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What is tensorViz?

tensorViz offers a unique platform that allows developers to explore PyTorch models and JAX/Flax programs through interactive graph visualizations. For PyTorch, users can delve into nested modules, inspect tensor shapes captured during a single forward pass, navigate to the related Python source code, and witness architectural changes along with code variations. The platform also includes pre-recorded model graphs that can be analyzed without any installation requirements. A free extension compatible with VS Code is available on both the Visual Studio Marketplace and Open VSX. To enable local capture, users need to have Python installed along with the relevant model dependencies and sample inputs. Additionally, there are standalone coding-agent plugins in preview specifically designed for Apple Silicon macOS, allowing MCP agents to connect seamlessly with the extension. While JAX/Flax graphs rely on CPU tracing to determine shapes, it's crucial to understand that source modifications for PyTorch models do not apply in this scenario. By providing a cohesive experience for both frameworks, tensorViz significantly improves the development and debugging workflow for its users, ultimately leading to more efficient coding practices. This dual framework support ensures that developers have the tools they need to effectively manage their projects and enhance their productivity.

What is PyTorch?

Seamlessly transition between eager and graph modes with TorchScript, while expediting your production journey using TorchServe. The torch-distributed backend supports scalable distributed training, boosting performance optimization in both research and production contexts. A diverse array of tools and libraries enhances the PyTorch ecosystem, facilitating development across various domains, including computer vision and natural language processing. Furthermore, PyTorch's compatibility with major cloud platforms streamlines the development workflow and allows for effortless scaling. Users can easily select their preferences and run the installation command with minimal hassle. The stable version represents the latest thoroughly tested and approved iteration of PyTorch, generally suitable for a wide audience. For those desiring the latest features, a preview is available, showcasing the newest nightly builds of version 1.10, though these may lack full testing and support. It's important to ensure that all prerequisites are met, including having numpy installed, depending on your chosen package manager. Anaconda is strongly suggested as the preferred package manager, as it proficiently installs all required dependencies, guaranteeing a seamless installation experience for users. This all-encompassing strategy not only boosts productivity but also lays a solid groundwork for development, ultimately leading to more successful projects. Additionally, leveraging community support and documentation can further enhance your experience with PyTorch.

Media

No images available

Media

Integrations Supported

Visual Studio Code

Integrations Supported

AWS Elastic Fabric Adapter (EFA)
Amazon EC2 Inf1 Instances
Amazon EC2 UltraClusters
Amazon SageMaker Model Training
Collimator
Comet
Dataoorts GPU Cloud
GPUonCLOUD
Gemma
Guild AI
Huawei Cloud ModelArts
Keepsake
Lightning AI
MLReef
Skyportal
Superwise
Voyager SDK
spaCy

API Availability

Has API

API Availability

Pricing Information

$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

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

Android
iPhone
iPad
Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

tensorViz

Company Website

tensorviz.dev/

Company Facts

Organization Name

PyTorch

Date Founded

2016

Company Website

pytorch.org

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

Categories and Features

AI Development

Not specified

AI/ML Model Training

Not specified

Machine Learning

Not specified

Neural Network

Not specified

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