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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 NVIDIA PhysicsNeMo?

NVIDIA's PhysicsNeMo is an open-source deep-learning framework built in Python that facilitates the design, training, fine-tuning, and inference of AI models that marry physical laws with data, thereby improving simulations, creating precise surrogate models, and enabling near-real-time predictions across a variety of domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate science, and digital twin technologies. It boasts robust GPU-accelerated performance and offers Python APIs based on the PyTorch framework, all distributed under the Apache 2.0 license, featuring a variety of pre-designed model architectures, including physics-informed neural networks, neural operators, graph neural networks, and generative AI methods, allowing developers to effectively harness the causal relationships present in physics along with empirical data for superior engineering modeling. Furthermore, PhysicsNeMo includes extensive training pipelines that cover all aspects from geometry ingestion to the implementation of differential equations, in addition to providing reference application recipes that assist users in rapidly kickstarting their development processes. This unique integration of powerful features positions PhysicsNeMo as a vital resource for engineers and researchers aiming to push the boundaries of physics-based AI applications. Overall, its capabilities make it a crucial asset for anyone looking to innovate in fields that rely on the intersection of artificial intelligence and physical modeling.

Media

No images available

Media

Integrations Supported

Claude Code
Codex CLI
Cursor
GitHub
Model Context Protocol (MCP)
T3 Code
Visual Studio Code

Integrations Supported

PyTorch
Python

API Availability

Has API

API Availability

Has API

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

Free
Free Version

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training

Company Facts

Organization Name

tensorViz

Company Website

tensorviz.dev/

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/physicsnemo

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

Categories and Features

AI Science

Not specified

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