Principle:Interpretml Interpret Visualization Environment Detection
| Metadata | |
|---|---|
| Sources | InterpretML, InterpretML Docs |
| Domains | Visualization, Infrastructure |
| Last Updated | 2026-02-07 12:00 GMT |
Overview
A runtime detection mechanism that identifies the execution environment to select the appropriate visualization rendering strategy.
Description
Visualization Environment Detection automatically determines whether code is running in a Jupyter notebook, Google Colab, Databricks, VS Code, Azure ML, Kaggle, SageMaker, or a local terminal. Based on the detected environment, it selects the optimal visualization provider: InlineProvider for notebook-based environments (renders HTML inline) or DashProvider for local environments (launches a Dash web server). This abstraction means users call the same show() API regardless of environment.
Usage
Use this principle when building visualization systems that must work across diverse execution environments without requiring users to configure rendering manually.
Theoretical Basis
The detection follows a priority-ordered chain of environment checks:
# Abstract detection algorithm
for env_name, check_fn in ordered_checks:
if check_fn():
detected_envs.append(env_name)
if "colab" in detected or "ipython-zmq" in detected:
use InlineProvider # Render HTML in notebook cell
else:
use DashProvider # Launch local web server