TensorBoard

TensorBoard

Tensorflow
+
+

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About

TensorBoard is TensorFlow's comprehensive visualization toolkit designed to facilitate machine learning experimentation. It enables users to track and visualize metrics such as loss and accuracy, visualize the model graph (operations and layers), view histograms of weights, biases, or other tensors as they change over time, project embeddings to a lower-dimensional space, and display images, text, and audio data. Additionally, TensorBoard offers profiling capabilities to optimize TensorFlow programs. These features collectively provide a suite of tools to understand, debug, and optimize TensorFlow programs, enhancing the machine learning workflow. In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics, visualizing the model graph, and projecting embeddings to a lower dimensional space.

About

tensorViz helps developers explore PyTorch models and JAX/Flax programs as interactive graphs. For PyTorch, users can expand nested modules, inspect tensor shapes captured from one forward pass, jump to Python source, and review architecture changes beside code diffs. The website offers recorded model graphs to explore without installation. The free VS Code extension is available from the Visual Studio Marketplace and Open VSX. Local capture requires Python, model dependencies, and example inputs. Standalone coding-agent plugins are an Apple Silicon macOS preview; MCP agents can also connect to the extension. JAX/Flax graphs use CPU tracing with inferred shapes, and PyTorch source edits are unavailable for them.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Developers looking for a solution to optimize their TensorFlow programs

Audience

Machine learning engineers, PyTorch developers, researchers and students exploring model architecture, tensor shapes and architecture changes.

Support

Phone Support Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

$0
The VS Code extension is free to install. Core graph exploration and source-edit forms work without an agent or provider API key; connecting a coding agent is optional.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Tensorflow
United States
www.tensorflow.org/tensorboard

Company Information

tensorViz
tensorviz.dev/

Alternatives

Visdom

Visdom

Meta

Alternatives

AWS Neuron

AWS Neuron

Amazon Web Services
TF-Agents

TF-Agents

Tensorflow
Keepsake

Keepsake

Replicate

Categories

Categories

Deep Learning Supported
Machine Learning Supported

Integrations

GitHub Supported
Claude Code Not Supported
Codex CLI Not Supported
Cursor Not Supported
Dataoorts GPU Cloud Supported
Google Colab Supported
LLaMA-Factory Supported
Ludwig Supported
Model Context Protocol (MCP) Not Supported
T3 Code Not Supported
TensorFlow Supported
Visual Studio Code Not Supported

Integrations

GitHub Supported
Claude Code Supported
Codex CLI Supported
Cursor Supported
Dataoorts GPU Cloud Not Supported
Google Colab Not Supported
LLaMA-Factory Not Supported
Ludwig Not Supported
Model Context Protocol (MCP) Supported
T3 Code Supported
TensorFlow Not Supported
Visual Studio Code Supported
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