Related Products
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About
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
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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.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
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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
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Audience
Developers looking for a scientific computing framework for their neural networks and energy-based models
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Audience
Machine learning engineers, PyTorch developers, researchers and students exploring model architecture, tensor shapes and architecture changes.
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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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
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationTorch
torch.ch/
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Company InformationtensorViz
tensorviz.dev/
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Claude Code
Not Supported
Codex CLI
Not Supported
Cursor
Not Supported
GitHub
Not Supported
Hetman Internet Spy
Supported
LeaderGPU
Supported
Model Context Protocol (MCP)
Not Supported
T3 Code
Not Supported
Visual Studio Code
Not Supported
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Integrations
Claude Code
Supported
Codex CLI
Supported
Cursor
Supported
GitHub
Supported
Hetman Internet Spy
Not Supported
LeaderGPU
Not Supported
Model Context Protocol (MCP)
Supported
T3 Code
Supported
Visual Studio Code
Supported
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