tensorboard

TensorBoard lets you watch Tensors Flow

tensorboard Permissive license Apache 2.0 Active ★ 7,204 v2.21.0 released 2026-06-29

Install

tensorboard on PyPI

pip

pip install tensorboard

uv

uv add tensorboard

poetry

poetry add tensorboard

Package facts

License	Apache 2.0 (permissive)
Python support	supports the current Python release (>=3.9)
Install friction	low — pure-Python wheel
Runtime dependencies	10 — absl-py, grpcio, markdown, numpy, packaging, pillow, protobuf, setuptools, tensorboard-data-server, werkzeug
Maintenance	actively maintained — 45 days since the last release
Last repo commit	2026-08-07
First released	2017-02-25
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: tensorboard-2.21.0-py3-none-any.whl

Keywords: tensorflow, tensorboard, tensor, machine, learning, visualizer

Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules

About tensorboard

from the package's own PyPI description — quoted content, verbatim

TensorBoard GitHub Actions CI (image) GitHub Actions Nightly CI (image) PyPI (image)

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.

This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: [TensorBoard: Getting Started][]. Documentation on how to use TensorBoard to work with images, graphs, hyper parameters, and more are linked from there, along with tutorial walk-throughs in Colab.

TensorBoard is designed to run entirely offline, without requiring any access to the Internet. For instance, this may be on your local machine, behind a corporate firewall, or in a...

Read as markdown · JSON record · Source repository · Homepage

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

TensorBoard is a web application suite for visualizing and inspecting TensorFlow training runs, graphs, and metrics. It reads event files from a log directory and serves interactive dashboards for scalar metrics, histograms, images, and model graphs.

Low friction installation with a pure-Python wheel and 10 well-established runtime dependencies. Active maintenance with a recent release 45 days ago and 7204 GitHub stars indicate solid project health.

Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.

Usage

pip install tensorboard==2.21.0

import tensorboard

# Run from command line:
# tensorboard --logdir /path/to/logs
# Then open http://localhost:6006

Requires Python >=3.9 and TensorFlow event files written to a log directory by tf.summary.FileWriter or equivalent.

Verdict: TensorBoard 2.21.0 is a mature, actively maintained visualization tool with no known vulnerabilities and permissive licensing. Its low install friction and broad dependency ecosystem make it a reliable choice for TensorFlow experiment monitoring. Suitable for production use in research and ML engineering workflows.

Needs verification

Whether tensorboard-data-server (a runtime dependency) requires system libraries or network access beyond what the fact sheet indicates.
Performance characteristics when handling very large log directories or high-frequency event streams.

tensorflow training visualizationmachine learning experiment trackingneural network metrics dashboardtensorboard web interfacetraining run comparison tooldeep learning monitoringmodel performance visualization