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About

TorchMetrics is a collection of 90+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. A standardized interface to increase reproducibility. It reduces boilerplate. distributed-training compatible. It has been rigorously tested. Automatic accumulation over batches. Automatic synchronization between multiple devices. You can use TorchMetrics in any PyTorch model, or within PyTorch Lightning to enjoy additional benefits. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. Nearly all functional metrics have a corresponding class-based metric.

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

Anyone seeking a solution providing several PyTorch metrics implementations to create custom metrics

Audience

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

Support

Phone Support Not 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

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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 Not Supported

Training

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

Company Information

TorchMetrics
United States
torchmetrics.readthedocs.io/en/stable/

Company Information

tensorViz
tensorviz.dev/

Alternatives

Alternatives

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Keepsake

Keepsake

Replicate

Categories

Categories

Deep Learning Supported
Machine Learning Supported

Integrations

Claude Code Not Supported
Codex CLI Not Supported
Cursor Not Supported
GitHub Not Supported
Lightning AI Supported
Model Context Protocol (MCP) Not Supported
PyTorch Supported
T3 Code Not Supported
Visual Studio Code Not Supported

Integrations

Claude Code Supported
Codex CLI Supported
Cursor Supported
GitHub Supported
Lightning AI Not Supported
Model Context Protocol (MCP) Supported
PyTorch Not Supported
T3 Code Supported
Visual Studio Code Supported
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