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Average Ratings 0 Ratings

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ease
features
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support

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Description

Amazon Elastic Inference provides an affordable way to enhance Amazon EC2 and Sagemaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference costs by as much as 75%. It is compatible with models built on TensorFlow, Apache MXNet, PyTorch, and ONNX. The term "inference" refers to the act of generating predictions from a trained model. In the realm of deep learning, inference can represent up to 90% of the total operational expenses, primarily for two reasons. Firstly, GPU instances are generally optimized for model training rather than inference, as training tasks can handle numerous data samples simultaneously, while inference typically involves processing one input at a time in real-time, resulting in minimal GPU usage. Consequently, relying solely on GPU instances for inference can lead to higher costs. Conversely, CPU instances lack the necessary specialization for matrix computations, making them inefficient and often too sluggish for deep learning inference tasks. This necessitates a solution like Elastic Inference, which optimally balances cost and performance in inference scenarios.

Description

tensorViz provides a platform for developers to delve into PyTorch models and JAX/Flax programs through dynamic graph visualizations. In the case of PyTorch, users have the ability to open nested modules, examine tensor shapes recorded during a single forward pass, navigate to the corresponding Python source code, and observe architectural modifications alongside code differences. The site additionally features pre-recorded model graphs for users to analyze without the need for any installation. A free extension for VS Code can be found in both the Visual Studio Marketplace and Open VSX. For local capture, users must have Python installed along with the necessary model dependencies and sample inputs. There are also standalone coding-agent plugins that are currently in preview for Apple Silicon macOS; MCP agents can establish connections with the extension as well. While JAX/Flax graphs utilize CPU tracing to infer shapes, it is important to note that source edits for PyTorch models are not applicable in this context. By offering a seamless experience for both frameworks, tensorViz enhances the development and debugging process for users.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

Amazon EC2 Yes 
Amazon EC2 G4 Instances Yes 
Amazon Web Services (AWS) Yes 
Claude Code No 
Codex CLI No 
Cursor No 
GitHub No 
MXNet Yes 
Model Context Protocol (MCP) No 
PyTorch Yes 
T3 Code No 
TensorFlow Yes 
Visual Studio Code No 

Integrations

Amazon EC2 No 
Amazon EC2 G4 Instances No 
Amazon Web Services (AWS) No 
Claude Code Yes 
Codex CLI Yes 
Cursor Yes 
GitHub Yes 
MXNet No 
Model Context Protocol (MCP) Yes 
PyTorch No 
T3 Code Yes 
TensorFlow No 
Visual Studio Code Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$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 Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Amazon

Founded

2006

Country

United States

Website

aws.amazon.com/machine-learning/elastic-inference/

Vendor Details

Company Name

tensorViz

Website

tensorviz.dev/

Product Features

Infrastructure-as-a-Service (IaaS)

Analytics / Reporting No 
Configuration Management No 
Data Migration No 
Data Security No 
Load Balancing No 
Log Access No 
Network Monitoring No 
Performance Monitoring No 
SLA Monitoring No 

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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