for a particular use case or significant technical support.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
tensorflow/lucid · Official homepage title · retained in its source language
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