---
id: dvc-render
version: "1.0.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# dvc-render — Dvc Render

License: permissive · Maintenance: active · Downloads: 1.9M/mo

## What it is and what it does

dvc-render is a library for converting data in DVC plots format into Vega visualizations and generating HTML or Markdown reports. It provides renderers that take plot data and properties to produce Vega-compatible output, report builders to combine multiple plots into a single document, and support for both custom and pre-defined Vega templates. The library has no runtime dependencies for basic Vega rendering, making it lightweight to install. It is used internally by DVC, DVCLive, and Studio, and targets Python 3.9 and later.

The package is designed for developers and data teams who need to programmatically generate interactive plots and reports from experiment or analysis data. Its main use is as a rendering backend for visualization pipelines, particularly those already using DVC's ecosystem. The Beta development status and active maintenance indicate it is stable enough for production use but may see API refinements.

Use it for:

- Generate confusion matrix plots and other Vega templates from experiment results in DVC pipelines
- Build HTML reports combining multiple plots for experiment comparison and sharing
- Render Markdown reports with embedded Vega plots for documentation and notebooks
- Convert DVC plot format data into Vega JSON for use in custom web dashboards
- Automate visualization output as part of machine learning experiment tracking workflows

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Renders data in DVC plots format into Vega visualizations and generates HTML or Markdown reports containing multiple plots.

Yes. dvc-render is worth installing if you need to render plots from DVC or generate Vega-based reports programmatically. It has zero runtime dependencies for basic use, active maintenance, no known vulnerabilities, and a permissive Apache 2.0 license. Install friction is minimal. The only caveat is Beta status, which may mean occasional API changes, but the library is actively used in production by DVC's own ecosystem.

## Install

pip install dvc-render
uv add dvc-render
poetry add dvc-render

## Installing dvc-render

Before you install: Low install friction with no runtime dependencies for basic Vega rendering. Actively maintained with recent commits and Beta-stage stability across Python 3.9–3.12.

License in practice: Apache 2.0 permissive license allows free use, modification, and distribution with minimal restrictions, suitable for both open and closed projects.

Quickstart:

pip install dvc-render

from dvc_render import VegaRenderer
properties = {"template": "confusion", "x": "predicted", "y": "actual"}
datapoints = [{"predicted": "B", "actual": "A"}, {"predicted": "A", "actual": "A"}]
renderer = VegaRenderer(datapoints, "foo", **properties)
plot_content = renderer.get_filled_template()

Verify before relying:

- Whether optional dependencies (referenced in setup.cfg) are required for features beyond basic Vega rendering
- Performance characteristics when rendering large datasets or many plots in a single report

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.9M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

vega plot rendering, dvc plots to html, data visualization reports, plot template rendering, vega report generation, dvc visualization library, plot format conversion, visualization, dvc-ecosystem, vega

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