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Description
Amazon EC2 Inf1 instances are specifically designed to provide efficient, high-performance machine learning inference at a competitive cost. They offer an impressive throughput that is up to 2.3 times greater and a cost that is up to 70% lower per inference compared to other EC2 offerings. Equipped with up to 16 AWS Inferentia chips—custom ML inference accelerators developed by AWS—these instances also incorporate 2nd generation Intel Xeon Scalable processors and boast networking bandwidth of up to 100 Gbps, making them suitable for large-scale machine learning applications. Inf1 instances are particularly well-suited for a variety of applications, including search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers have the advantage of deploying their ML models on Inf1 instances through the AWS Neuron SDK, which is compatible with widely-used ML frameworks such as TensorFlow, PyTorch, and Apache MXNet, enabling a smooth transition with minimal adjustments to existing code. This makes Inf1 instances not only powerful but also user-friendly for developers looking to optimize their machine learning workloads. The combination of advanced hardware and software support makes them a compelling choice for enterprises aiming to enhance their AI capabilities.
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
No images available
Integrations
AWS Inferentia
Yes
AWS Nitro System
Yes
AWS Trainium
Yes
Amazon EC2
Yes
Amazon EC2 Capacity Blocks for ML
Yes
Amazon EC2 G5 Instances
Yes
Amazon EC2 P4 Instances
Yes
Amazon EC2 P5 Instances
Yes
Amazon EC2 UltraClusters
Yes
Amazon EKS
Yes
Integrations
AWS Inferentia
No
AWS Nitro System
No
AWS Trainium
No
Amazon EC2
No
Amazon EC2 Capacity Blocks for ML
No
Amazon EC2 G5 Instances
No
Amazon EC2 P4 Instances
No
Amazon EC2 P5 Instances
No
Amazon EC2 UltraClusters
No
Amazon EKS
No
Pricing Details
$0.228 per hour
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
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/ec2/instance-types/inf1/
Vendor Details
Company Name
tensorViz
Website
tensorviz.dev/
Product Features
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
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
