Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Amazon EC2 Trn2 instances, equipped with AWS Trainium2 chips, are specifically designed to deliver exceptional performance in the training of generative AI models, such as large language and diffusion models. Users can experience cost savings of up to 50% in training expenses compared to other Amazon EC2 instances. These Trn2 instances can accommodate as many as 16 Trainium2 accelerators, boasting an impressive compute power of up to 3 petaflops using FP16/BF16 and 512 GB of high-bandwidth memory. For enhanced data and model parallelism, they are built with NeuronLink, a high-speed, nonblocking interconnect, and offer a substantial network bandwidth of up to 1600 Gbps via the second-generation Elastic Fabric Adapter (EFAv2). Trn2 instances are part of EC2 UltraClusters, which allow for scaling up to 30,000 interconnected Trainium2 chips within a nonblocking petabit-scale network, achieving a remarkable 6 exaflops of compute capability. Additionally, the AWS Neuron SDK provides seamless integration with widely used machine learning frameworks, including PyTorch and TensorFlow, making these instances a powerful choice for developers and researchers alike. This combination of cutting-edge technology and cost efficiency positions Trn2 instances as a leading option in the realm of high-performance deep learning.
Description
Declarative machine learning systems offer an ideal combination of flexibility and ease of use, facilitating the rapid implementation of cutting-edge models. Users concentrate on defining the “what” while the system autonomously determines the “how.” Though you can start with intelligent defaults, you have the freedom to adjust parameters extensively, even diving into code if necessary. Our team has been at the forefront of developing declarative machine learning systems in the industry, exemplified by Ludwig at Uber and Overton at Apple. Enjoy a selection of prebuilt data connectors designed for seamless compatibility with your databases, data warehouses, lakehouses, and object storage solutions. This approach allows you to train advanced deep learning models without the hassle of infrastructure management. Automated Machine Learning achieves a perfect equilibrium between flexibility and control, all while maintaining a declarative structure. By adopting this declarative method, you can finally train and deploy models at the speed you desire, enhancing productivity and innovation in your projects. The ease of use encourages experimentation, making it easier to refine models based on your specific needs.
API Access
Has API
No
API Access
Has API
Yes
Integrations
AWS Batch
Yes
AWS Neuron
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 Inf1 Instances
Yes
Amazon EC2 P5 Instances
Yes
Amazon EC2 Trn1 Instances
Yes
Integrations
AWS Batch
No
AWS Neuron
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 Inf1 Instances
No
Amazon EC2 P5 Instances
No
Amazon EC2 Trn1 Instances
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Pay-as-you-go pricing
Free Trial
Yes
Free Version
No
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
No
Mac
No
Linux
No
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)
Yes
In Person
No
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/ec2/instance-types/trn2/
Vendor Details
Company Name
Predibase
Country
United States
Website
predibase.com
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
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