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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 DeepSpeed?

DeepSpeed is an innovative open-source library designed to optimize deep learning workflows specifically for PyTorch. Its main objective is to boost efficiency by reducing the demand for computational resources and memory, while also enabling the effective training of large-scale distributed models through enhanced parallel processing on the hardware available. Utilizing state-of-the-art techniques, DeepSpeed delivers both low latency and high throughput during the training phase of models. This powerful tool is adept at managing deep learning architectures that contain over one hundred billion parameters on modern GPU clusters and can train models with up to 13 billion parameters using a single graphics processing unit. Created by Microsoft, DeepSpeed is intentionally engineered to facilitate distributed training for large models and is built on the robust PyTorch framework, which is well-suited for data parallelism. Furthermore, the library is constantly updated to integrate the latest advancements in deep learning, ensuring that it maintains its position as a leader in AI technology. Future updates are expected to enhance its capabilities even further, making it an essential resource for researchers and developers in the field.

Media

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Media

Integrations Supported

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

Integrations Supported

Axolotl
Cake AI
Comet LLM
Nurix
PyTorch
Python

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

Free
Open source
Free Version

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

tensorViz

Company Website

tensorviz.dev/

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

www.deepspeed.ai/

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

Categories and Features

AI Development

Not specified

AI/ML Model Training

Not specified

Deep Learning

Not specified

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